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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Serhii Reznichenko Shijie Zhou Department of Computer Science and Software Engineering College of Engineering and Computing Miami University Oxford OH USA Department of Chemical Paper and Biomedical Engineering and Department of Computer Science and Software Engineering College of Engineering and Computing Miami University Oxford OH USA

The 12-lead ECG only has 8 independent ECG leads, which leads to diagnostic redundancy when using all 12 leads for heart arrhythmias classification. We have previously developed a deep learning (DL)-based computer-interpreted ECG (CIE) approach to identify an optimal 4-lead ECG subset for classifying heart arrhythmias. However, the clinical diagnostic criteria of cardiac arrhythmia types are often lead-specific, so this study is going to explore the selection of arrhythmia-based ECG-lead subsets rather than one general optimal ECG-lead subset, which could improve the classification performance for the CIE. The DL-based CIE model previously developed was used to learn 4 common types of heart arrhythmias (LBBB, RBBB, AF, and I-AVB) for identifying corresponding optimal ECG-lead subsets. A public dataset that splits into training (approx. 70%), validation (approx. 15%), and test (approx. 15%) sets from the PhysioNet Cardiology Challenge 2020 was used to explore the study. The results demonstrated that the DL-based CIE model identified an optimal ECG-lead subset for each arrhythmia: I, II, aVR, aVL, V1, V3, and V5 for I-AVB; I, II, aVR, and V3 for AF; I, II, aVR, aVF, V1, V3, and V4 for LBBB; and I, II, III, aVR, V1, V4, and V6 for RBBB. For each arrhythmia classification, the DL-based CIE model using the optimal ECG-lead subset significantly outperformed the model using the full 12-lead ECG set on the validation set and on the external test *** results support the hypothesis that using an optimal ECG-lead subset instead of the full 12-lead ECG set can improve the classification performance of a specific arrhythmia when using the DL-based CIE *** Relevance— Using an arrhythmia-based optimal ECG-lead subset, the classification performance of a deep-learning-based model can be achieved without loss of accuracy in comparison with the full 12-lead set (p<0.05).

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Subgraph Sampling for Inductive Sparse Cloud Services QoS Prediction

Subgraph Sampling for Inductive Sparse Cloud Services QoS Pr...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jianlong Xu Zhiyu Xia Yuhui Li Yuxiang Zeng Zhidan Liu College of Engineering Shantou University Shantou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Quality-of-Service (QoS) based collaborative prediction models are emerging to select appropriate edge cloud services for users. Nevertheless, there are still challenges in the realworld QoS prediction task. First, existing QoS prediction models are mostly transductive, failing to generalize to unseen users and services. Secondly, an accurate prediction model remains unexplored under the extreme sparse data scenario, where only a few interactions are available for collaborative filtering. To address these problems, we propose -Inductive -Subgraph -Pattern -Aware Graph Neural Network (ISPA-GNN), which leverages a novel graph-based collaborative filtering method with a subgraph sampling strategy. We further optimize the embeddings components, replacing the user/service embeddings with compositional context information to enable better generalization to unseen nodes while reducing memory usage. Extensive experiments on a large-scale real-world service QoS dataset demonstrate some decent properties of our model, including high prediction accuracy, memory efficiency, and slight performance degradation even if 25% of users/services are never seen.

关键词： Fluctuations Collaborative filtering Time series analysis Memory management Collaboration Quality of service Predictive models

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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M-DFCPP: A runtime library for multi-machine dataflow computing

M-DFCPP: A runtime library for multi-machine dataflow comput...

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作者： Luo, Qiuming Liu, Senhong Huang, Jinke Li, Jinrong College of Computer Science and Software Engineering Shenzhen University Shenzhen China SKT Group Guangdong Province Key Laboratory of Popular High-Performance Computers Shenzhen China SKT Group Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen China

This article designs and implements a runtime library for general dataflow programming, DFCPP (Luo Q, Huang J, Li J, Du Z. Proceedings of the 52nd International Conference on Parallel Processing Workshops. ACM;2023:145-152.), and builds upon it to design and implement a multi-machine C++ dataflow library, M-DFCPP. In comparison to existing dataflow programming environments, DFCPP features a user-friendly interface and richer expressive capabilities (Luo Q, Huang J, Li J, Du Z. Proceedings of the 52nd International Conference on Parallel Processing Workshops. ACM;2023:145-152.), enabling the representation of various types of dataflow actor tasks (static, dynamic and conditional task). Besides that, DFCPP addresses the memory management and task scheduling for non-uniform memory access architectures, while other dataflow libraries lack attention to these issues. M-DFCPP extends the capability of current dataflow runtime libraries (DFCPP, taskflow, openstream, etc.) and capable of multi-machine computing, while maintains the API compatible with DFCPP. M-DFCPP adopts the concepts of master and follower (Dean J, Ghemawat S. Commun ACM. 2008;51(1):107-113;Ghemawat S, Gobioff H, Leung ST. ACM SIGOPS Operating Systems Review. ACM;2003:29-43.), which form a worksharing framework as many multi-machine system. To shift to the M-DFCPP framework, a filtering layer is inserted to the original DFCPP, transforming it into followers that can cooperate with each other. The master is made of modules for scheduling, data processing, graph partition, state management and so forth. In benchmark tests with workload with directed acyclic graph topology of binary trees and random graphs, DFCPP demonstrated performance improvements of 20% and 8%, respectively, compared to the second fastest library. M-DFCPP consistently exhibits outstanding performance across varying levels of concurrency and task workloads, achieving a maximum speedup of more than 20 over DFCPP, when the task parallelism e

关键词： Libraries

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A Hybrid Data-flow Visual Programing Language* 22

A Hybrid Data-flow Visual Programing Language*

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Workshop Proceedings of the 51st International Conference on Parallel Processing

作者： Hongxin Wang Qiuming Luo Zheng Du College of Computer Science and Software Engineering Shenzhen University China Tsinghua University China

ISBN: (纸本)9781450394451

In this paper, we introduced a Hybrid Data-flow Visual Programing Language (HDVPL), which is an extended C/C++ language with a visual frontend and a dataflow runtime library. Although, most of the popular dataflow visual programming languages are designed for specialized purposes, HDVPL is for general-purpose programming. Unlike the others, the dataflow node behavior of HDVPL can be customized by programmer. Our intuitive visual interface can easily build a general-purpose dataflow program. It provides a visual editor to create nodes and connect them to form a DAG of dataflow task. This makes the beginner of computer programming capable of building parallel programs easily. With subgraph feature, complex hierarchical graphs can be built with container node. After the whole program is accomplished, the HDVPL can translate it into text-based source code and compile it into object file, which will be linked with HDVPL dataflow runtime library. To visualize dataflow programs in runtime, we integrated our dataflow library with frontend visual editor. The visual frontend will show the detailed information about the running program in console window.

关键词： User interface Visual Programming Languages (VPLs) Flow-based Programming

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FedPartial: Enabling Model-Heterogeneous Federated Learning via Partial Model Transmission and Aggregation

FedPartial: Enabling Model-Heterogeneous Federated Learning ...

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IEEE International Conference on Web Services (ICWS)

作者： Changkun Jiang Jiahao Chen Lin Gao Jianqiang Li College of Computer Science and Software Engineering Shenzhen University China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Federated learning (FL) is emerging as a new privacy-preserving learning paradigm that allows multiple devices to collaborate in training a model without sharing their raw data, using a central server for coordination. However, device heterogeneity poses a challenge in FL, as participating devices often have different computing capacities. To address this issue, heterogeneous models need to be designed to accommodate different device computing capacities. The existing approach involves pre-designing multiple heterogeneous models and extracting sub-models from the server model. While such an approach effectively tackles device heterogeneity, it has several drawbacks such as high communication overhead and insufficient personalization. In each training round, the server distributes the entire model parameters to each device, and each device also transmits the entire model parameters to the server for aggregation. In this work, we propose FedPartial, a new framework that overcomes these challenges by introducing a partial model transmission and aggregation mechanism. The FedPartial framework eliminates the need for devices to transmit the entire model parameters in each training round while still benefiting from global model aggregation. Specifically, FedPartial divides the device model into two parts: the shallow part participates in the global aggregation of heterogeneous models, while the deep part remains on the device locally. By keeping the deep part of the model on the device, FedPartial reduces the communication overhead significantly and achieves a certain degree of personalization. Through extensive experiments, we demonstrate that FedPartial outperforms existing state-of-the-art methods, particularly in more complex and statistically heterogeneous scenarios.

关键词： Training Federated learning Web services Computational modeling Data models Servers

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A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making

A Complex Belief Jensen-Shannon Divergence in Complex Eviden...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Gao, Linlu Yin, Mingrui Xiao, Fuyuan Cao, Zehong The School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Mawson Lakes Campus University of South Australia Stem Adelaide Australia

ISBN: (纸本)9798350316308

Effective target detection and recognition are essential in remote sensing image field, and complex evidence theory (CET) is widely used for this purpose. However, measuring conflict between complex basic belief assignments (CBBAs) in CET is challenging. This study proposes a complex belief Jensen-Shannon divergence based on the complex Pignistic transformation to measure conflict, accounting for quantum interference effects in CBBAs. We analyze the properties of the CBJS divergence, including boundedness, nondegeneracy, and symmetry. Numerical examples are presented to show the effectiveness of the conflict measure method and the potential of improving the robustness of intelligent interpretation system. Moreover, an algorithm for decision-making is presented and applied in pattern recognition illustrating its superiority. © 2023 IEEE.

关键词： Decision making

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Human-Cyber-Physical Automata and Their Synthesis 1

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19th International Colloquium on Theoretical Aspects of Computing, ICTAC 2022

作者： Zhang, Miaomiao Liu, Wanwei Tang, Xiaochen Du, Bowen Liu, Zhiming School of Software Engineering Tongji University Shanghai China College of Computer Science National University of Defense Technology Changsha China Department of Computer Science University of Warwick Coventry United Kingdom School of Computer and Information Science Southwest University Chongqing China School of Software Northwest Polytechnical University Xi’an China

A cyber-physical system (CPS) is now well understood as an integration of hierarchical composition digital computing (or information processing) systems and physical systems [3, 10, 14]. © 2022, The Author(s), un... 详细信息

ISBN: (数字)9783031177156

ISBN: (纸本)9783031177149

关键词： Cyber Physical System

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Ev-IDID: Enhancing Solutions to Interactive Dynamic Influence Diagrams through Evolutionary Algorithms 21

Ev-IDID: Enhancing Solutions to Interactive Dynamic Influenc...

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21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022

作者： Ma, Biyang Pan, Yinghui Zeng, Yifeng Ming, Zhong School of Computer Science and Engineering Minnan Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781713854333

Interactive dynamic influence diagrams (I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system that implements most of the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Decision making

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MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks

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computer Modeling in engineering & sciences 2021年第8期128卷 427-445页

作者： Juhong Tie Hui Peng Jiliu Zhou College of Computer Science Sichuan UniversityChengdu610065China School of Software Engineering Chengdu University of Information TechnologyChengdu610225China School of Computer Science Chengdu University of Information TechnologyChengdu610225China

The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder *** used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method.

关键词： MRI brain tumor segmentation U-Net dense block residual block

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