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arXiv 2021年

作者： Xu, Xiaohang Peng, Hao Sun, Lichao Bhuiyan, Md Zakirul Alam Liu, Lianzhong He, Lifang School of Cyber Science and Technology Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100083 China Department of Computer Science University of Illinois at Chicago Chicago United States Department of Computer and Information Sciences Fordham University JMH 334 E Fordham Road BronxNY10458 United States Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States

Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research. Although researchers hope that artificial intelligence can contribute to the diagnosis and treatment of depression, the traditional centralized machine learning needs to aggregate patient data, and the data privacy of patients with mental illness needs to be strictly confidential, which hinders machine learning algorithms clinical application. To solve the problem of privacy of the medical history of patients with depression, we implement federated learning to analyze and diagnose depression. First, we propose a general multi-view federated learning framework using multi-source data,which can extend any traditional machine learning model to support federated learning across different institutions or parties. Secondly, we adopt late fusion methods to solve the problem of inconsistent time series of multi-view data. Finally, we compare the federated framework with other cooperative learning frameworks in performance and discuss the related results. Copyright © 2021, The Authors. All rights reserved.

关键词： data privacy

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A Deep Learning Framework for Stock Prediction Using LSTM 1

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International Conference on Decision Economics, DECON 2019

作者： Lin, Yaohu Liu, Shancun Yang, Haijun Wu, Harris School of Economics and Management Beihang University Beijing China Key Laboratory of Complex System Analysis Management and Decision Beihang University Ministry of Education Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Strome College of Business Old Dominion University NorfolkVA United States

ISBN: (数字)9783030382278

ISBN: (纸本)9783030382261

In order to test the predictive power of the deep learning model, several machine learning methods were introduced for comparison. Empirical case results for the period of 2000 to 2017 show the forecasting power of deep learning technology. With a series of linear regression indicator measurement, we find LSTM networks outperform traditional machine learning methods, i.e., Linear Regression, Auto ARIMA, KNN. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

arXiv

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arXiv 2021年

作者： Li, Qian Peng, Hao Li, Jianxin Wu, Jia Ning, Yuanxing Wang, Lihong Yu, Philip S. Wang, Zheng Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Computing Macquarie University Sydney Australia National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China Department of Computer Science University of Illinois at Chicago Chicago60607 United States School of Computing University of Leeds LeedsLS2 9JT United Kingdom

Event extraction is a fundamental task for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doing so for real-life event descriptions is challenging because an argument’s role often varies in different contexts. While the relationship and interactions between multiple arguments are useful for settling the argument roles, such information is largely ignored by existing approaches. This paper presents a better approach for event extraction by explicitly utilizing the relationships of event arguments. We achieve this through a carefully designed task-oriented dialogue system. To model the argument relation, we employ reinforcement learning and incremental learning to extract multiple arguments via a multi-turned, iterative process. Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually. It then uses the newly obtained information to improve the decisions of previously extracted arguments. This two-way feedback process allows us to exploit the argument relations to effectively settle argument roles, leading to better sentence understanding and event extraction. Experimental results show that our approach consistently outperforms seven state-of-the-art event extraction methods for the classification of events and argument role and argument identification. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Design and analysis of a 2D discrete memristive map

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Physica Scripta 2025年第7期100卷 075219-075219页

作者： Haiwei Sang Qiao Wang Yuling Chen Xiong Yu Feifei Wu Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing College of Mathematics and Big Data Guizhou Education University Guiyang 550018 People’s Republic of China School of artificial intelligence Guangzhou University 510006 People’s Republic of China State Key Laboratory of Public Big Data Guizhou University Guiyang 550025 People’s Republic of China Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor 43600 Malaysia

This study proposes a novel 2D memristive hyperchaotic map (2DMHM) with hyperbolic tangent and absolute value functions. The 2DMHM exhibits an infinite of fixed points in a set of lines on the y-axis, with stability characteristics partitioned across memristor parameters and initial condition planes. System dynamics are systematically investigated through bifurcation analysis, Lyapunov exponent spectra, and basin of attraction, trajectory plots, revealing remarkable multistability and initial-sensitive chaotic behavior. The map demonstrates superior spectral entropy (SE) complexity across critical parameter ranges, significantly outperforming conventional chaotic systems. A microcontroller-based digital implementation validates the physical realizability of 2DMHM, while the NIST test success rate demonstrates its exceptional performance in pseudorandom number generation. These results establish 2DMHM as a promising candidate for secure communication systems and cryptographic applications.

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Asynchronous control for Markov jump systems subject to actuator saturation

Asynchronous control for Markov jump systems subject to actu...

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第33届中国控制与决策会议

作者： Chao Wang Lei Liu Wei Wang Xiaobing Luo Huijin Fan the National Key Laboratory of Science and Technology of Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology the School of Automation Science and Electrical Engineering and the Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Technology and Software Engineering Wuhan Polytechnic

In this paper,the asynchronous controller problem is considered for Markov jump systems(MJSs) subject to actuator *** asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are *** on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique,a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic ***,a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

关键词： Asynchronous control actuator saturation Markov jump systems

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Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection

arXiv

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arXiv 2020年

作者： Liu, Zhiwei Dou, Yingtong Yu, Philip S. Deng, Yutong Peng, Hao Department of Computer Science University of Illinois at Chicago School of Software Beijing University of Posts and Telecommunications Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Graph-based models have been widely used to fraud detection tasks. Owing to the development of Graph Neural Networks (GNNs), recent works have proposed many GNN-based fraud detectors based on either homogeneous or heterogeneous graphs. These works leverage existing GNNs and aggregate the neighborhood information to learn the node embeddings, which relies on the assumption that the neighbors share similar context, features, and relations. However, the inconsistency problem incurred by fraudsters is hardly investigated, i.e., the context inconsistency, feature inconsistency, and relation inconsistency. In this paper, we introduce these inconsistencies and design a new GNN framework, GraphConsis, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features;(2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability;(3) for the relation inconsistency, we learn the relation attention weights associated with the sampled nodes. Empirical analysis on four datasets demonstrates that the inconsistency problem is critical in fraud detection tasks. Extensive experiments show the effectiveness of GraphConsis. We also released a GNN-based fraud detection toolbox with implementations of SOTA models. The code is available at https://***/safe-graph/DGFraud. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Distributed adaptive finite-time time-varying group formation tracking for high-order multi-agent systems with directed topologies

Distributed adaptive finite-time time-varying group formatio...

引用

第三十九届中国控制会议

作者： Lei Tian Yongzhao Hua Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Beihang University Department of Aerospace Engineering University of Bristol Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates finite-time time-varying group formation（TVGF） tracking control problems for high-order multi-agent systems（MASs） with directed topologies. Firstly, the agents are classified into three types, i.e., the virtual leader, the group leader and the follower. A virtual leader is used to control the macroscopic movement of MASs. Subgroups can be interacted and coordinated with each other by the communication among group leaders. Secondly, a novel finite-time group formation tracking protocol using only the neighboring interaction is constructed based on adaptive integral sliding mode control. Then sufficient conditions for MASs with unknown external disturbances and directed topologies to realize finite-time TVGF tracking are derived using Lyapunov theory. Finally, a numerical simulation example is performed to demonstrate the effectiveness of theoretical results.

关键词： TVGF tracking finite-time control MASs adaptive integral sliding mode control

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Deep Reinforcement Learning Guided Graph Neural Networks for brain Network Analysis

arXiv

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arXiv 2022年

作者： Zhao, Xusheng Wu, Jia Peng, Hao Beheshti, Amin Monaghan, Jessica J.M. McAlpine, David Hernandez-Perez, Heivet Dras, Mark Dai, Qiong Li, Yangyang Yu, Philip S. He, Lifang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computing Macquarie University Sydney Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China National Acoustic Laboratories Sydney Australia Department of Linguistics The Australian Hearing Hub Macquarie University Sydney Australia CAEIT Beijing China Department of Computer Science University of Illinois ChicagoIL United States Computer Science & Engineering Lehigh University PA United States

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks. © 2022, CC BY.

关键词： Reinforcement learning

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Picosecond Optospintronic Tunnel Junctions for Non-volatile Photonic Memories

Research Square

引用

Research Square 2021年

作者： Wang, Luding Cheng, Houyi Li, Pingzhi Liu, Yang van Hees, Youri L.W. Lavrijsen, Reinoud Lin, Xiaoyang Cao, Kaihua Koopmans, Bert Zhao, Weisheng Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Applied Physics Institute for Photonic Integration Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands

Perpendicular magnetic tunnel junctions are one of the building blocks for spintronic memories, which allow fast nonvolatile data access, offering substantial potentials to revolutionize the mainstream computing architecture1-3. However, conventional switching mechanisms of such devices are fundamentally hindered by spin polarized currents4, either spin transfer torque5,6 or spin orbit torque7,8 with spin precession time limitation and excessive power dissipation. These physical constraints significantly stimulate the advancement of modern spintronics1,4. Here, we report an optospintronic tunnel junction using a photonic-spintronic combination. This composite device incorporates an all-optically switchable Co/Gd bilayer coupled to a CoFeB/MgO-based perpendicular magnetic tunnel junction by the Ruderman-Kittel-Kasuya-Yosida interaction. A picosecond all-optical operation of the optospintronic tunnel junction is explicitly confirmed by time-resolved measurements. Moreover, the device shows a considerable tunnel magnetoresistance and thermal stability. This proof-of-concept device represents an essential step towards ultrafast photonic memories with THz data access, as well as ultralow power consumption. © 2021, CC BY.

关键词： Tunnel junctions

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Incorporating Multiple Features to Predict Bug Fixing Time with Neural Networks

Incorporating Multiple Features to Predict Bug Fixing Time w...

引用

International Conference on Software Maintenance (ICSM)

作者： Wei Yuan Yuan Xiong Hailong Sun Xudong Liu SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China SKLSDE Lab School of Software Beihang University Beijing China

Debugging is a well-known time-consuming task, and knowing how long it would take to resolve bugs is of great importance for allocating the limited resources in a software development team. However, it is challenging to predict bug fixing time since fixing bugs is subject to a plethora of uncertain factors such as types of bugs, program complexity and developers' abilities. Existing work mainly focuses on developers' activities in a bug lifecycle and ignores other important factors. In light of the limitations of existing work, we propose a novel approach to predicting the bug fixing time by incorporating a comprehensive set of relevant features. Specifically, we consider four types of features including developers' activities, developers' sentiments, semantics of bugs, and efforts caused by understanding and analyzing source code, and design particular neural networks to take advantage of these features and make them work efficiently. Experimental results on four real application datasets demonstrate that on the one hand, our approach outperforms the state-of-the-art by over 5% in accuracy and 7.3% in F1-score on average; on the other hand, each type of the features considered in our approach plays an important part in the prediction.

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