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An improved Schnorr-based multi-signature scheme with application to blockchain 3

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An improved Schnorr-based multi-signature scheme with applic...

3rd IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

作者： Li, Chunlan Wu, Yulong Yu, Feng Computer Science and Engineering School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

ISBN: (纸本)9781665425186

A multi-signature scheme allows a group of signers to jointly generate a signature on a common message, which can be verified by anyone, making the signature more compact. Multi-signature has the advantage of fast verification, and it helps reduce bandwidth and the cost of validation and storage. Recently, the multi-signature has drawn much attention because its advantages are desirable for blockchain and cryptocurrency. However, the traditional Schnorr-based multisignature scheme suffers from an excessive number of communications. This paper proposes an improved Schnorr-based multi-signature scheme ASMS, which reduces one round of communication in a traditional scheme, supporting public key aggregation. Thus, it is suitable for e-business and e-government affairs scenarios. As an application, we implemented our solution in Fabric, an enterprise blockchain platform, using chaincode technology. © 2021 IEEE

关键词： Blockchain

A Fault Diagnostic Method for Rolling Bearings Based on CAR-SNN Model

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A Fault Diagnostic Method for Rolling Bearings Based on CAR-...

2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Li, Qiuting Wang, Xiuqing Du, Wenxia Hebei Normal University College of Computer and Cyber Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China Hebei Normal University College of China Combustion Engineering Shijiazhuang050024 China

ISBN: (纸本)9798350337754

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual network (ResNet), and Spiking Neural networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings. © 2023 IEEE.

关键词： Failure analysis

Multi-instance partial-label learning with margin adjustment 24

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Multi-instance partial-label learning with margin adjustment

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Wei Tang Yin-Fang Yang Zhaofei Wang Weijia Zhang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China School of Information and Physical Sciences The University of Newcastle Callaghan NSW 2308 Australia

ISBN: (纸本)9798331314385

Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms.

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Beyond 5G network Failure Classification for network Digital Twin Using Graph Neural network

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arXiv 2024年

作者： Isah, Abubakar Aliyu, Ibrahim Shim, Jaechan Ryu, Hoyong Kim, Jinsul HyperIntelligence Media Network Platform Laboratory Department of Intelligent Electronics and Computer Engineering Chonnam National University Gwangju61186 Korea Republic of Department of Computer Science Ahmadu Bello University Zaria Nigeria Korea Republic of

Fifth-generation (5G) core networks in network digital twins (NDTs) are complex systems with numerous components, generating considerable data. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classes in multiclass classification. To address this problem, we propose a novel method of integrating a graph Fourier transform (GFT) into a message-passing neural network (MPNN) designed for NDTs. This approach transforms the data into a graph using the GFT to address class imbalance, whereas the MPNN extracts features and models dependencies between network components. This combined approach identifies failure types in real and simulated NDT environments, demonstrating its potential for accurate failure classification in 5G and beyond (B5G) networks. Moreover, the MPNN is adept at learning complex local structures among neighbors in an end-to-end setting. Extensive experiments have demonstrated that the proposed approach can identify failure types in three multiclass domain datasets at multiple failure points in real networks and NDT environments. The results demonstrate that the proposed GFT-MPNN can accurately classify network failures in B5G networks, especially when employed within NDTs to detect failure types. Copyright © 2024, The Authors. All rights reserved.

关键词： Message passing

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information Research Institute.

关键词： Long short-term memory

Inaccurate Label Distribution Learning

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arXiv 2023年

作者： Kou, Zhiqiang Jia, Yuheng Wang, Jing Geng, Xin The School of Computer Science and Engineering Southeast University Nanjing211189 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China

Label distribution learning (LDL) trains a model to predict the relevance of a set of labels (called label distribution (LD)) to an instance. The previous LDL methods all assumed the LDs of the training instances are accurate. However, annotating highly accurate LDs for training instances is time-consuming and very expensive, and in reality the collected LD is usually inaccurate and disturbed by annotating errors. For the first time, this paper investigates the problem of inaccurate LDL, i.e., developing an LDL model with noisy LDs. We assume that the noisy LD matrix is a linear combination of an ideal LD matrix and a sparse noise matrix. Consequently, the problem of inaccurate LDL becomes an inverse problem, where the objective is to recover the ideal LD and noise matrices from the noisy LDs. We hypothesize that the ideal LD matrix is low-rank due to the correlation of labels and utilize the local geometric structure of instances captured by a graph to assist in recovering the ideal LD. This is based on the premise that similar instances are likely to share the same LD. The proposed model is finally formulated as a graph-regularized low-rank and sparse decomposition problem and numerically solved by the alternating direction method of multipliers. Furthermore, a specialized objective function is utilized to induce a LD predictive model in LDL, taking into account the recovered label distributions. Extensive experiments conducted on multiple datasets from various real-world tasks effectively demonstrate the efficacy of the proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

Residual K-Nearest Neighbors Label Distribution Learning

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SSRN

SSRN 2023年

作者： Wang, Jing Geng, Xin School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China

Label Distribution Learning (LDL) is a novel learning paradigm that assigns label distribution to each instance. It aims to learn the label distribution of training instances and predict unknown ones. Algorithm Adaptation (AA)-kNN is one of the most representative LDL baselines that adapts the kNN algorithm to LDL. Its generalization risk has been proven to approach zero if given infinite training data. Despite its theoretical advantage, AA-kNN generally performs poorly because real-world LDL problems only have finite or small training data. In this paper, we improve AA-kNN and propose a novel method called Residual k-Nearest Neighbors Label Distribution Learning (RkNN-LDL). First, RkNN-LDL introduces residual label distribution learning. Second, RkNN-LDL exploits the neighborhood structure of label distribution. We prove that RkNN-LDL has an O( [[EQUATION]] ) generalization bound. Besides, extensive experiments validate that RkNN-LDL beats several state-of-the-art LDL methods and statistically outperforms AA-NN. © 2023, The Authors. All rights reserved.

关键词： Nearest neighbor search

Boosting Semi-Supervised Object Detection in Remote Sensing Images with Active Teaching

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arXiv 2024年

作者： Zhang, Boxuan Wang, Zengmao Du, Bo The School of Computer Science Wuhan University Wuhan430072 China The National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence The Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China

The lack of object-level annotations poses a significant challenge for object detection in remote sensing images. To address this issue, active learning and semi-supervised learning techniques have been proposed to enhance the quality and quantity of annotations. Active learning focuses on selecting the most informative samples for annotation, while semi-supervised learning leverages the knowledge from unlabeled samples. In this paper, we propose a novel active learning method to boost semi-supervised object detection for remote sensing images with a teacher-student network, called SSOD-AT. The proposed method incorporates a RoI Comparison module (RoICM) to generate high-confidence pseudo-labels for Regions of Interest (RoIs). Meanwhile, the RoICM is utilized to identify the top-K uncertain images. To reduce redundancy in the top-K uncertain images for human labeling, a diversity criterion is introduced based on object-level prototypes of different categories using both labeled and pseudo-labeled images. Extensive experiments on DOTA and DIOR two popular datasets demonstrate that our proposed method outperforms state-of-the-art methods for object detection in remote sensing images. Compared with the best performance in the SOTA methods, the proposed method achieves 1% improvement at most cases in the whole active learning. © 2024, CC BY.

关键词： Remote sensing

Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems

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arXiv 2024年

作者： Zhong, Qihuang Wang, Kang Xu, Ziyang Liu, Juhua Ding, Liang Du, Bo School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China School of Computer Science University of Sydney Sydney2006 Australia

Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex math word problems, as it usually suffers from three pitfalls: semantic misunderstanding errors, calculation errors, and step-missing errors. Prior studies involve addressing the calculation errors and step-missing errors, but neglect the semantic misunderstanding errors, which is the major factor limiting the reasoning performance of LLMs. To this end, we propose a simple-yet-effective method, namely Deeply Understanding the Problems (DUP), to improve the LLMs’ math problem-solving ability by addressing semantic misunderstanding errors. The core of our method is to encourage the LLMs to deeply understand the problems and extract the key problem-solving information used for better reasoning. Extensive experiments on 10 diverse reasoning benchmarks show that our DUP method consistently outperforms the other counterparts by a large margin. More encouragingly, DUP achieves a new SOTA result on the GSM8K benchmark, with an accuracy of 97.1% under the zero-shot setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics