152 research outputs found
FedChain: An Efficient and Secure Consensus Protocol based on Proof of Useful Federated Learning for Blockchain
Blockchain has become a popular decentralized paradigm for various
applications in the zero-trust environment. The core of the blockchain is the
consensus protocol, which establishes consensus among all the participants. PoW
(Proof-of-Work) is one of the most popular consensus protocols. However, the
PoW consensus protocol which incentives the participants to use their computing
power to solve a meaningless hash puzzle is continuously questioned as
energy-wasting. To address these issues, we propose an efficient and secure
consensus protocol based on proof of useful federated learning for blockchain
(called FedChain). We first propose a secure and robust blockchain architecture
that takes federated learning tasks as proof of work. Then a pool aggregation
mechanism is integrated to improve the efficiency of the FedChain architecture.
To protect model parameter privacy for each participant within a mining pool, a
secret sharing-based ring-all reduce architecture is designed. We also
introduce a data distribution-based federated learning model optimization
algorithm to improve the model performance of FedChain. At last, a
zero-knowledge proof-based federated learning model verification is introduced
to preserve the privacy of federated learning participants while proving the
model performance of federated learning participants. Our approach has been
tested and validated through extensive experiments, demonstrating its
performance
THE RELATIONSHIP BETWEEN TEACHERS' PERCEPTION TOWARDS THEIR LEADERSHIP CAPACITY AND THEIR CLASSROOM MANAGEMENT STYLES IN THE SECOND AFFILIATED MIDDLE SCHOOL OF YUNNAN NORMAL UNIVERSITY, CHINA
This study aimed to survey 80 full-time lecturers in selected schools for the 2020 school year (September to January). The main data collection tool is a questionnaire divided into three parts. The relationship between these two variables is analyzed using the mean and standard deviation. The research results show that the summary of means and standard deviations of teachers’ perception towards their leadership capacity. The total mean score was 2.86 in the range of 2.51-3.50 and is interpreted as Good enough. And the research results show that the total mean score of teachers’ classroom management styles was 2.84, and it was in the range of 2.51-3.50. According to the criteria of the interpretation, teachers’ perceptions of classroom management styles were Moderate. Pearson Product Moment Correlation Coefficient was applied to test in this study, Pearson correlation was -.152 and Sig was .001. Which indicated that there was a weak negative relationship between teachers’ perception towards leadership capacity and classroom management styles at Second Affiliated Middle School of Yunnan Normal University, China
Dynamic analysis of an under levelling-gripping system of an jacket platform under offshore environmental loads
This paper concerns dynamic analysis of an underwater leveling-gripping system which is mounted on a jacket under the influence of offshore environmental loads. Based on the Shinozuka theory, the wave load is calculated in the time domain while the ocean current and wind load on the jacket structure are calculated as constant loads. The main environmental loads and its combination which jacket withstand in leveling process are therefore defined. Using SACS software, according to the South China Sea conditions, a platform bottom dynamic response is calculated under extreme environmental loads in different return period. ADAMS software is also used to dynamically analyze the contact force of key clamping contact parts of leveling-gripping system in leveling process. With the result of analysis, the influence of environmental loads on leveling-gripping system, changes with time, can be obtained accurately, which is an important basis for the design of key parts of the leveling-gripping system
Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System
The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system
FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Table
Low-Light Video Enhancement (LLVE) has received considerable attention in
recent years. One of the critical requirements of LLVE is inter-frame
brightness consistency, which is essential for maintaining the temporal
coherence of the enhanced video. However, most existing single-image-based
methods fail to address this issue, resulting in flickering effect that
degrades the overall quality after enhancement. Moreover, 3D Convolution Neural
Network (CNN)-based methods, which are designed for video to maintain
inter-frame consistency, are computationally expensive, making them impractical
for real-time applications. To address these issues, we propose an efficient
pipeline named FastLLVE that leverages the Look-Up-Table (LUT) technique to
maintain inter-frame brightness consistency effectively. Specifically, we
design a learnable Intensity-Aware LUT (IA-LUT) module for adaptive
enhancement, which addresses the low-dynamic problem in low-light scenarios.
This enables FastLLVE to perform low-latency and low-complexity enhancement
operations while maintaining high-quality results. Experimental results on
benchmark datasets demonstrate that our method achieves the State-Of-The-Art
(SOTA) performance in terms of both image quality and inter-frame brightness
consistency. More importantly, our FastLLVE can process 1,080p videos at
Frames Per Second (FPS), which is faster
than SOTA CNN-based methods in inference time, making it a promising solution
for real-time applications. The code is available at
https://github.com/Wenhao-Li-777/FastLLVE.Comment: 11pages, 9 Figures, and 6 Tables. Accepted by ACMMM 202
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning
Federated learning (FL) is an emerging paradigm in machine learning, where a
shared model is collaboratively learned using data from multiple devices to
mitigate the risk of data leakage. While recent studies posit that Vision
Transformer (ViT) outperforms Convolutional Neural Networks (CNNs) in
addressing data heterogeneity in FL, the specific architectural components that
underpin this advantage have yet to be elucidated. In this paper, we
systematically investigate the impact of different architectural elements, such
as activation functions and normalization layers, on the performance within
heterogeneous FL. Through rigorous empirical analyses, we are able to offer the
first-of-its-kind general guidance on micro-architecture design principles for
heterogeneous FL.
Intriguingly, our findings indicate that with strategic architectural
modifications, pure CNNs can achieve a level of robustness that either matches
or even exceeds that of ViTs when handling heterogeneous data clients in FL.
Additionally, our approach is compatible with existing FL techniques and
delivers state-of-the-art solutions across a broad spectrum of FL benchmarks.
The code is publicly available at https://github.com/UCSC-VLAA/FedConvComment: 9 pages, 6 figures. Equal contribution by P. Xu and Z. Wan
Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?
Objectives: To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. Methods: After signing informed consent, 54 biopsy-naĂŻve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). Results: Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value < 0.05) using the Gradient Boosting classifier. Conclusions: Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.</p
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