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Three-Dimensional Tradeoffs for Consensus Algorithms: A Review

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第2期19卷 1216-1228页

作者： Li, Linhui Shi, Peichang Fu, Xiang Chen, Peng Zhong, Tao Kong, Jinzhu Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci Key Lab Software Engn Complex Syst Changsha 410073 Peoples R China Zhejiang Lab Hangzhou 311100 Peoples R China Kylin Software Co Ltd Changsha 410073 Peoples R China

Blockchain has been applied in many fields to solve the problems of trust, security, efficiency benefiting from its tamper-proof and traceability of data. However, it is still necessary to consider the technical constraints that limit the large-scale application of blockchain: scalability, security, and decentralization cannot be achieved altogether. Consensus algorithm is the core of blockchain, which determines the performance of blockchain system to a certain extent. The existing reviews or surveys mainly focus on processes of consensus algorithms, but fall short in covering the current trends and scenarios, thereby lacking intrinsic understanding of their design philosophy. In this paper, we propose a multi-dimensional tradeoff model and unearth various indicators of different dimensions to guide the construction of consensus algorithms. To summarize the existing efforts, we compare and analyze various classical consensus algorithms, and focus on the design principles of these algorithms under the multi-dimensional tradeoff model. According to different requirements, each algorithm has different tradeoffs. Furthermore, we provide different solutions for blockchain in different dimensions. Finally, we summarize the development trend of consensus design and the key technology prospects of blockchain. This is, to the best of our knowledge, the first survey that accomplishes such goals.

关键词： Blockchains Security Consensus algorithm Scalability Throughput Bitcoin Technological innovation Scalability decentralization security consensus

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NEDORT: a novel and efficient approach to the data overlap problem in relational triples

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COMPLEX & INTELLIGENT SYSTEMS 2023年第5期9卷 5235-5250页

作者： Zhang, Zhanjun Hu, Xiaoru Zhang, Haoyu Liu, Jie Natl Univ Def Technol Coll Comp Changsha Peoples R China Cent South Univ Xiangya Hosp 2 Dept Nephrol Hunan Key Lab Kidney Dis & Blood Purificat Changsha Peoples R China Def Innovat Inst Beijing Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha Peoples R China

Relation triple extraction is a combination of named entity recognition and relation prediction. Early works ignore the problem of data overlap when extracting triples, resulting in poor extraction performance. Subsequent works improve the capability of the model to extract overlapping triples through generative and extractive methods. These works achieve considerable performance but still suffer from some defects, such as poor extraction capability for individual triplets and inappropriate spatial distribution of the data. To solve the above problems, we perform sequence-to-matrix transformation and propose the NEDORT model. NEDORT predicts all subjects in the sentence and then completes the extraction of relation-object pairs. There are overlapping parts between relation-object pairs, so we conduct the conversion of sequence to matrix. We design the Differential Amplified Multi-head Attention method to extract subjects. This method highlights the locations of entities and captures sequence features from multiple dimensions. When performing the extraction of relation-object pairs, we fuse subject and sequence information through the Biaffine method and generate relation-sequence matrices. In addition, we design a multi-layer U-Net network to optimize the matrix representation and improve the extraction performance of the model. Experimental results on two public datasets show that our model outperforms other baseline models on triples of all categories

关键词： Relation triple extraction Overlapping data Differential amplified Multi-head attention Biaffine U-Net network

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Deep Learning for the Automatic Diagnosis and Analysis of Bone Metastasis on Bone Scintigrams

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CANCER MANAGEMENT AND RESEARCH 2022年 14卷 51-65页

作者： Liu, Sinnin Feng, Ming Qiao, Tingting Cai, Haidong Xu, Kele Yu, Xiaqing Jiang, Wen Lv, Zhongwei Wang, Yin Li, Dan Tongji Univ Shanghai Peoples Hosp 10 Dept Nucl Med Sch Med Shanghai Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

Objective: To develop an approach for automatically analyzing bone metastases (BMs) on bone scintigrams based on deep learning technology. Methods: This research included a bone scan classification model, a regional segmentation model, an assessment model for tumor burden and a diagnostic report generation model. Two hundred eighty patients with BMs and 341 patients with non-BMs were involved. Eighty percent of cases were randomly extracted from two groups as training set. Remaining cases were as testing set. A deep residual convolutional neural network with different structures was used to determine whether metastatic bone lesions existed, regions of lesions were automatically segmented. Bone scan tumor burden index (BSTBI) was calculated;finally, diagnostic report could be automatically generated. The sensitivity, specificity and accuracy of classification model were compared with three physicians with different clinical experience. The Dice coefficient evaluated the effect of segmentation model and compared to the result of nnU-Net model. The correlation between BSTBI and blood alkaline phosphatase (ALP) level was analyzed to verify the efficiency of BSTBI. The performance of report generation model was evaluated by the accuracy of interpretation of report. Results: In testing set, the sensitivity, specificity and accuracy of classification model were 92.59%, 85.51% and 88.62%, respectively. The accuracy showed no statistical difference with moderately and experienced physicians and obviously outperformed the inexperienced. The Dice coefficient of BMs area was 0.7387 in segmentation stage. Based on the whole model frame, our segmentation model outperformed the nnU-Net. BSTBI value changed as the BMs changed. There was a positive correlation between BSTBI and ALP level. The accuracy of report generation model was 78.05%. Conclusion: Deep learning based on automatic analysis frameworks for BMs can accurately identify BMs, preliminarily realize a fully automatic ana

关键词： bone metastases bone scintigraphy deep learning tumor burden automatic report generation

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A Verifiable and Fair Attribute-Based Proxy Re-Encryption Scheme for Data Sharing in Clouds

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2022年第5期19卷 2907-2919页

作者： Ge, Chunpeng Susilo, Willy Baek, Joonsang Liu, Zhe Xia, Jinyue Fang, Liming Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211100 Peoples R China State Key Lab Cryptol Beijing 100878 Peoples R China Sci & Technol Parallel & Distributed Proc Lab Changsha 410000 Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW 2522 Australia IBM Corp Durham NC 27709 USA

To manage outsourced encrypted data sharing in clouds, attribute-based proxy re-encryption (ABPRE) has become an elegant primitive. In ABPRE, a cloud server can transform an original recipient's ciphertext to a new one of a shared user's. As the transformation is computation consuming, a malicious cloud server may return an incorrect re-encrypted ciphertext to save its computation resources. Moreover, a shared user may accuse the cloud server of returning an incorrect re-encrypted ciphertext to refuse to pay the cost of using the cloud service. However, existing ABPRE schemes do not support a mechanism to achieve verifiability and fairness. In this article, a novel verifiable and fair attribute-based proxy re-encryption (VF-ABPRE) scheme is introduced to support verifiability and fairness. The verifiability enables a shared user to verify whether the re-encrypted ciphertext returned by the server is correct and the fairness ensures a cloud server escape from malicious accusation if it has indeed conducted the re-encryption operation honestly. Additionally, we conduct a performance experiment to show the efficiency and practicality of the new VF-ABPRE scheme.

关键词： Servers Cryptography Cloud computing Genomics Bioinformatics Systems architecture Neurology Attribute-based proxy re-encryption data sharing cloud computing verifiability fairness

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Improved Partitioning Graph Embedding Framework for Small Cluster 14th

Improved Partitioning Graph Embedding Framework for Small Cl...

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14th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Sun, Ding Huang, Zhen Li, Dongsheng Ye, Xiangyu Wang, Yilin Natl Univ Def Technol Changsha 410073 Hunan Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9783030821364;9783030821357

Graph embedding is a crucial method to produce node features that can be used for various machine learning tasks. Because of the large number of embedded parameters in large graphs, a single machine cannot load the entire graph into GPUs at once, so a partitioning strategy is required. However, there are some problems with partitioning strategies. Firstly, partitioning introduces data I/O and processing overhead, which increases training time, especially on the cluster with a small number of sites. Secondly, partitioning can affect the performance of the model. For multi-relation graphs, this effect is often negative. To address these problems, we propose the training pipeline and random partitions recombination methods. The training pipeline can reduce the time overhead by masking data loading time to GPU computation, and partitions recombination can effectively improve multi-relation model performance. We conducted experiments on multi-relation graphs and social networks, and the results show that both methods are effective.

关键词： Graph embedding Knowledge graphs Partitioning graph framework GPU distributed graph algorithms

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Unsupervised Learning-Based Depth Estimation-Aided Visual SLAM Approach

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CIRCUITS SYSTEMS AND SIGNAL procESSING 2020年第2期39卷 543-570页

作者： Geng, Mingyang Shang, Suning Ding, Bo Wang, Huaimin Zhang, Pengfei Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

Simultaneous localization and map construction (SLAM) tasks have been proven to benefit greatly from the depth information of the environment. In this paper, we first present an unsupervised end-to-end learning framework for the task of monocular depth and camera motion estimation from video sequences. The difference between our work and the existing unsupervised methods is that we not only use image reconstruction for supervising but also exploit the pose estimation method used in traditional SLAM approaches to enhance the supervised signal and add extra training constraints for the task of monocular depth and camera motion estimation. Furthermore, we successfully exploit our unsupervised learning framework to assist the traditional ORB-SLAM system when the initialization module of ORB-SLAM method could not match enough features. Qualitative and quantitative experiments have shown that our unsupervised learning framework performs the depth estimation task superior to the supervised methods and outperforms the previous state-of-the-art unsupervised approach by 13.5% on KITTI dataset. For the pose estimation task, our method performs comparably to the supervised methods that use ground-truth pose data for training. Besides, our unsupervised learning framework can significantly accelerate the initialization process of the traditional ORB-SLAM system and effectively improve the accuracy of environmental mapping in strong lighting and weak texture scenes.

关键词： Monocular depth estimation Pose estimation Unsupervised learning Visual SLAM system

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Rumor Verification on Social Media with Stance-Aware Recursive Tree 14th

Rumor Verification on Social Media with Stance-Aware Recursi...

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14th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Han, Xiaoyun Huang, Zhen Lu, Menglong Li, Dongsheng Qiu, Jinyan Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China HR Support Ctr Beijing Peoples R China

ISBN: (纸本)9783030821531;9783030821524

Since rumors have affected real society harmfully, automatic rumor verification attracts much attention from researchers. Incorporating the stance-aware knowledge into rumor verification is a hot direction, because its great potential to boost verification performance has been revealed in many studies. However, existing methods are still limited by two problems, the existence of short retweets and fraud nodes. For short retweets, since it is hard to extract the semantic information from short retweets, modeling the stance between a tweet and its short retweets could carry out training noises. For fraud nodes, they might perturb the normal propagation structure of rumors, so the model could be misled to capture those wrong stance information. To mitigate them, we propose a Credibility and Stance Aware recursive Tree (CSATree) for rumor verification. Firstly, we utilize a self-attention mechanism and a multi-task learning module to explore the context of short retweets, which could help to enrich the semantics and stance information in short retweets. In detail, the context of short retweets refers to those retweets that respond to a same tweet, i.e., sibling nodes in a conversation tree. Secondly, we take the node credibility into account and adopt another novel attention mechanism to reduce the impact of fraud nodes. Experiments on two public datasets demonstrate that CSATree significantly outperforms the current best stance-aware model by around 9%.

关键词： Rumor verification Stance-aware knowledge Recursive tree Attention mechanism Node credibility

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Domain adaptation towards speaker-independent ultrasound tongue imaging based articulatory-to-acoustic conversion

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JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2023年第3期153卷 A366-A366页

作者： You, Kang Xu, Kele Wang, Jilong Feng, Ming Tongji Univ Shanghai Peoples R China Natl Key Lab Parallel & Distributed Proc PDL 107 Yanwachi Changsha 410073 Peoples R China

In this paper, we endeavor to address an articulatory-to-acoustic issue which aims to estimate the mel-spectrogram of the acoustical signals, using midsagittal ultrasound tongue images of the vocal tract as input. Previous attempts employed statistical methods for the inversion between the articulatory movements and speech, while deep learning has begun to dominate this field. Despite the sustainable efforts that have been made, the mapping performance can be greatly varied for different speakers and most of the previous methods are constrained for the speaker-dependent scenario. Here, we present a novel approach towards speaker-independent mapping, which is inspired by the domain adaptation method. Specifically, we explore decoupling the spectrogram generation task and the speaker recognition task. Leveraging a novel designed loss function, we can improve the performance under the speaker-independent scenarios, through the adversarial learning strategy. To demonstrate the effectiveness of the proposed method, extensive experiments are conducted on the Tongue and Lips (TaL) corpus. Objective evaluation is conducted to compare the generated spectrograms and ground truth, using three evaluation metrics, including the MSE, SSIM, and CW-SSIM. The results indicate that our proposed method can achieve superior performance under the speaker-independent scenario, compared with competitive solutions. Our code is available at https://***/xianyi11/Articulatory-to-Acoustic-with-Domain-Adaptation.

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Cooperative Localization Using Expected Minimum Segment for Irregular Multi-Hop Networks

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IEEE TRANSACTIONS ON NETWORKING 2025年第1期33卷 369-381页

作者： Yan, Xiaoyong Cao, Jiannong Zhang, Shigeng Ding, Chuntao Wu, Chenhuang Liu, Alex X. Song, Aiguo Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing 210003 Peoples R China Putian Univ Key Lab Appl Math Putian 351200 Fujian Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Sci & Technol Parallel & Distributed Proc Lab PDL Changsha 410003 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Putian Univ Key Lab Appl Math Putian 351200 Fujian Peoples R China Midea Grp Foshan 528311 Guangdong Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 211102 Peoples R China

For the creation of wireless network applications, node locations are frequently necessary. However, communication effectiveness, measurement accuracy, and localization stability will be low in irregular multi-hop networks when locating nodes using conventional algorithms. To this end, a novel cooperative localization algorithm using expected minimum segments (LEMS, for short) is proposed in this paper. LEMS begins by measuring the distance between paired nodes, which is completed along with network initialization. Then, each unlocated node constructs its own sub-network, including it, based on the error characteristics among anchor nodes. Finally, each unlocated node searches for its estimated location in its sub-region based on the objective function generated by the chaotic mapping. Simulation results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art regarding efficiency, accuracy, and stability for various irregular networks. Specifically, our proposed algorithm achieves a median improvement in localization accuracy of 0.62 to 29.57 times and a reduction in the range of localization errors of 0.06 to 16.8 times.

关键词： Location awareness Accuracy Routing Spread spectrum communication Measurement uncertainty Linear programming Global navigation satellite system Geometry Estimation Particle swarm optimization Cooperative localization expected minimum segment error analysis chaotic mapping

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Focus on Hard Categories and Hard Examples: Remote Sensing Image Scene Classification via Expert Model and Hard Example Mining

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Xiong, Yunsheng Zhang, Peng Dou, Yong Xu, Kele Niu, Xin Natl Univ Def Technol Dept Comp Changsha 410005 Peoples R China Key Lab Parallel & Distributed Proc Changsha 410005 Peoples R China

Deep learning has seen dramatic improvements in remote-sensing image scene classification. However, hard categories and hard examples widely exist in the data sets, due to the intraclass diversity and interclass similarity. In this letter, we propose a novel framework to address these issues. Specifically, our method first trains a general model to obtain the confusion matrix and select the hard categories. Then a sampling strategy is proposed to restructure the training set and an expert model is trained to focus on the hard categories. Finally, the knowledge of the expert model is distilled into the student model through a novel loss function, which encourages the student model to predict the hard label provided by manual annotation. Thus, the model can match the soft label provided by the expert model and pay more attention to the hard examples simultaneously. With this method, the student model cannot only deal with hard categories but also hard examples existing in other easy categories. To empirically demonstrate the effectiveness of the proposed method, we comprehensively evaluate the method on three publicly available benchmark data sets, the obtained results show that the proposed method outperforms the existing baseline methods and achieves superior results on all three data sets.

关键词： Training Remote sensing Predictive models Data models Loss measurement Task analysis Feature extraction Expert model hard categories hard examples knowledge distillation (KD) remote sensing scene classification

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