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LBFL: A Lightweight Blockchain-based Federated Learning Framework with Proof-of-Contribution Committee Consensus

IEEE Transactions on Big Data 2024年

作者： Qiao, Shaojie Jiang, Yuhe Han, Nan Hua, Wei Lin, Yufeng Min, Shengjie Wu, Xindong Chengdu University of Information Technology School of Software Engineering Chengdu610225 China Sichuan University NMPA Key Laboratory of Human Cosmetic Evaluation and Big Data West China Hospital Chengdu610041 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230009 China

Blockchain technology makes it possible to design robust decentralized federated learning (FL). Minimizing the communication cost and storage consumption incurred is one of the essential challenges. In addition, maintaining the security and privacy of Big Data raises to be a difficult problem. Aiming to tackle these challenges, this paper presents LBFL (a Lightweight Blockchain-based FL framework) that offers three novel features. First, it employs a new committee consensus mechanism called Proof-of-Contribution, which is used to avoid the selection latency from the competition of miners and alleviate the congestion in cross-validation of parameters in an asynchronous fashion. Second, LBFL employs a role-adaptive incentive mechanism to estimate devices' workloads and identify malicious nodes effectively. Third, to cope with the excessive storage overheads incurred in full-replication, LBFL applies a new storage partition mechanism that distributes triple redundant chunks in ReedSolomon coding (RSC) evenly to participating devices with high fault tolerance and recovery efficiency. To evaluate LBFL, empirical studies are performed on the famous MNIST dataset and LBFL is compared with the state-of-the-art FL frameworks. The results demonstrate that LBFL can reduce evaluation latency and storage consumption by 69.2% and 72.1%, respectively, and the learning efficiency of LBFL is higher than the state-ofthe-art methods. In particular, important findings are obtained: the proposed role-adaptive incentive mechanism can properly identify malicious devices and switch the roles of legitimate devices to achieve good decentralization © 2015 IEEE.

关键词： Federated learning

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Testing Non-Commutativity of Reduce Functions with Multi-Column Inputs

SSRN

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SSRN 2024年

作者： Zhang, Xuan Zhu, Chenlu Li, Ning Zhang, Peng Liu, Lei College of Software Jilin University Jilin China College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Jilin China

With the continuous development of the MapReduce programming model, it is necessary to ensure the reliability of MapReduce programs. In practice, the non-commutativity of Reduce functions seriously affects the reliability of the MapReduce program, which is difficult to debug and even causes errors. Current researches on the non-commutability detection of Reduce function consider the case that the input value is a single attribute. However, such researches ignore the situation where inputs to most reduce functions in practical applications consist of multiple columns (such as a table). To test the commutativity of reduce functions where each input record may contain several input attributes, a new testing method is proposed. This approach uses symbolic execution tools to help generate a few input records, and breaks their data dependencies to generate an original test case t0, with a dynamic program slicing technique to lessen the scale of t0. And the ultimate test suite is consisted of different permutations of records in t0. In the end, experiments demonstrate the effectiveness of our testing method and that the permutation method Gm is helpful to reduce its complexity. © 2024, The Authors. All rights reserved.

关键词： MapReduce

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Improving Local Search for Pseudo Boolean Optimization by Fragile Scoring Function and Deep Optimization 29

Improving Local Search for Pseudo Boolean Optimization by Fr...

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29th International Conference on Principles and Practice of Constraint Programming, CP 2023

作者： Zhou, Wenbo Zhao, Yujiao Wang, Yiyuan Cai, Shaowei Wang, Shimao Wang, Xinyu Yin, Minghao School of Information Science and Technology Northeast Normal University Changchun China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University Changchun China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959773003

Pseudo-Boolean optimization (PBO) is usually used to model combinatorial optimization problems, especially for some real-world applications. Despite its significant importance in both theory and applications, there are few works on using local search to solve PBO. This paper develops a novel local search framework for PBO, which has three main ideas. First, we design a two-level selection strategy to evaluate all candidate variables. Second, we propose a novel deep optimization strategy to disturb some search spaces. Third, a sampling flipping method is applied to help the algorithm jump out of local optimum. Experimental results show that the proposed algorithms outperform three state-of-the-art PBO algorithms on most instances. © Wenbo Zhou, Yujiao Zhao, Yiyuan Wang, Shaowei Cai, Shimao Wang, Xinyu Wang, and Minghao Yin.

关键词： Combinatorial optimization

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Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

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Chinese Journal of Electronics 2020年第6期29卷 1153-1164页

作者： XU Qianyi QIN Guihe SUN Minghui YAN Jie JIANG Huiming ZHANG Zhonghan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering Ministry of Education Jilin University College of Software Jilin University

Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional ***,video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture *** that if the displays are more relevant,the results will more accurate,so ResNext,a kind of compact and efficient neural network,is firstly used as feature extractor,then an improved weighted frame unification method is adopted to obtain the key frame samples,finally the Discriminant correlation analysis(DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch(FEB) on static *** public dataset named Depth based gesture recognition database(DGRD) is used in this paper,but the dataset is a little small and the class distribution is largely imbalance,and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training *** order to conquer the disadvantages of limited dataset,a special loss function scheme combining the soft max loss and dice loss is *** of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.

关键词： feature extraction gesture recognition learning (artificial intelligence) neural nets Depth based gesture recognition database Feature embedding branch dynamic data static data key frame samples improved weighted frame unification method feature extractor efficient neural network compact network uncorrelated gesture factors video gestures recognition depth videos automotive interfaces Feature fusion based hand gesture recognition method Weighted frame unification Discriminant correlation analysis (DCA) Feature embedding branch (FEB) Gesture recognition Loss function

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Computational Methods for Inferring and Reconstructing Gene Regulatory Networks

Computational Methods for Inferring and Reconstructing Gene ...

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作者： Xianglu Zhou Haiyang Jia College of Computer Science and Technology Jilin University College of Software Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin University

Gene regulatory network(GRN) describes dynamic and complex gene interactions that determine the functions of ***,understanding of GRNs has played a great role in cancer treatment,drug design,and gene ***,the GRN study suffers from a lack of experiment measurement data and the complexity of the *** approaches have evolved in recent years to promote the study of *** this review,we summarized popular Computational approaches for GRN *** started with traditional bioinformatic methods,such as Bayesian networks and mutual information ***,we introduced how today's hot technology in the computer field-machine learning benefited GRN ***-based approaches and other machine-learning methods are elaborated on in this *** discussed not only the advantages and progression brought by various methods but also the drawbacks and limitations,such as the accuracy and robustness of GRN *** expect to inspire readers for improved GRN study approaches via our introduction to this field.

关键词： Gene Regulatory Network Bioinformatics Machine Learning

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LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

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Robust Federated Semi-Supervised Learning for Medical Image Classification via Pseudo-Label Filtering

Robust Federated Semi-Supervised Learning for Medical Image ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziwei Wang Shuyu Guo Mingzhu Zhu Tian Bai College of Software Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Federated learning (FL) enables collaborative model training across multiple medical institutions to ensure data security. However, due to the variations in medical imaging equipment and regions at different medical institutions, FL methods usually suffer from insufficient data annotations and irrelevant noise within private datasets. To address these issues, a robust federated semi-supervised learning method via pseudo-label filtering (PFRFed) is introduced to utilize unlabeled data while mitigating the impact of noise data. Compared with existing federated semi-supervised learning methods, we propose a pseudo-label filtering mechanism with double dynamic thresholds, which allows the model to adopt more unlabeled data by adjusting the confidence and entropy thresholds at each stage of model training. Moreover, to reduce the degradation caused by noise data in private datasets from different clients, a noise-tolerant loss function and a grouping aggregation method based on the local model similarity are employed. The comparative experiments demonstrate the effectiveness of PFRFed, which has achieved the best classification accuracy of 95.20% and 88.72% on two public medical datasets. Also, PFRFed exhibits heightened resilience to variations in noisy data ratio and labeled data ratio, reaffirming its versatility and robustness.

关键词： Training Filtering Biological system modeling Noise Semisupervised learning Data models Robustness Noise measurement Biomedical imaging Image classification

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A Novel Drug-Drug Interaction Prediction Model Based on Line Subgraph Generation Strategy

A Novel Drug-Drug Interaction Prediction Model Based on Line...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Bai, Tian Li, Chu Peng, Xinyue Guan, Haotian Zhang, Zefan Wang, Guishen Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China Jilin University School of Nursing Changchun China Jilin University School of Life Sciences Changchun China Changchun University of Technology College of Computer Science and Engineering Changchun China

ISBN: (纸本)9798350337488

Drug-Drug Interaction (DDI) prediction task is helpful for better-understanding drugs. In this paper, we propose a novel drug-drug interaction prediction model based on line subgraph generation strategy, named DDI-LSG model. Our DDI-LSG model consists of three main parts which include drug relation graph construction, line subgraph generation strategy, and graph-level classification. To consider more relationships among drugs, we propose a node feature-enhancing method to encode drug features in drug relation graph construction process. To consider drugs and DDI as equivalent factors of our DDI-LSG model, we introduce line graph transformation to integrate DDI with drug feature enhancing vector. Combining Jaccard similarity with cosine similarity, we propose a line subgraph generation strategy to evaluate node relation and extract key structures around target DDI in the line graph. Then, we reformulate the DDI prediction task into a graph-level classification task for the line subgraph of the target DDI. Therefore, in the final part of our DDI-LSG model, we use a graph-level classifier to classify the line subgraphs. Our DDI-LSG model outperforms better experiment results than baselines. Ablation results have validated the node feature enhancing method and line subgraph generation strategy. © 2023 IEEE.

关键词： drug-drug interaction prediction graph-level classification line graph line subgraph strategy node feature enhancing method

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Medical Scene Graphs and Reasoning

Medical Scene Graphs and Reasoning

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Cao, Chuxue He, Yiming Chen, Yuzhen Song, Chunli Ling, Hao Guan, Renchu Feng, Xiaoyue Jilin University The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Software Changchun China The Second Hospital of Jilin University Changchun China Jilin University The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665401265

Medical scene graph contributes to cognitive tasks such as question answering. An automatic medical scene graph generator can annotate medical images with scene graphs conveniently. An end-to-end model is proposed, which contains an automatic medical scene graph generator based on medical images and surgery records. The novel model can extract symptom information of the images from surgery records via name entity recognition then convert it into scene graphs. The content and format of the scene graph are pre-defined according to the features of the images. A medical scene graph (MSG) dataset is also produced. The dataset contains 116 scene graphs where each graph includes 6 to 30 entities. Experiments show that our model performs well on making scene graphs of CT coronary angiography. Significantly, the scene graphs have the type, state, location, and index of arteries. To verify the practicability of the MSG dataset, We construct an intelligent model that can generate key diagnostic results. In addition, our medical scene graph generator can be applied to other medical areas to annotate different kinds of medical images intelligently. And the MSG dataset can directly be used in other cognitive visual tasks. © 2021 IEEE.

关键词： Graphic methods

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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