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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Gen Shi Yifan Zhu Fuquan Zhang Wenjin Liu Yuxiang Yao Xuesong Li School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China College of Computer and Control Engineering Minjiang University Fuzhou China Department of Nephrology Northern Jiangsu People’s Hospital Yangzhou University Yangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Neuroimaging plays an significant role in diagnosing and pathological study of brain diseases. Considering that both functional and structural abnormalities may lead to brain dis-eases and disorders, single modal neuroimaging approach may not fully characterize brain activities and working modes. Fusion of multimodal neuroimaging data is expected to provide more comprehensive characterization of brain diseases, given that the different modalities contain more complementary information. Recently, Graph Convolutional Networks (GCNs) is shown to have powerful capacity in representation learning for graph-structure data, which is considered to integrate both graph se-mantic structure and node information. Therefore, in this paper, we propose the Weighted Graph AutoEncoder (WGAE), a GCN- driven multimodal fusion model, to learn the combinational latent node representation of fMRI and DTI neuroimaging data, which are used as node features and graph structure respectively in the graph in unsupervised manner. Experimental results on two real-world datasets show the superiority of the proposed model over other existing single-modal or multi-modal methods in learning representations for disease prediction as the downstream task. Furthermore, ablation experiments also show the collaborative contribution of multimodal neuroimaging fusion in the proposed model, and also show the feasibility of assessing the respective importance of the two modalities during the disease prediction.

关键词： Neuroimaging Representation learning Pathology Predictive models Functional magnetic resonance imaging Brain modeling Data models

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FDP-GAMLP: New Financial Distress Prediction Technique based on Genetic Algorithm and Multi-Layer Perceptron 19

FDP-GAMLP: New Financial Distress Prediction Technique based...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Khedr, Ahmed M. Bannany, Magdi El Saif, Abdul-Wahid A. University of Sharjah Computer Science Dept. Sharjah27272 United Arab Emirates College of Business Administration Umm Al Quwain University Umm Al Quwain356 United Arab Emirates Ain Shams University Faculty of Business Cairo11566 Egypt King Fahd University of Petroleum & Minerals Control and Instrumentation Engineering Department Dhahran Saudi Arabia

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Developing a reliable financial distress prediction model has been a long-standing research area. Recently, machine learning algorithms have been increasingly popular in the area. In this paper, a hybrid approach of Genetic Algorithm (GA) and Multi-Layer Perceptron (MLP) for Financial Distress Prediction (FDP) (FDP-GAMLP) is proposed. FDP-GAMLP emphasizes on GA-based tuning of the four major hyper-parameters, namely network width, network depth, network optimizer, and dense layer activation function, which can influence on whether the algorithm explodes or converges. An improved GA is utilized to optimize the MLP model's hyper-parameters for better prediction. The prediction performance is evaluated using real data set with samples of companies from countries in Middle East region. The resampling technique using k-fold evaluation metrics is adopted to get unbiased and most accurate results. The simulation results show that FDP-GAMLP outperforms the classical machine learning models in terms of predictive accuracy. © 2022 IEEE.

关键词： Genetic algorithms

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Application of artificial intelligence in diagnosis of pulmonary tuberculosis

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Chinese Medical Journal 2024年第5期137卷 559-561页

作者： Jingli Du Yue Su Juan Qiao Shang Gao Enjun Dong Ruilan Wang Yanhui Nie Jing Ji Zhendong Wang Jianqin Liang Wenping Gong Tuberculosis Control Team Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China Department of Computer Science and Technology College of Management Science and EngineeringCapital University of Economics and BusinessBeijing 100071China The Second Ward of Tuberculosis Department Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China

Tuberculosis(TB)is an infectious disease caused by Mycobacterium *** the diagnostic technology of pulmonary tuberculosis(PTB)has advanced,accurate and differential diagnoses of PTB are still *** recent years,the rapid development of artificial intelligence(AI)and its wide application in the medical field have provided new opportunities for diagnosing and treating TB and *** machine learning model of AI has not only helped physicians improve diagnostic accuracy,but also enabled them to make early preventive diagnoses for individuals at increased risk of ***,AI can guide physicians to formulate targeted treatment strategies for PTB patients with different conditions.

关键词： tuberculosis diagnosis artificial

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Clebpi: Contrastive Learning for Bug Priority Inference

SSRN

引用

SSRN 2022年

作者： Wang, Wen-Yao Wu, Chen-Hao He, Jie Faculty of Innovation Engineering Macau University of Science and Technology Macau China College of Computer Science and Electronic Engineering Hunan University Hunan Changsha China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Guangxi Wuzhou China

Context: Automated bug priority inference can reduce the time overhead of bug triagers for priority assignments, improving the efficiency of software ***: There are two orthogonal lines for this task, i.e., traditional machine learning based (TML-based) and neural network based (NN based) approaches. Although these approaches achieve competitive performance, our observation finds that existing approaches face the following two issues: 1) TML-based approaches require much manual feature engineering and cannot learn the semantic information of bug reports;2) Both TML-based and NN-based approaches cannot effectively address the label imbalance problem because they are difficult to distinguish the semantic difference between bug reports with different ***: We propose CLeBPI (Contrastive Learning for Bug Priority Inference), which leverages pre-trained language model and contrastive learning to tackle the above-mentioned two issues. Specifically, CLeBPI is first pre-trained on a large-scale bug report corpus in a self-supervised way, thus it can automatically learn contextual representations of bug reports without manual feature engineering. Afterward, it is further pre-trained by a contrastive learning objective, which enables it to distinguish semantic differences between bug reports, learning more precise contextual representations for each bug report. When finishing pre-training, we can connect a classification layer to CLeBPI and fine-tune it for bug priority inference in a supervised ***: We choose four baseline approaches and conduct comparison experiments on a public dataset. The experimental results show that CLeBPI outperforms all baseline approaches by 23.86%-77.80% in terms of weighted average F1-score, showing its ***: This paper proposes CLeBPI, a pre-trained model combining contrastive learning that can automatically predict bug priority. Experimental results show its effectiveness. © 2022, The A

关键词： computer software maintenance

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A Practical Quantum Network Coding Protocol Based on Non-Maximally Entangled State

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computers, Materials & Continua 2021年第8期68卷 2651-2663页

作者： Zhen-Zhen Li Zi-Chen Li Xiu-Bo Chen Zhiguo Qu Xiaojun Wang Haizhu Pan School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing210044China School of Electronic Engineering Dublin City UniversityDublin9Ireland College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China

In many earlier works,perfect quantum state transmission over the butterfly network can be achieved via quantum network coding protocols with the assist of maximally entangled ***,in actual quantum networks,a maximally entangled state as auxiliary resource is hard to be obtained or easily turned into a non-maximally entangled state subject to all kinds of environmental ***,we propose a more practical quantum network coding scheme with the assist of non-maximally entangled *** this paper,a practical quantum network coding protocol over grail network is proposed,in which the non-maximally entangled resource is assisted and even the desired quantum state can be perfectly *** achievable rate region,security and practicability of the proposed protocol are discussed and *** practical quantum network coding protocol proposed over the grail network can be regarded as a useful attempt to help move the theory of quantum network coding towards practicability.

关键词： Quantum network coding non-maximally entangled state quantum grail network practical protocol

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Comparative Study of Optimal Nonlinear control Schemes for Hanging Mass Actuated by Uncertain Pneumatic Muscle

Comparative Study of Optimal Nonlinear Control Schemes for H...

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11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Sadiq, Musaab Esam Humaidi, Amjad Jaleel Kadhim, Saleem Khalefa Mahdi, Abdulkareem Sh. Alkhayyat, Ahmed Ibraheem, Ibraheem Kasim General Company for Grain Processing Ministry of Trade Baghdad Iraq University of Technology Control and Systems Eng. Dept. Baghdad Iraq School of Computer Science and Eng. Taylor's University Malaysia College of Technical Engineering Islamic University Najaf Iraq College of Engineering University of Baghdad Baghdad Iraq

ISBN: (纸本)9781665437660

The systems actuated by Pneumatic Artificial Muscles (PAMs) are characterized by high nonlinearity and time-varying of their coefficients. Therefore, nonlinear and robust controllers are required to cope with these challenging control problems. This work presents the development of new control design for trajectory tracking of PAM-Actuated manipulator based on Sliding Mode control (SMC), Backstepping Sliding Mode control (BSMC). The control laws of controlled PAM hanging mass based on SMC and BSMC have been developed according to Lyapunov stability analysis. Additionally, the PSO technique is used to tune the design parameters of proposed controllers for further enhancement of controlled PAM system performance. A comparison study has been conducted among the proposed controllers via computer simulation using MATLAB/Simulink programming software. © 2021 IEEE.

关键词： Trajectory tracking System performance Muscles Programming Systems engineering and theory Software Nonlinear dynamical systems

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Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms

arXiv

引用

arXiv 2024年

作者： Zhang, Han Lin, Shengxiang Zhang, Xingyi Wang, Yu Zhang, Yangguang College of Aritificial Intelligence and Automation Hohai University Nanjing211100 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Control and Computer Engineering North China Electric Power University Beijing071003 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

In high-energy particle physics, extracting information from complex detector signals is crucial for energy reconstruction. Recent advancements involve using deep learning to process calorimeter images from various sub-detectors in experiments like the Large Hadron Collider (LHC) for energy map reconstruction. This paper compares classical algorithms—MLP, CNN, U-Net, and RNN—with variants that include self-attention and 3D convolution modules to evaluate their effectiveness in reconstructing the initial energy distribution. Additionally, a test dataset of jet events is utilized to analyze and compare models’ performance in handling anomalous high-energy events. The analysis highlights the effectiveness of deep learning techniques for energy image reconstruction and explores their potential in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Timeliness-Aware Multiple Rumor Sources Estimation in Dynamic Online Social Networks

SSRN

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SSRN 2024年

作者： Huang, Da-Wen Wu, Wenjie Bi, Jichao Li, Junli Gan, Chenquan Zhou, Wei College of Computer Science Sichuan Normal University Chengdu China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating the damage caused by them. However, most studies are not suitable for identifying rumor sources in dynamic OSNs with community structures. Moreover, these studies tend to neglect the impact of timeliness on the spread of rumors, potentially leading to an overestimation of rumors' propagation ability. To overcome these limitations, this paper proposes a multiple rumor sources estimation framework for OSNs. First, a community-based dynamic network model is introduced to depict the temporal nature and community feature of OSNs. Second, a meticulously designed microcosmic SIR model, incorporating the timeliness of rumor topics, is developed to unravel the complex dynamics of rumor propagation. Then, a computationally efficient multiple rumor sources estimation algorithm is proposed. This algorithm utilizes the infection information collected by sensors and applies the maximum likelihood estimation (MLE) method to identify the rumor sources. Finally, experimental results on real-world and synthetic temporal networks demonstrate the effectiveness of the proposed rumor source estimation algorithm. © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

arXiv

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arXiv 2024年

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China SYSTEMA Research Center European University Cyprus Nicosia Cyprus

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multistage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Distributed Optimal control with Recovered Robustness for Uncertain Network Systems: A Complementary Design Approach

arXiv

引用

arXiv 2022年

作者： Li, Zhongkui Jiao, Junjie Chen, Xiang The State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China The Chair of Information-oriented Control Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany The Department of Electrical and Computer Engineering University of Windsor Canada

This paper considers the distributed robust suboptimal consensus control problem of linear multi-agent systems, with both H2 and H∞ performance requirements. A novel two-step complementary design approach is proposed. In the first step, a distributed control law is designed for the nominal multi-agent system to achieve consensus with a prescribed H2 performance. In the second step, an extra control input, depending on some carefully chosen residual signals indicating the modeling mismatch, is designed to complement the H2 performance by providing robustness guarantee in terms of H∞ requirement with respect to disturbances or uncertainties. The proposed complementary design approach provides an additional degree of freedom for design, having two separate controls to deal with the H2 performance and the robustness of consensus, respectively. Thereby, it does not need to make much trade-off, and can be expected to be much less conservative than the trade-off design such as the mixed H2/H∞ control method. Besides, this complementary approach will recover the achievable H2 performance when external disturbances or uncertainties do not exist. The effectiveness of the theoretical results and the advantages of the complementary approach are validated via numerical simulations. Copyright © 2022, The Authors. All rights reserved.

关键词： Robust control

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