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arXiv 2023年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We study the impact of content moderation policies in online communities. In our theoretical model, a platform chooses a content moderation policy and individuals choose whether or not to participate in the community according to the fraction of user content that aligns with their preferences. The effects of content moderation, at first blush, might seem obvious: it restricts speech on a platform. However, when user participation decisions are taken into account, its effects can be more subtle - and counter-intuitive. For example, our model can straightforwardly demonstrate how moderation policies may increase participation and diversify content available on the platform. In our analysis, we explore a rich set of interconnected phenomena related to content moderation in online communities. We first characterize the effectiveness of a natural class of moderation policies for creating and sustaining communities. Building on this, we explore how resource-limited or ideological platforms might set policies, how communities are affected by differing levels of personalization, and competition between platforms. Our model provides a vocabulary and mathematically tractable framework for analyzing platform decisions about content moderation. Copyright © 2023, The Authors. All rights reserved.

关键词： Online systems

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Physics-Informed Machine Learning for the Inverse Design of Wave Scattering Clusters

arXiv

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arXiv 2024年

作者： Tempelman, Joshua R. Weidemann, Tobias Flynn, Eric B. Matlack, Kathryn H. Vakakis, Alexander F. Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Institute of Aircraft Propulsion Systems University of Stuttgart Germany Space Remote Sensing and Data Science Los Alamos National Laboratory Los AlamosNM United States

Clusters of wave-scattering oscillators offer the ability to passively control wave energy in elastic continua. However, designing such clusters to achieve a desired wave energy pattern is a highly nontrivial task. While the forward scattering problem may be readily analyzed, the inverse problem is very challenging as it is ill-posed, high-dimensional, and known to admit non-unique solutions. Therefore, the inverse design of multiple scattering fields and remote sensing of scattering elements remains a topic of great interest. Motivated by recent advances in physics-informed machine learning, we develop a deep neural network that is capable of predicting the locations of scatterers by evaluating the patterns of a target wavefield. We present a modeling and training formulation to optimize the multi-functional nature of our network in the context of inverse design, remote sensing, and wavefield engineering. Namely, we develop a multi-stage training routine with customized physics-based loss functions to optimize models to detect the locations of scatterers and predict cluster configurations that are physically consistent with the target wavefield. We demonstrate the efficacy of our model as a remote sensing and inverse design tool for three scattering problem types, and we subsequently applicability for designing clusters that direct waves along preferred paths or localize wave energy. Hence, we present an effective model for multiple scattering inverse design which may have diverse applications such as wavefield imaging or passive wave energy control. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Leveraging Deep Learning for COVID-19 Detection: A Comparative Study Using ResNet Models on X-Ray Images

Leveraging Deep Learning for COVID-19 Detection: A Comparati...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Disha Tuteja Anurag Sinha Deepak Chandra Uprety Dyuti Banerjee Ayushi Dewan Chetna Sharma Department of ECE College Guru Tegh Bahadur Institute of Technology Delhi India Department of Computer Science IGNOU New Delhi India Department of Computer Science and Engineering (CS) School of Computer Science in Emerging Technologies Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence & Data Science (AI&DS) Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh India Rawal Institutions Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The COVID-19 pandemic has rapidly spread across the globe, infecting millions of people, with no signs of slowing down. This continuous surge in cases presents a major challenge for healthcare systems worldwide. To combat the exponential rise in infections, medical researchers have primarily relied on RT-PCR testing to distinguish between COVID-19 positive and negative cases. However, this approach faces significant hurdles, particularly in low-income countries where these tests are often too expensive and inaccessible in rural areas. Another critical challenge is the shortage of skilled healthcare professionals, especially doctors and radiologists, whose expertise is in high demand for diagnosing and treating COVID-19 patients. As a result, healthcare resources have been stretched to their limits. To address these issues, innovative solutions are essential, and artificial intelligence (AI) and machine learning (ML) offer promising possibilities. In medical diagnostics, AI, ML, and deep learning (DL) have demonstrated remarkable accuracy. This study explores the application of DL models, specifically ResNet-34 and ResNet-50, for COVID-19 detection using X-ray images. The results indicate an impressive accuracy of 96.4% with ResNet-34. To comprehensively evaluate the performance of these models, the study employs various quantitative metrics, including Matthews Correlation Coefficient (MCC), F1 Score, Precision, and Recall. By contributing to both the effective detection of COVID-19 and addressing the shortage of healthcare professionals, this research provides a hopeful step forward in the ongoing fight against the pandemic.

关键词： COVID-19 Deep learning Measurement Technological innovation Accuracy Pandemics Medical services Public healthcare X-ray imaging Testing

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Machine Learning-based Alzheimer's Disease Prediction using Personalized Methods 3

Machine Learning-based Alzheimer's Disease Prediction using ...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Lavanya, M. Chandan, Radha Raman Rajasekar, P. Rham, P. Rethik Deivakani, M. Mahesh Kumar, A.S. Master of Computer Applications Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Shambhunath Institute of Engineering and Technology Department of Computer Science & Engineering Uttar PradeshJhalwa Prayagraj211015 India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Saveetha Medical College Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Pes College of Engineering Department of Electronics and Communication Engineering Karnataka Mandya571401 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Alzheimer's disease (AD) is a neurological illness which occurs over time. Early detection reduces the likelihood of disease worsening condition. Unfortunately, the present research focuses on defining disease processes in their existing stages rather than forecasting disease progression. The Generative Personalized embedding model (GPE) is a multi-layer perceptron that is proficient of relating prior knowledge to the prediction process by realizing the patient's behavioural data and helps in forecasting the disease progression. This work suggest a prediction process based on GPE. As a result, a GPA network is built with the connectivity layers and activated layers to observe the possible correlation among information and next generation of Alzheimer's disease. Experiments reveal that the outcomes significantly outperformed the majority of previous designs. © 2022 IEEE.

关键词： Machine learning

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Advancements in Artificial Intelligence for Better Image Analysis, Recognition, and Interpretation in the Field of Image Processing

Advancements in Artificial Intelligence for Better Image Ana...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： V. Saravanan A. Adaikkammai G. Sasikala Iyswariya A A. Babisha M. Latha Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science and Business Systems R.M.D. Engineering College Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Artificial Intelligence and Data Science Panimalar Engineering College Department of IT SRM Institute of Science and Technology

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Industries Image recognition Image analysis Transformers Satellite navigation systems Generative adversarial networks Satellite images Convolutional neural networks Security Artificial intelligence

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Hatespeechbert: Retraining Bert for Automatic Hate Speech Detection

SSRN

引用

SSRN 2022年

作者： Ishaq, Afia Zafar, Adeel Malik, Khalid Mahmood Department of Cyber Security and Data Science Riphah Institute of Systems Engineering Riphah International University Islamabad44210 Pakistan Department of Computer Science and Engineering Oakland University United States

In recent years, the evolution of the internet has increased communication via social media. In today’s world, people can communicate through it even if they are sitting poles apart. No doubt it helps people to be connected with their loved ones but this has increased the expression of hatred over social media platforms which can lead to mental sickness. To reduce the effect of social media hatred various models have been developed to identify negative or hate speech. As there was no unbiased domain-specific BERT that can detect hate speech automatically. This research aims to investigate the problem of how to make a domain-specific BERT for detecting hate speech posted online through a model and, its customization. The initial objective was to create a thorough review of the datasets focusing on hate speech. Therefore, the purpose of this research is to investigate if a big language model that has been pre-trained on domain-specific (Hate Speech) text corpora performs better than a model that has just been pre-trained on broad domain text when it is tested to in-domain downstream tasks. To this end, HateSpeechBERT (HSBERT), a domain-specific language representation model based on BERT and pre-trained on largescale Hate Speech datasets is introduced. Through several extrinsic and intrinsic evaluations, the performance of our novel model against the general-domain BERT is compared and it is confirmed that HateSpeechBERT gives state-of-the-art results over other models. In the end, the domain-specific HateSpeechBERT model is pushed to *** (The AI community building the future) platform, which will be freely available for the public to use in their implementation. This would be considered a great contribution to the field of data science. © 2022, The Authors. All rights reserved.

关键词： Large dataset

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Enhancing Cybersecurity with AI: Anomaly Detection in Network Traffic

Enhancing Cybersecurity with AI: Anomaly Detection in Networ...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： V. Kavitha S. Malathi Vishnu Priya P M Saranya Ekambaram B. Charith M. Deivakani Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Katankullathur Tamil Nadu India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer-Science Kristu Jayanti College Autonomous Bengaluru Karnataka India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Management & Commerce International Institute of Business Studies Bengaluru Karnataka India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Traditional symbol processing styles have the limitation to do so in the various and dynamic nature of ultramodern data transfers. The reason is that communication systems become more and more serious. This offers a potentially useful answer in that much of the functionality of machine literacy is afforded by data driven adaption and can offer the eventuality to ameliorate signal processing performance. The aim of this composition is to carry out a thorough investigation of several classes of machine literacy practices and their function in central fields of study like modulation recognition, noise reduction, channel estimation, and signal discovery. The study shows how machine literacy ways can provide much increase in the effectiveness, delicateness and robustness of signal processing tasks in communication systems. By doing detailed analysis and comparison you witness through this is fulfilled. Machine literacy will allow the unborn communication systems to adapt to advanced situations of robotization and smartness, which makes the progress possible in the similar fields, for instance, the Internet of Things (IoT), wireless deliverances, and other.

关键词： Wireless communication Symbols Telecommunication traffic Robustness Real-time systems Internet of Things Artificial intelligence Computer security Anomaly detection Robots

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An Intelligent Voice-Recognition Wheelchair System for Disabled Persons

An Intelligent Voice-Recognition Wheelchair System for Disab...

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Sustainable Computing and Smart systems (ICSCSS), International Conference on

作者： A.V. Kalpana V. Venkataramanan G. Charulatha G Geetha Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering D.J Sanghvi College of Engineering Mumbai Maharashtra India Department of Electronics and Communication Engineering Peri Institute of Technology Chennai Tamil Nadu India Department of Computer Science Engineering B V Raju Institute of Technology Hyderabad Telangana India

This study illustrates the creation of an intelligent voice-recognition wheelchair for disabled persons who cannot manually man-oeuvre their wheelchairs. Using voice recognition, the patient operates the wheelchair, and a GPS device included in the wheelchair can track the patient’s position and communicate the data to a Smartphone. To carry out the people’s orders, the voice module V3 records and can recognize the patient’s voice. This module converts the voice commands into letters, subsequently transmitted to the Wi-Fi module to operate the wheelchair. The Wi-Fi module sends commands to the motor drive for the wheels. In addition, three levels of motor speed adjustment are available: low, medium, and high. This device also utilizes an Infrared sensor to automatically identify obstacles, allowing the patient’s family members to know exactly where they are. Disabled People can be used this device, which simultaneously provides a voice-controlled wheelchair, obstacle detection, motor speed control, and patient GPS tracking via mobile phone; it will be a successful solution for disabled people across the world.

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When is the estimated propensity score better? High-dimensional analysis and bias correction

arXiv

引用

arXiv 2023年

作者： Su, Fangzhou Mou, Wenlong Ding, Peng Wainwright, Martin J. Department of Electrical Engineering and Computer Sciences United States Department of Statistics UC Berkeley United States Department of Mathematics Lab for Information and Decision Systems and Statistics and Data Science Center Massachusetts Institute of Technology United States

Anecdotally, using an estimated propensity score is superior to the true propensity score in estimating the average treatment effect based on observational data. However, this claim comes with several qualifications: it holds only if propensity score model is correctly specified and the number of covariates d is small relative to the sample size n. We revisit this phenomenon by studying the inverse propensity score weighting (IPW) estimator based on a logistic model with a diverging number of covariates. We first show that the IPW estimator based on the estimated propensity score is consistent and asymptotically normal with smaller variance than the oracle IPW estimator (using the true propensity score) if and only if n & d2. We then propose a debiased IPW estimator that achieves the same guarantees in the regime n & d3/2. Our proofs rely on a novel non-asymptotic decomposition of the IPW error along with careful control of the higher order terms. Copyright © 2023, The Authors. All rights reserved.

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Automatic nonlinear MPC approximation with closed-loop guarantees

arXiv

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arXiv 2023年

作者： Tokmak, Abdullah Fiedler, Christian Zeilinger, Melanie N. Trimpe, Sebastian Köhler, Johannes Department of Electrical Engineering and Automation Aalto University Espoo02150 Finland Institute for Data Science in Mechanical Engineering RWTH Aachen University Aachen52068 Germany Institute for Dynamic Systems and Control ETH Zurich ZurichCH-8092 Switzerland

Safety guarantees are vital in many control applications, such as robotics. Model predictive control (MPC) provides a constructive framework for controlling safety-critical systems, but is limited by its computational complexity. We address this problem by presenting a novel algorithm that automatically computes an explicit approximation to nonlinear MPC schemes while retaining closed-loop guarantees. Specifically, the problem can be reduced to a function approximation problem, which we then tackle by proposing ALKIA-X, the Adaptive and Localized Kernel Interpolation Algorithm with eXtrapolated reproducing kernel Hilbert space norm. ALKIA-X is a non-iterative algorithm that ensures numerically well-conditioned computations, a fast-to-evaluate approximating function, and the guaranteed satisfaction of any desired bound on the approximation error. Hence, ALKIA-X automatically computes an explicit function that approximates the MPC, yielding a controller suitable for safety-critical systems and high sampling rates. We apply ALKIA-X to approximate two nonlinear MPC schemes, demonstrating reduced computational demand and applicability to realistic problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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