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Big social data analysis for quality of MOOC courses: the moderating role of practical examples

Quality and Quantity 2025年 1-33页

作者： Nilashi, Mehrbakhsh Abumalloh, Rabab Ali Zibarzani, Masoumeh Centre for Business Informatics and Industrial Management (CBIIM) UCSI University No. 1 Jalan Menara Gading UCSI Heights Cheras Kuala Lumpur 56000 Malaysia Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Department of Management Faculty of Social Sciences and Economics Alzahra University Tehran Iran

Big social data analysis has played an important role in understanding learners’ preferences in Massive Open Online Courses (MOOCs). This study investigates learners’ satisfaction with MOOCs using big social data analysis and a large-scale survey. The learners’ online reviews are analyzed using a hybrid method based on machine learning techniques and Partial Least Squares Structural Equation Modeling. We adopted a text mining approach, Latent Dirichlet Allocation (LDA), which is widely used in topic modeling. We utilized a large dataset of 6004 online reviews obtained from the learners of Udemy’s online courses. The results from the analysis identified nine topics as aspects of the quality of online courses, namely Course Accessibility, Course Cost, Course Material Quality, Course Organization, Course Resource Availability, Course Usefulness, Instructor Explanation, Instructor Knowledge, and Practical Examples in the Course. The results of the analysis were used to construct a research model, which was evaluated using a large-scale survey (N = 464). The results of the PLS-SEM confirmed the significant impacts of the proposed research factors, except for course organization, on learners’ satisfaction. The results also revealed that the practical examples in the course moderate the relationships between course usefulness, instructor explanation, instructor knowledge, and learners’ satisfaction. The findings of this research contribute to both the theoretical and methodological aspects of discovering and analyzing the influential factors for learners' satisfaction with MOOCs, thereby offering valuable insights for enhancing the overall quality of education in digital learning environments. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Big social data Education quality Learners' satisfaction Learners’ online reviews Online courses Soft computing

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Dynamic Channel Allocation via Bandit Learning for WiFi 7 Networks with Multi-Link Operation

Dynamic Channel Allocation via Bandit Learning for WiFi 7 Ne...

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IEEE Conference on Wireless Communications and Networking

作者： Shumin Lian Jingwen Tong Liqun Fu School of Informatics Xiamen University Xiamen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The upcoming IEEE 802.11be standard, termed WiFi 7, introduces multi-link operation (MLO), enabling devices to establish multiple simultaneous connections utilizing different frequencies and channels. While MLO has the potential to boost network throughput, optimizing channel allocation in WiFi 7 networks introduces many challenges. In this paper, we propose a best-arm identification-enabled Monte Carlo tree search (BAI-MCTS) algorithm for efficient channel allocation in WiFi 7 networks. Specifically, we first employ an efficient mechanism to calculate the network throughput by capturing the essential features of the CSMA protocol. We then formulate this channel allocation problem as a multi-armed bandit (MAB) problem. However, solving this MAB problem induces high sample complexity due to the large-arm space. To overcome this challenge, we introduce BAI-MCTS by combining the BAI and MCTS techniques. Notably, BAI-MCTS has a fast convergence rate and low sample complexity. Simulation results demonstrate that the proposed algorithm outperforms the baseline algorithms in terms of the convergence rate, which is about 42.40% faster than the UCT algorithm when reaching 95% of the optimal value.

关键词： Protocols Monte Carlo methods Heuristic algorithms Simulation Channel allocation Throughput Dynamic scheduling Multiaccess communication Wireless fidelity Convergence

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TTT-SEG: A Multi-structure Segmentation on Cardiac MRI using Vision Testing-time Training Network

TTT-SEG: A Multi-structure Segmentation on Cardiac MRI using...

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Su, Vanessa Yuan, Xiaohan Pan, Shaoyan Hu, Mingzhe Chen, Xiaoqian Tian, Sibo Yang, Xiaofeng Department of Computer Science Emory University AtlantaGA United States Department of Radiation Oncology School of Medicine Emory University AtlantaGA United States Department of Computer Science and Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9781510685987

We propose TTT-SEG, a Test-Time Training Segmentation model, for automatic cardiac organ segmentation on MRIs. Manual contouring in MR imaging is time-consuming and labor-intensive, making automatic segmentation crucial for efficient therapy workflows. Despite advancements in Convolutional Neural Networks (CNNs) and Vision Transformer-based Networks achieving state-of-the-art accuracy, their performance is hindered by the lack of large-scale MRI datasets, leading to poor generalization on new patients. TTT-SEG addresses this issue by integrating a CNN with a novel learning-to-learn strategy. Our model features a dual-loop mechanism: a Reconstruction Learning Loop (RLL) for learning self-supervised features and an Adaptive Learning Loop (ALL) for segmentation features. Unlike the traditional networks, the RLL learns even during testing, improving segmentation accuracy for specific samples. Evaluating TTT-SEG on the Automated Cardiac Diagnosis Challenge (ACDC) dataset shows superior performance in segmenting the left and right ventricles and myocardium, outperforming methods like Vnet, nnUnet, SwinUNETR, and nnFormer. These results demonstrate TTT-SEG's potential to enhance cardiac organ segmentation and improve cardiac-related diagnosis and therapy workflows. © 2025 SPIE.

关键词： Electrocardiography

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Evaluating Beam Sweeping for AoA Estimation with an RIS Prototype: Indoor/Outdoor Field Trials

arXiv

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arXiv 2025年

作者： Vordonis, Dimitris Kompostiotis, Dimitris Paliouras, Vassilis Alexandropoulos, George C. Grec, Florin Electrical and Computer Engineering Department University of Patras Greece Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Noordwijk Netherlands

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology to enhance wireless communication systems by enabling dynamic control over the propagation environment. However, practical experiments are crucial towards the validation of the theoretical potential of RISs while establishing their real-world applicability, especially since most studies rely on simplified models and lack comprehensive field trials. In this paper, we present an efficient method for configuring a 1-bit RIS prototype at sub-6 GHz, resulting in a codebook oriented for beam sweeping;an essential protocol for initial access and Angle of Arrival (AoA) estimation. The measured radiation patterns of the RIS validate the theoretical model, demonstrating consistency between the experimental results and the predicted beamforming behavior. Furthermore, we experimentally prove that RIS can alter channel properties and by harnessing the diversity it provides, we evaluate beam sweeping as an AoA estimation technique. Finally, we investigate the frequency selectivity of the RIS and propose an approach to address indoor challenges by leveraging the geometry of environment. © 2025, CC BY-NC-ND.

关键词： Frequency estimation

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Direction-of-Arrival Estimation with Arbitrary Linear Array: A Turbo-HS Approach

Direction-of-Arrival Estimation with Arbitrary Linear Array:...

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Power Electronics, computer Applications (ICPECA), IEEE International Conference on

作者： Zhendong Chen Yongfeng Huang Dingzhao Li Xicheng Lu Haixin Sun School of Informatics Xiamen University Xiamen China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Conventional subspace-based direction-of-arrival (DOA) estimation algorithms require optimal environments to achieve satisfactory estimation accuracy. With the advancement of sparse signal recovery theory, sparse optimization-based DOA estimation algorithms have exhibited commendable performance. One of the popular sparse optimization method is sparse Bayesian learning (SBL), due to the fact that it does not require regularization parameters. However, existing SBL algorithms do not adequately address the issue of noise modeling. In order to more accurately model the received signal covariance matrix under finite snapshots, we transform the signal covariance matrix into the form of sparse representation, normalize the variance of the observation matrix and vectorize it. Based on this, we introduce the Turbo-CS framework, which divides the DOA estimation problem into two subproblems: LMMSE estimation for external noise separation and a novel prior for internal SBL DOA estimation. As demonstrated by the experimental results, the proposed algorithm performs optimally in a variety of scenarios.

关键词： Direction-of-arrival estimation Sparse approximation Computational modeling Noise Estimation Transforms Power electronics Bayes methods Sparse matrices Covariance matrices

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Method of Automated Dataset Collection for Microwave Filters Synthesis

Method of Automated Dataset Collection for Microwave Filters...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： O. V. Arinin D. M. Bakhmach A. I. Katsnelson A. S. Kurbeteva A. A. Yelizarov Moscow Technical University of Communications and Informatics Moscow Russia Electronic Engineering Department HSE University Moscow Russia Computer Engineering Department HSE University Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

This research discusses the method of dataset collection automatization for microwave filter synthesis by integrating machine learning techniques, thus reducing development time. Utilizing the 3D electromagnetic analysis software package, the study involves simulation and collecting geometric parameters and amplitude-frequency characteristics from three variants of passband highly selective microstrip tworesonator combined filters with stepped impedance resonators. In this paper, specific items and restrictions on filter topology creation in the simulation program are discussed, as well the review of the script used for dataset collection and its requirements is given. In addition, a dataset structure and format are described.

关键词： Microstrip filters Three-dimensional displays Network topology Resonator filters Machine learning Microwave communication Information filters Topology Microwave filters Optimization

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NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medical Image Retrieval

NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medi...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ju-Chan Kim Pham Van Nguyen Duc-Tai Le Hyunseung Choo College of Computing and Informatics Sungkyunkwan University Suwon South Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This paper presents NDAS (Noise-Decomposed Ab-normal Segmentation), an innovative framework for robust med-ical image retrieval and segmentation. By explicitly decomposing noise and abnormal features, NDAS enhances retrieval accuracy and segmentation precision. Experimental results on the BraTS 2019 dataset validate NDAS's superiority over CBMIR and SBMIR methods, achieving the highest Top-5 accuracy and Dice coefficient. This demonstrates its effectiveness in handling noise, isolating pathological regions, and retrieving diagnostically relevant medical images. NDAS offers a significant advancement in medical imaging analysis, emphasizing its potential for clinical applications requiring precision and reliability.

关键词： Image segmentation Pathology Accuracy Noise Image retrieval Information management Reliability Medical diagnostic imaging

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S2L-CM: Scribble-supervised nuclei segmentation in histopathology images using contrastive regularization and pixel-level multiple instance learning

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computers in Biology and Medicine 2025年第Pt B期192卷 110293-110293页

作者： Oh, Hyun-Jic Min, Seonghui Jeong, Won-Ki Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of

Deep learning-based pathology nuclei segmentation algorithms have demonstrated remarkable performance. Conventional methods mostly focus on supervised learning, which requires significant manual effort to generate ground truth labels. Recently, weakly supervised learning has been extensively explored as a method for overcoming this limitation by training models with sparse annotations. However, the performance is inferior compared to that of fully supervised learning schemes. This paper proposes S2L-CM, a scribble-supervised nuclei segmentation framework based on two ideas;first, we leverage self-generated pseudo labels from user-given sparse scribble labels to train the deep learning model without full ground-truth labels, and second, we utilize multiscale contrastive regularization and pixel-level multiple-instance learning to further refine pseudo labels to improve segmentation performance. We demonstrate the effectiveness and robustness of our method on four nuclei datasets by comparing it with existing state-of-the-art methods. Code will be available at: https://***/hvcl/S2L-CMhttps://***/hvcl/S2L-CM. © 2025

关键词： Self-supervised learning

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Closed-Loop Clustering-Based Global Bandwidth Prediction in Real-Time Video Streaming

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 448-462页

作者： Afshar, Sepideh Razavi, Reza Moshirpour, Mohammad Department of Electrical and Software Engineering University of Calgary CalgaryABT2N 1N4 Canada Userful CalgaryABT2G 4Y5 Canada Department of Informatics Donald Bren School of Information and Computer Sciences University of California at Irvine IrvineCA92697 United States

Accurate throughput forecasting is essential for ensuring the seamless operation of Real-Time Communication (RTC) applications. These demands for accurate throughput forecasting become particularly challenging when dealing with wireless access links, as they inherently exhibit fluctuating bandwidth. Ensuring an exceptional user Quality of Experience (QoE) in this scenario depends on accurately predicting available bandwidth in the short term since it plays a pivotal role in guiding video rate adaptation. Yet, current methodologies for short-term bandwidth prediction (SBP) struggle to perform adequately in dynamically changing real-world network environments and lack generalizability to adapt across varied network conditions. Also, acquiring long and representative traces that capture real-world network complexity is challenging. To overcome these challenges, we propose closed-loop clustering-based Global Forecasting Models (GFMs) for SBP. Unlike local models, GFMs apply the same function to all traces enabling cross-learning, and leveraging relationships among traces to address the performance issues seen in current SBP algorithms. To address potential heterogeneity within the data and improve prediction quality, a clustered-wise GFM is utilized to group similar traces based on prediction accuracy. Finally, the proposed method is validated using real-world datasets of HSDPA 3G, NYC LTE, and Irish 5G data demonstrating significant improvements in accuracy and generalizability. © 2023 CCBY.

关键词： Video streaming

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A Wideband Rotary Antenna Based on Gap Wavegudie Technology for mmWave Applications

A Wideband Rotary Antenna Based on Gap Wavegudie Technology ...

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European Conference on Antennas and Propagation, EuCAP

作者： Ali Farahbakhsh Davood Zarifi Michal Mrozowski Department of Microwave and Antenna Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland Department of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran School of Electrical and Computer Engineering University of Kashan Kashan Iran

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This study introduces the design of a gap waveguide-based rotary antenna capable of 360° mechanical beam-steering for wideband mmWave applications. This innovative design overcomes the typical limitations of electronically steerable antennas, such as narrow bandwidth, restricted steering range, gain reduction, and high cost and complexity. The designed rotary antenna achieves a gain of 13.8 dBi without any scan loss across the entire 360° steering range from 45 to 60 GHz. The proposed rotary antenna is a promising candidate for a variety of mmWave mechanical beam scanning systems.

关键词： Steerable antennas Costs Electromagnetic scattering Gap waveguide Europe Broadband antennas Impedance Millimeter wave communication Electromagnetics Wideband

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