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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wu, Zihui Sun, Yu Chen, Yifan Zhang, Bingliang Yue, Yisong Bouman, Katherine L. Department of Computing and Mathematical Sciences Caltech United States Department of Electrical Engineering Caltech United States Department of Astronomy Caltech United States Department of Electrical and Computer Engineering Johns Hopkins University United States Courant Institute of Mathematical Sciences New York University United States

Diffusion models (DMs) have recently shown outstanding capabilities in modeling complex image distributions, making them expressive image priors for solving Bayesian inverse problems. However, most existing DM-based methods rely on approximations in the generative process to be generic to different inverse problems, leading to inaccurate sample distributions that deviate from the target posterior defined within the Bayesian framework. To harness the generative power of DMs while avoiding such approximations, we propose a Markov chain Monte Carlo algorithm that performs posterior sampling for general inverse problems by reducing it to sampling the posterior of a Gaussian denoising problem. Crucially, we leverage a general DM formulation as a unified interface that allows for rigorously solving the denoising problem with a range of state-of-the-art DMs. We demonstrate the effectiveness of the proposed method on six inverse problems (three linear and three nonlinear), including a real-world black hole imaging problem. Experimental results indicate that our proposed method offers more accurate reconstructions and posterior estimation compared to existing DM-based imaging inverse methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Representation Learning based Time Synchronization Attack Detection for Synchrophasors 14

Representation Learning based Time Synchronization Attack De...

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14th IEEE International Conference on Communications, Control, and computing Technologies for Smart Grids, SmartGridComm 2023

作者： Iqbal, Asif Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Nebraska-Lincoln School of Computing Nebraska United States

ISBN: (纸本)9781665455541

Phasor measurement units (PMUs) play a crucial role in ensuring the reliable operation of modern power grid monitoring systems, such as wide-area measurement systems (WAMS). These systems heavily rely on accurate time synchronization, typically achieved through the Global Positioning System (GPS). However, the open nature of civilian GPS signals exposes PMUs to potential time synchronization attacks (TSA), where malicious actors manipulate PMU time stamps by transmitting deceptive GPS signals in close proximity to the PMUs. In this paper, we propose a framework for TSA detection using machine learning (ML) models at the control center of a WAMS. The feature set used includes power and correlation distortion measurements, which can be extracted in real-time using any generic GPS receiver. We propose a spoof detector based on representation learning, which offers advantages over supervised ML methods by not requiring exhaustive coverage of all possible attack scenarios during training. Instead, it can be trained using only authentic GPS features. Experimental results highlight that our proposed method performs comparably or even surpasses the performance of the compared ML algorithms. This improvement is particularly evident when considering the TEXBAT subtle attack scenario DS-7, where the ML methods struggle to detect the presence of spoofers. In contrast, our proposed method achieves a detection probability of 98% at a false alarm probability of 2.5%. © 2023 IEEE.

关键词： Global positioning system

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Low-cost contact-free screen control based on machine vision 4

Low-cost contact-free screen control based on machine vision

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4th Optics Frontier Conference, OFS 2024

作者： Lin, Yangbing Wang, Teng Yan, Wei Peng, Xiao Zhang, Xinhai Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China College of Physics and Optoelectronic Engineering Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen University Shenzhen518060 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9781510682269

Contact-free manipulation technology represents a novel approach that offers a fresh possibility for human-machine interaction, aiming to meet the evolving demands of this interface. In this paper, we introduce a contact-free planar display control technique based on imaging. In our research, we employ cameras as sensors and integrate infrared lasers with image processing techniques to detect and localize input signals. By establishing a coordinate mapping relationship between the operational plane and the display plane, we achieved contact-free control over the planar display. Our paper introduces two methods for establishing this coordinate mapping. The first involves constructing mapping functions between the rectified and transformed camera imagery and the display plane, thereby creating a point-to-point correspondence. The second method entails dividing both the camera imagery and the display plane into segments, establishing a one-to-one relationship between regions. We analyze the strengths and limitations of these two approaches. While the former boasts higher theoretical precision, it is more susceptible to equipment factors and exhibits slower response speeds. Conversely, the latter, despite its lower theoretical precision, offers faster response times. Through the setup of an experimental platform, we demonstrate the feasibility of our proposed. © 2024 SPIE.

关键词： Machine vision

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Empirical Study on Mispronunciation Detection for Tajweed Rules during Quran Recitation 6

Empirical Study on Mispronunciation Detection for Tajweed Ru...

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6th International Conference on computing and Informatics, ICCI 2024

作者： Alsahafí, Yousef S. Asad, Muhammad Department of Information technology Computing and Information technology Khulis College University of Jeddah Jeddah Saudi Arabia Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad Pakistan

ISBN: (纸本)9798350373875

This paper presents an extensive empirical study aiming to identify the optimal combination of feature extraction techniques and machine learning algorithms, including deep learning, for automated mispronunciation detection during Quran recitation. Three feature extraction methods-Mel-Frequency Cepstral Coefficients (MFCC), Spectrogram, and Kaldi pitch - are systematically evaluated in conjunction with a diverse set of classifiers, including Gradient Boosting (GB), Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. The study leverages the QDAT dataset, comprising 1500 audio samples from 150 readers, with metadata specifying age, gender, and three distinct rules for mispronunciation detection. Experimental results demonstrate that the integration of MFCC with deep learning classifiers consistently achieves the highest performance across multiple evaluation metrics. Model robustness is validated using receiver operator characteristic curves (ROC) and accuracy metrics. © 2024 IEEE.

关键词： Speech recognition

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Developing Engagement in the Learning Management System Supported by Learning Analytics

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computer Systems Science & engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

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Electro-Optic Modulator Stabilizer for Consistent Generation of Short Tunable Optical Pulses

Electro-Optic Modulator Stabilizer for Consistent Generation...

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2023 Photonics North, PN 2023

作者： Li, Michael Anderson, Paul Hawes, Kassandra Al Maruf, Rubayet Bajcsy, Michal University of Waterloo Department of Electrical and Computer Engineering Canada Institute of Quantum Computing University of Waterloo Canada University of Waterloo Department of Physics and Astronomy Canada

ISBN: (纸本)9798350326734

We demonstrate a digital electro-optic modulator (EOM) bias controller designed to maximize the extinction ratio of 100ps optical pulses, which are utilized for exciting single photons from quantum dots. The optical pulses formed by this EOM, continuous-wave tunable diode laser, and electronic pulse generator are an affordable and compact alternative to pulsed Ti: Sapphire Lasers. © 2023 IEEE.

关键词： Semiconductor quantum dots

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Predicting Cervical Cancer Using Deep Learning Mutation-based Atom Search Optimization (MASO) Algorithm 2

Predicting Cervical Cancer Using Deep Learning Mutation-base...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Mayuri, A.V.R. Selvekumar Subash, K. Aeron, Anurag Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Sehore India Bahir Dar University Bahir Dar Institute of Technology Department of Electrical and Computer Engineering Ethiopia Department of Data Science Trichy India Miet Meerut Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350384277

Worldwide, women are compressed by cervical cancer, which is a prevalent malignancy. This disease, which is currently the fourth leading cause of death for women, shows no symptoms when it first arises. Cells that cause cervical cancer multiply gradually at the cervix. If this cancer is detected early enough, treatment can be effective. Presently, it is difficult for medical workers to detect this kind of cancer before it spreads rapidly. This study used a number of machine learning classification algorithms with risk markers to predict cervical cancer. These study suggestions mutation-based Atom Search Optimization (MASO) with a Deep Convolutional Neural Network (DCNN) to offer a original method for automated diagnosis of uterine cervical cancer. Though DCNN is used to extract features from medical imaging data, MASO is used to increase the optimization of the diagnostic model. Composed, these technologies have the potential to rise cervical cancer diagnosis accuracy and efficiency, offering an automated approach for early detection and prompt intervention. Our proposed model established a strong 95% accuracy rate for generalization. This study explores the difficulties caused by missing values and class imbalance in the specific dataset with the goal of assisting medical professionals in the early detection of cervical cancer and improving treatment for people that are impacted by the illness. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Infant Monitoring System Using computer Vision and AI

Smart Infant Monitoring System Using Computer Vision and AI

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Singh, Gurpreet Shekhar, Abhishek Raj Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

The new era of technology is being greatly influenced by the field of artificial intelligence. computer vision and deep learning have become increasingly important due to their ability to process vast amounts of data and provide insights and solutions in a variety of fields. computer vision, deep learning and signal analysis have been used in a growing number of applications and services including smart devices, image, and speech recognition, healthcare, etc., one such device is an infant monitoring system. It monitors the daily activities of the infant such as their sleeping patterns, sounds, and movements. In this paper, deep learning and computer vision libraries were used to develop algorithms to detect whether the infant was in any uncomfortable situation such as sleeping on its back, face being covered and whether the infant was awake. The smart infant monitoring system detects the infant's unsafe resting situation in real time and sent immediate alerts to the caretaker's device. This paper presents the design flow of a smart infant monitoring system consisting of a night vision camera, a Jetson Nano, and a Wi-Fi internet connection. The pose estimation and awake detection algorithms were developed and tested successfully for different infant resting/sleeping situations. The smart infant monitoring system provides significant benefits for safety and an improved understanding of infants' sleep patterns and behavior. © 2023 IEEE.

关键词： Face recognition

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U-PEN++: Redesigning U-PEN Architecture with Multi-Head Attention for Retinal Image Segmentation 34

U-PEN++: Redesigning U-PEN Architecture with Multi-Head Atte...

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Pattern Recognition and Tracking XXXIV 2023

作者： Paheding, Sidke Reyes, Abel A. Devagiri, Jeevan S. Asari, Vijayan K. Alam, Mohammad Department of Applied Computing Michigan Technological University MI49931 United States Department of Electrical and Computer Engineering University of Dayton OH45469 United States Department of Electrical Engineering and Computer Science Texas AM University KingsvilleTX78363 United States

ISBN: (数字)9781510661691

ISBN: (纸本)9781510661684

In the era of the ever-increasing need for computing power, deep learning (DL) algorithms are becoming critical for accomplishing success in various domains, such as accessibility and processing of information from the quantum of data present in the physical, digital, and biological realms. Medical image segmentation is one such application of DL in the healthcare sector. The segmentation of medical images, such as retinal images, enables an efficient analytical process for diagnostics and medical procedures. To segment regions of interest in medical images, UNet has been primarily used as the baseline DL architecture that consists of contracting and expanding paths for capturing semantic features and precision localization. Although several forms of U-Net have shown promise, its limitations such as hardware memory requirements and inaccurate localization of nonstandard shapes still need to be addressed effectively. In this work, we propose U-PEN++, which reconfigures previously developed U-PEN (U-Net with Progressively Expanded Neuron) architecture by introducing a new module named Progressively Expanded Neuron with Attention (PEN-A) that consists of Maclaurin Series of a nonlinear function and multihead attention mechanism. The proposed PEN-A module enriches the feature representation by capturing more relevant contextual information when compared to the U-PEN model. Moreover, the proposed model removes excessive hidden layers, resulting in less trainable parameters when compared to U-PEN. Experimental analysis performed on DRIVE and CHASE datasets demonstrated more effective s egmentation a nd p arameter efficiency of the proposed U-PEN++ architecture for retinal image segmentation tasks when compared to U-Net, U-PEN, and Residual U-Net architectures. © 2023 SPIE.

关键词： Deep learning

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Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems

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IEEE Communications Surveys and Tutorials 2025年第3期27卷 2085-2128页

作者： Wang, Xiaofei Zhao, Yunfeng Qiu, Chao Hu, Qinghua Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin300072 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system’s adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research, with the hope of arousing the research community’s interest in this emerging and exciting interdisciplinary field. © 1

关键词： Resource allocation

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