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Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Ayush Tibrewal Shikha Divyashikha Sethia Department of Applied Physics Delhi Technological University Delhi India Department of Computer Science and Engineering Delhi Technological University Delhi India Department of Software Engineering Delhi Technological University Delhi India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Understanding and detecting stress in today’s fast-paced world remains challenging. Previous research has explored various physiological signals, including EEG (Electroencephalography), to quantify stress. However, accurately assessing stress poses a significant challenge, particularly in preprocessing and feature extraction of EEG signals. Hence, this paper employs advanced deep learning algorithms, including ResNet18-1D, DenseNet-1D, and VGG16-1D, on raw EEG signals for automated stress classification. The study utilizes the publicly available EEG-based SAM40 dataset for stress classification and further validates the results on the EEG-based MAT dataset. The results demonstrate the efficacy of the ResNet18-1D model by achieving the highest of 98.73% and 97.24% on the SAM40 and MAT datasets, respectively. The findings pave the way for further exploration and utilization of raw EEG signals across diverse research domains beyond stress classification.

关键词： Deep learning Measurement Technological innovation Time series analysis Brain modeling Electroencephalography Data models

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Symmetric Pruning in Quantum Neural Networks 11

Symmetric Pruning in Quantum Neural Networks

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Xinbiao Liu, Junyu Liu, Tongliang Luo, Yong Du, Yuxuan Tao, Dacheng Institute of Artificial Intelligence School of Computer Science Wuhan University China Pritzker School of Molecular Engineering The University of Chicago United States JD Explore Academy Chicago Quantum Exchange United States Kadanoff Center for Theoretical Physics United States qBraid Co. Sydney AI Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory Wuhan China

Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance, ground states preparation (GSP) is classically intractable for most large-scale Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern quantum machines, have emerged as a leading protocol to conquer this issue. As such, the performance enhancement of QNNs becomes the core in GSP. Empirical evidence showed that QNNs with handcraft symmetric ansätze generally experience better trainability than those with asymmetric ansätze, while theoretical explanations remain vague. To fill this knowledge gap, here we propose the effective quantum neural tangent kernel (EQNTK) and connect this concept with over-parameterization theory to quantify the convergence of QNNs towards the global optima. We uncover that the advance of symmetric ansätze attributes to their large EQNTK value with low effective dimension, which requests few parameters and quantum circuit depth to reach the over-parameterization regime permitting a benign loss landscape and fast convergence. Guided by EQNTK, we further devise a symmetric pruning (SP) scheme to automatically tailor a symmetric ansatz from an over-parameterized and asymmetric one to greatly improve the performance of QNNs when the explicit symmetry information of Hamiltonian is unavailable. Extensive numerical simulations are conducted to validate the analytical results of EQNTK and the effectiveness of SP. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Hamiltonians

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Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion 23

Maat: Performance Metric Anomaly Anticipation for Cloud Serv...

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Proceedings of the 38th IEEE/ACM International Conference on Automated software engineering

作者： Cheryl Lee Tianyi Yang Zhuangbin Chen Yuxin Su Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Software Engineering Sun Yat-sen University Zhuhai China. School of Software Engineering Sun Yat-sen University Zhuhai China

ISBN: (纸本)9798350329964

Ensuring the reliability and user satisfaction of cloud services necessitates prompt anomaly detection followed by diagnosis. Existing techniques for anomaly detection focus solely on real-time detection, meaning that anomaly alerts are issued as soon as anomalies occur. However, anomalies can propagate and escalate into failures, making faster-than-real-time anomaly detection highly desirable for expediting downstream analysis and intervention. This paper proposes Maat, the first work to address anomaly anticipation of performance metrics in cloud services. Maat adopts a novel two-stage paradigm for anomaly anticipation, consisting of metric forecasting and anomaly detection on forecasts. The metric forecasting stage employs a conditional denoising diffusion model to enable multi-step forecasting in an auto-regressive manner. The detection stage extracts anomaly-indicating features based on domain knowledge and applies isolation forest with incremental learning to detect upcoming anomalies. Thus, our method can uncover anomalies that better conform to human expertise. Evaluation on three publicly available datasets demonstrates that Maat can anticipate anomalies faster than real-time comparatively or more effectively compared with state-of-the-art real-time anomaly detectors. We also present cases highlighting Maat's success in forecasting abnormal metrics and discovering anomalies.

关键词： could computing

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Real-Time Traffic Sign Detection for Autonomous Vehicles Using YOLOv11

Real-Time Traffic Sign Detection for Autonomous Vehicles Usi...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Shahabub Alam Al Sani Md Tanvir Islam Ayan Kumar Ghosh Avijit Chowdhury Department of Computer Science Universität Potsdam Potsdam Germany Dept. of Industrial & Production Engineering Jashore University of Science and Technology Jashore Bangladesh Department of Software Sungkyunkwan University Suwon Republic of Korea Department of Mechanical Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Reliable detection of traffic signs plays a critical role in autonomous vehicles and driver-assistance technologies, especially when operating under diverse and complex real-world conditions. This study investigates the effectiveness of various YOLO-based object detection frameworks, ranging from YOLOv5 to the newly proposed YOLOv11, in addressing the task of traffic sign recognition. The dataset includes varying class configurations and challenging conditions such as low-light and noise, enabling a comprehensive analysis of model robustness. YOLOv11 outperformed prior versions, achieving the highest mAP of 0.961, establishing it as a state-of-the-art (SOTA) solution. The study also conducted ablations to analyze the impact of batch size, data augmentation, and adverse conditions on model performance. Notably, YOLOv11 demonstrated strong resilience to noise and low-light scenarios, maintaining high accuracy even under dense conditions. Comparisons with existing models further validated its superiority in terms of precision and consistency. These results highlight YOLOv11’s potential for real-world traffic sign detection tasks, where reliability is critical.

关键词： YOLO Analytical models Adaptation models Electric potential Accuracy Noise Robustness Real-time systems Autonomous vehicles Resilience

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3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

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computers, Materials & Continua 2021年第2期66卷 1757-1770页

作者： Khalil Khan Jehad Ali Kashif Ahmad Asma Gul Ghulam Sarwar Sahib Khan Qui Thanh Hoai Ta Tae-Sun Chung Muhammad Attique Department of Computer Science and Software Engineering Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur-KPKPakistan Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwon16499South Korea Hamad Bin Khalifa University DohaQatar Department of Statistics Shaheed Benazir Bhutto Women UniversityPeshawarPakistan Department of Software Engineering University of Azad Jammu and KashmirPakistan Department of Electronics and Telecommunications Politecnico di TorinoTorino10129Italy Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Department of Computer Engineering Ajou UniversitySuwon16499Korea Department of Software Sejong UniversitySeoul05006South Korea

Face image analysis is one among several important cues in computer *** the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis *** parsing strongly benefits various human face image analysis tasks inducing face pose *** this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing *** have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and *** extract features from gray scale images by using *** train a classifier using the extracted *** use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic *** use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation *** assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.

关键词： Face image analysis face parsing face pose estimation

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Terrestrial Radio Propagation Research Datasets Repository 2

Terrestrial Radio Propagation Research Datasets Repository

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Adewole, Kayode S. Olagunju, Hawau Faruk, Nasir Imoize, Agbotiname Lucky Alozie, Emmanuel Sowande, Olugbenga A. Abdulkarim, Abubakar Usman, Aliyu D. Olayinka Imam-Fulani, Yusuf Oloyede, Abdulkarim A. Garba, Salisu Baba, Bashir Abdullahi Musa, Abdulwaheed Adediran, Yinusa A. Taura, Lawan S. University of Ilorin Department of Computer Science Ilorin Nigeria University of Ilorin Department of Telecommunication Science Ilorin Nigeria Sule Lamido University Department of Information Technology Kafin Hausa Nigeria University of Lagos Department of Electrical and Electronics Engineering Nigeria Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany Ahmadu Bello University Department of Electrical Engineering Zaria Nigeria Ahmadu Bello University Department of Electronics and Telecommunication Engineering Zaria Nigeria Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria Sule Lamido University Department of Cyber Security Kafin Hausa Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

Terrestrial Radio Propagation (TRP) involves radio wave propagation from one station to another over the surface of the earth. Radio communication systems have been deployed for broadcasting, mobile cellular, and public safety. Radio propagation planning plays a crucial role in designing and deploying terrestrial radio networks. Radio propagation models (Path loss Models) are utilized during the link budget, coverage, and interference estimations. The models guide radio network engineers in choosing appropriate placements of radio network equipment, such as the base stations. However, the high cost attributed to TRP data collection, lack of open access, and accurate TRP datasets hinders the development of path loss models that can reliably predict outcomes for different use cases. To address this problem, this paper aims to present a robust TRP repository that provides a platform for hosting and disseminating TRP datasets, which the research community could use for path loss modeling. The repository was implemented using the latest technologies, and its performance was evaluated. The system has been deployed for public access. © 2023 IEEE.

关键词： Wave propagation

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge Computing Environment

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge Computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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EEG Spectral Analysis for Inattention Detection in Academic Domain

EEG Spectral Analysis for Inattention Detection in Academic ...

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Frontiers in Education (FIE) Conference

作者： Sreekanth Gopi Nasrin Dehbozorgi Department of Computer Science Kennesaw State University Kennesaw GA Department of Software Engineering Kennesaw State University Kennesaw GA

The increasing rate of attention deficit among students, attributed to social media, has far-reaching consequences on their academic performance. Inattention or lack of attention is a state of absent-mindedness or not paying enough attention to the details. A rich body of literature suggests that it is highly associated with underachievement in the academic context. In particular, students who have been clinically diagnosed with Attention Deficit Hyperactivity Disorder(ADHD), a neuro-developmental disorder characterized by inattention, hyperactivity, and impulsivity symptoms are more at risk of under-performance and retention, with some leaving school without a terminal degree. In addition to the academic domain, inattention generally impacts the quality of life and future occupations. Research suggests that ADHD is also attributed to societal and unemployment excess costs as well as productivity loss and healthcare expenses which are estimated to be over $14K per adult in the United States (US). Although more than eight million adults were identified with ADHD in the US by 2018, not all inattention cases are associated with ADHD. Inattention could be caused by several other factors such as stress or anxiety and its early detection and timely intervention is critical, especially in the academic domain. There are existing studies that analyze brain signals by Electroencephalogram (EEG) scans to identify individuals who have ADHD. In this study, we developed a Machine learning (ML) pipeline model that is trained on both ADHD and inattention data to determine if a person is having an attention problem. In the first phase of developing the model, we trained a few different classifiers on a 19-channel public EEG dataset of 60 ADHD and 61 non-ADHD participants. Data analysis showed K-Nearest Neighbor (KNN) classifier outperformed other classifiers with an accuracy of 89%. While many of the existing papers focus on ADHD data, in this work we expand our model to analyze the

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Federated Non-Intrusive Load Monitoring for Smart Homes Utilizing Attention-Based Aggregation

Federated Non-Intrusive Load Monitoring for Smart Homes Util...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Shamisa Kaspour Abdulsalam Yassine Department of Computer Science Lakehead University Thunder Bay Canada Department of Software Engineering Lakehead University Thunder Bay Canada

Nowadays, Non-Intrusive Load Monitoring (NILM) with Federated Learning (FL) framework has become a growing study towards providing a secure energy disaggregation system in smart homes. This study aims at deploying an attention-based aggregation (FedAtt) approach in FL to emphasize agents’ behavioral differences when consuming energy from various appliances. The goal of the proposed technique is to minimize the weighted distance between the parameters of the local model and the global model to better represent each local model’s characteristics. In this paper, we examine two different models for NILM: Short Sequence-to-Point (SS2P) and Variational Auto-Encoder (VAE). Our goal is to evaluate the effectiveness of FedAtt. The evaluation of the framework was carried out using the UK-DALE and REFIT datasets. The obtained results were then compared against centralized approaches of the models as well as FedAvg. Our findings show that FedAtt generates comparable results to the centralized model and FedAvg while improving the stability of FL at different values of added noise to local parameters.

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Stroboscopic x-ray diffraction microscopy of dynamic strain in diamond thin-film bulk acoustic resonators for quantum control of nitrogen-vacancy centers

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Physical Review Applied 2024年第2期22卷 024016-024016页

作者： Anthony D’Addario Johnathan Kuan Noah F. Opondo Ozan Erturk Tao Zhou Sunil A. Bhave Martin V. Holt Gregory D. Fuchs Department of Physics Elmore Family School of Electrical and Computer Engineering Center for Nanoscale Materials School of Applied and Engineering Physics Kavli Institute at Cornell for Nanoscale Science Ithaca New York USA

Bulk-mode acoustic waves in a crystalline material exert lattice strain through the thickness of the sample, which couples to the spin Hamiltonian of defect-based qubits such as the nitrogen-vacancy (N-V) center defect in diamond. This mechanism has previously been harnessed for unconventional quantum spin control, spin decoherence protection, and quantum sensing. Bulk-mode acoustic wave devices are also important in the microelectronics industry as microwave filters. A key challenge in both applications is a lack of appropriate operando microscopy tools for quantifying and visualizing gigahertz-frequency dynamic strain. In this work, we directly image acoustic strain within N-V center-coupled diamond thin-film bulk acoustic wave resonators using stroboscopic scanning hard x-ray diffraction microscopy at the Advanced Photon Source. The far-field scattering patterns of the nanofocused x-ray diffraction encode strain information entirely through the illuminated thickness of the resonator. These patterns have a real-space spatial variation that is consistent with the bulk strain’s expected modal distribution and a momentum-space angular variation from which the strain amplitude can be quantitatively deduced. We also perform optical measurements of strain-driven Rabi precession of of the N-V center spin ensemble, providing an additional quantitative measurement of the strain amplitude. As a result, we directly measure one of the six N-V spin-stress coupling parameters, b=2.73(2) MHz/GPa, by correlating these measurements at the same spatial position and applied microwave power. Our results demonstrate a unique technique for directly imaging ac lattice strain in micromechanical structures and provide a direct measurement of a fundamental constant for the N-V center defect spin Hamiltonian.

关键词： Quantum control Spin dynamics Strain Stress Micromechanical devices Nitrogen vacancy centers in diamond Acoustic techniques X-ray diffraction

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