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New directions for artificial intelligence: human, machine, biological, and quantum intelligence Comment

Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6,   Pages 984-990 doi: 10.1631/FITEE.2100227

Abstract:

This comment reviews the “once learning” mechanism (OLM) that was proposed byWeigang (1998), the subsequent success of “one-shot learning” in object categories (Li FF et al., 2003), and “you only look once” (YOLO) in objective detection (Redmon et al., 2016). Upon analyzing the current state of research in artificial intelligence (AI), we propose to divide AI into the following basic theory categories: artificial human intelligence (AHI), artificial machine intelligence (AMI), artificial biological intelligence (ABI), and artificial quantum intelligence (AQI). These can also be considered as the main directions of research and development (R&D) within AI, and distinguished by the following classification standards and methods: (1) human-, machine-, biological-, and quantum-oriented AI R&D; (2) information input processed by dimensionality increase or reduction; (3) the use of one/a few or a large number of samples for knowledge learning.

Keywords： 人工智能；机器学习；一次性学习；一瞥学习；量子计算    

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Communicative Learning: A Unified Learning Formalism Review

Luyao Yuan, Song-Chun Zhu

Engineering 2023, Volume 25, Issue 6,   Pages 77-100 doi: 10.1016/j.eng.2022.10.017

Abstract:

In this article, we propose a communicative learning (CL) formalism that unifies existing machine learning paradigms, such as passive learning, active learning, algorithmic teaching, and so forth, and facilitates the development of new learning methods. Arising from human cooperative communication, this formalism poses learning as a communicative process and combines pedagogy with the burgeoning field of machine learning. The pedagogical insight facilitates the adoption of alternative information sources in machine learning besides randomly sampled data, such as intentional messages given by a helpful teacher. More specifically, in CL, a teacher and a student exchange information with each other collaboratively to transmit and acquire certain knowledge. Each agent has a mind, which includes the agent's knowledge, utility, and mental dynamics. To establish effective communication, each agent also needs an estimation of its partner's mind. We define expressive mental representations and learning formulation sufficient for such recursive modeling, which endows CL with human-comparable learning efficiency. We demonstrate the application of CL to several prototypical collaboration tasks and illustrate that this formalism allows learning protocols to go beyond Shannon's communication limit. Finally, we present our contribution to the foundations of learning by putting forth hierarchies in learning and defining the halting problem of learning.

Keywords： Artificial intelligencehine     Cooperative communication     Machine learning     Pedagogy     Theory of mind    

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A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring Article

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Engineering 2021, Volume 7, Issue 9,   Pages 1262-1273 doi: 10.1016/j.eng.2020.08.028

Abstract:

Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge. However, most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution. In fact, due to the harsh environment of industrial systems, the
collected data from real industrial processes are always affected by many factors, such as the changeable operating environment, variation in the raw materials, and production indexes. These factors often cause the distributions of online monitoring data and historical training data to differ, which induces a model mismatch in the process-monitoring task. Thus, it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring. In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments, a robust transfer dictionary learning (RTDL) algorithm is proposed in this paper for industrial process monitoring. The RTDL is a synergy of representative learning and domain adaptive transfer learning. The proposed method regards historical training data and online testing data as the source domain and the target domain, respectively, in the transfer learning problem. Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework, which can reduce the distribution divergence between the source domain and target domain. In this way, a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment. Such a dictionary can effectively improve the performance of process monitoring and mode classification. Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.

Keywords： Process monitoring     Multimode process     Dictionary learning     Transfer learning    

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Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1800743

Abstract: models have achieved state-of-the-art performance in (NER); the good performance, however, relies heavily on substantial amounts of labeled data. In some specific areas such as medical, financial, and military domains, labeled data is very scarce, while is readily available. Previous studies have used to enrich word representations, but a large amount of entity information in is neglected, which may be beneficial to the NER task. In this study, we propose a for NER tasks, which learns to create high-quality labeled data by applying a pre-trained module to filter out erroneous pseudo labels. Pseudo labels are automatically generated for and used as if they were true labels. Our semi-supervised framework includes three steps: constructing an optimal single neural model for a specific NER task, learning a module that evaluates pseudo labels, and creating new labeled data and improving the NER model iteratively. Experimental results on two English NER tasks and one Chinese clinical NER task demonstrate that our method further improves the performance of the best single neural model. Even when we use only pre-trained static word embeddings and do not rely on any external knowledge, our method achieves comparable performance to those state-of-the-art models on the CoNLL-2003 and OntoNotes 5.0 English NER tasks.

Keywords： 命名实体识别；无标注数据；深度学习；半监督学习方法    

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The State of the Art of Data Science and Engineering in Structural Health Monitoring Article

Yuequan Bao, Zhicheng Chen, Shiyin Wei, Yang Xu, Zhiyi Tang, Hui Li

Engineering 2019, Volume 5, Issue 2,   Pages 234-242 doi: 10.1016/j.eng.2018.11.027

Abstract:

Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.

Keywords： Structural health monitoring     Monitoring data     Compressive sampling     Machine learning     Deep learning    

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Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective Review

Li Sun, Fengqi You

Engineering 2021, Volume 7, Issue 9,   Pages 1239-1247 doi: 10.1016/j.eng.2021.04.020

Abstract:

Due to growing concerns regarding climate change and environmental protection, smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy. Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties. The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improved alternative to these outdated methods. This paper reviews typical applications of ML and DDC at the level of monitoring, control, optimization, and fault detection of power generation systems, with a particular focus on uncovering how these methods can function in evaluating, counteracting, or withstanding the effects of the associated uncertainties. A holistic view is provided on the control techniques of smart power generation, from the regulation level to the planning level. The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility, maneuverability, flexibility, profitability, and safety (abbreviated as the "5-TYs”), respectively. Finally, an outlook on future research and applications is presented.

Keywords： Smart power generation     Machine learning     Data-driven control     Systems engineering    

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A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing Article

Yaoyao Bao, Yuanming Zhu, Feng Qian

Engineering 2022, Volume 18, Issue 11,   Pages 186-196 doi: 10.1016/j.eng.2022.04.025

Abstract:

Inspired by the tremendous achievements of meta-learning in various fields, this paper proposes the local quadratic embedding learning (LQEL) algorithm for regression problems based on metric learning and neural networks (NNs). First, Mahalanobis metric learning is improved by optimizing the global consistency of the metrics between instances in the input and output space. Then, we further prove that the improved metric learning problem is equivalent to a convex programming problem by relaxing the constraints. Based on the hypothesis of local quadratic interpolation, the algorithm introduces two lightweight NNs; one is used to learn the coefficient matrix in the local quadratic model, and the other is implemented for weight assignment for the prediction results obtained from different local neighbors. Finally, the two sub-models are embedded in a unified regression framework, and the parameters are learned by means of a stochastic gradient descent (SGD) algorithm. The proposed algorithm can make full use of the information implied in target labels to find more reliable reference instances. Moreover, it prevents the model degradation caused by sensor drift and unmeasurable variables by modeling variable differences with the LQEL algorithm. Simulation results on multiple benchmark datasets and two practical industrial applications show that the proposed method outperforms several popular regression methods.

Keywords： Local quadratic embedding     Metric learning     Regression machine     Soft sensor    

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One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning Research Articles

Minggang DONG, Ming LIU, Chao JING,jingchao@glut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 2,   Pages 278-290 doi: 10.1631/FITEE.2000417

Abstract: Since traditional machine learning methods are sensitive to skewed distribution and do not consider the characteristics in multiclass imbalance problems, the skewed distribution of multiclass data poses a major challenge to machine learning algorithms. To tackle such issues, we propose a new splitting criterion of the decision tree based on the one-against-all-based (OAHD). Two crucial elements are included in OAHD. First, the is integrated into the process of computing the in OAHD, thereby extending the decision tree to cope with the multiclass imbalance problem. Second, for the multiclass imbalance problem, the distribution and the number of distinct classes are taken into account, and a modified Gini index is designed. Moreover, we give theoretical proofs for the properties of OAHD, including skew insensitivity and the ability to seek a purer node in the decision tree. Finally, we collect 20 public real-world imbalanced data sets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and the University of California, Irvine (UCI) repository. Experimental and statistical results show that OAHD significantly improves the performance compared with the five other well-known in terms of Precision, F-measure, and multiclass area under the receiver operating characteristic curve (MAUC). Moreover, through statistical analysis, the Friedman and Nemenyi tests are used to prove the advantage of OAHD over the five other .

Keywords： Decision trees     Multiclass imbalanced learning     Node splitting criterion     Hellinger distance     One-against-all scheme    

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MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning Research Articles

Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie,17034203@qq.com

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900121

Abstract: With the growing amount of information and data, s have been widely used in many applications, including the Google File System, Amazon S3, Hadoop Distributed File System, and Ceph, in which load balancing of plays an important role in improving the input/output performance of the entire system. Unbalanced load on the server leads to a serious bottleneck problem for system performance. However, most existing load balancing strategies, which are based on subtree segmentation or hashing, lack good dynamics and adaptability. In this study, we propose a (MDLB) mechanism based on (RL). We learn that the algorithm and our RL-based strategy consist of three modules, i.e., the policy selection network, load balancing network, and parameter update network. Experimental results show that the proposed MDLB algorithm can adjust the load dynamically according to the performance of the servers, and that it has good adaptability in the case of sudden change of data volume.

Keywords： 面向对象的存储系统；元数据；动态负载均衡；强化学习；Q_learning    

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Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives Research Article

Tao SHEN, Jie ZHANG, Xinkang JIA, Fengda ZHANG, Zheqi LV, Kun KUANG, Chao WU, Fei WU,chao.wu@zju.edu.cn,wufei@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1390-1402 doi: 10.1631/FITEE.2300098

Abstract: (FL) is a novel technique in deep learning that enables clients to collaboratively train a shared model while retaining their decentralized data. However, researchers working on FL face several unique challenges, especially in the context of heterogeneity. Heterogeneity in data distributions, computational capabilities, and scenarios among clients necessitates the development of customized models and objectives in FL. Unfortunately, existing works such as FedAvg may not effectively accommodate the specific needs of each client. To address the challenges arising from heterogeneity in FL, we provide an overview of the heterogeneities in data, model, and objective (DMO). Furthermore, we propose a novel framework called federated mutual learning (FML), which enables each client to train a personalized model that accounts for the data heterogeneity (DH). A "meme model" serves as an intermediary between the personalized and global models to address model heterogeneity (MH). We introduce a technique called deep mutual learning (DML) to transfer knowledge between these two models on local data. To overcome objective heterogeneity (OH), we design a shared global model that includes only certain parts, and the personalized model is task-specific and enhanced through mutual learning with the meme model. We evaluate the performance of FML in addressing DMO heterogeneities through experiments and compare it with other commonly used FL methods in similar scenarios. The results demonstrate that FML outperforms other methods and effectively addresses the DMO challenges encountered in the FL setting.

Keywords： Federated learning     Knowledge distillation     Privacy preserving     Heterogeneous environment    

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Disturbance rejection via iterative learning controlwith a disturbance observer for active magnetic bearing systems None

Ze-zhi TANG, Yuan-jin YU, Zhen-hong LI, Zheng-tao DING

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 1,   Pages 131-140 doi: 10.1631/FITEE.1800558

Abstract:

Although standard iterative learning control (ILC) approaches can achieve perfect tracking for active magnetic bearing (AMB) systems under external disturbances, the disturbances are required to be iteration-invariant. In contrast to existing approaches, we address the tracking control problem of AMB systems under iteration-variant disturbances that are in different channels from the control inputs. A disturbance observer based ILC scheme is proposed that consists of a universal extended state observer (ESO) and a classical ILC law. Using only output feedback, the proposed control approach estimates and attenuates the disturbances in every iteration. The convergence of the closed-loop system is guaranteed by analyzing the contraction behavior of the tracking error. Simulation and comparison studies demonstrate the superior tracking performance of the proposed control approach.

Keywords： Active magnetic bearings (AMBs)     Iterative learning control (ILC)     Disturbance observer    

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Decentralized multi-agent reinforcement learning with networked agents: recent advances Review Article

Kaiqing Zhang, Zhuoran Yang, Tamer Başar,kzhang66@illinois.edu,zy6@princeton.edu,basar1@illinois.edu

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 802-814 doi: 10.1631/FITEE.1900661

Abstract: Multi-agent (MARL) has long been a significant research topic in both machine learning and control systems. Recent development of (single-agent) deep has created a resurgence of interest in developing new MARL algorithms, especially those founded on theoretical analysis. In this paper, we review recent advances on a sub-area of this topic: decentralized MARL with networked agents. In this scenario, multiple agents perform sequential decision-making in a common environment, and without the coordination of any central controller, while being allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and the smart grid. This review covers several of our research endeavors in this direction, as well as progress made by other researchers along the line. We hope that this review promotes additional research efforts in this exciting yet challenging area.

Keywords： 强化学习；多智能体系统；网络系统；一致性优化；分布式优化；博弈论    

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Federated Learning for 6G: Applications, Challenges, and Opportunities Review

Zhaohui Yang, Mingzhe Chen, Kai-Kit Wong, H. Vincent Poor, Shuguang Cui

Engineering 2022, Volume 8, Issue 1,   Pages 33-41 doi: 10.1016/j.eng.2021.12.002

Abstract:

Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and inference. However, due to privacy restrictions and limited communication resources in wireless networks, it is often undesirable or impractical for the devices to transmit data to parameter sever. One approach to mitigate these problems is federated learning (FL), which enables the devices to train a common machine learning model without data sharing and transmission. This paper provides a comprehensive overview of FL applications for envisioned sixth generation (6G) wireless networks. In particular, the essential requirements for applying FL to wireless communications are first described. Then potential FL applications in wireless communications are detailed. The main problems and challenges associated with such applications are discussed. Finally, a comprehensive FL implementation for wireless communications is described.

Keywords： Federated learning 6G     Reconfigurable intelligent surface     Semantic communication     Sensing     communication and computing    

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Optimal synchronization control for multi-agent systems with input saturation: a nonzero-sum game Research Article

Hongyang LI, Qinglai WEI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1010-1019 doi: 10.1631/FITEE.2200010

Abstract: This paper presents a novel method for with . The multi-agent game theory is introduced to transform the problem into a multi-agent . Then, the Nash equilibrium can be achieved by solving the coupled Hamilton–Jacobi–Bellman (HJB) equations with nonquadratic input energy terms. A novel method is presented to obtain the Nash equilibrium solution without the system models, and the critic neural networks (NNs) and actor NNs are introduced to implement the presented method. Theoretical analysis is provided, which shows that the iterative control laws converge to the Nash equilibrium. Simulation results show the good performance of the presented method.

Keywords： Optimal synchronization control     Multi-agent systems     Nonzero-sum game     Adaptive dynamic programming     Input saturation     Off-policy reinforcement learning     Policy iteration    

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DAN: a deep association neural network approach for personalization recommendation Research Articles

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-980 doi: 10.1631/FITEE.1900236

Abstract: The collaborative filtering technology used in traditional systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional algorithms, thus leading to the emergence of systems based on . At present, s mostly use deep s to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the . Aimed at this problem, in this paper we propose a feedforward deep method, called the deep association (DAN), which is based on the joint action of multiple categories of information, for implicit feedback . Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint s can provide better performance.

Keywords： Neural network     Deep learning     Deep association neural network (DAN)     Recommendation    

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Title Author Date Type Operation

New directions for artificial intelligence: human, machine, biological, and quantum intelligence

Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO

Journal Article

Communicative Learning: A Unified Learning Formalism

Luyao Yuan, Song-Chun Zhu

Journal Article

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Journal Article

Learning to select pseudo labels: a semi-supervised method for named entity recognition

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Journal Article

The State of the Art of Data Science and Engineering in Structural Health Monitoring

Yuequan Bao, Zhicheng Chen, Shiyin Wei, Yang Xu, Zhiyi Tang, Hui Li

Journal Article

Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective

Li Sun, Fengqi You

Journal Article

A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing

Yaoyao Bao, Yuanming Zhu, Feng Qian

Journal Article

One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning

Minggang DONG, Ming LIU, Chao JING,jingchao@glut.edu.cn

Journal Article

MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning

Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie,17034203@qq.com

Journal Article

Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives

Tao SHEN, Jie ZHANG, Xinkang JIA, Fengda ZHANG, Zheqi LV, Kun KUANG, Chao WU, Fei WU,chao.wu@zju.edu.cn,wufei@zju.edu.cn

Journal Article

Disturbance rejection via iterative learning controlwith a disturbance observer for active magnetic bearing systems

Ze-zhi TANG, Yuan-jin YU, Zhen-hong LI, Zheng-tao DING

Journal Article

Decentralized multi-agent reinforcement learning with networked agents: recent advances

Kaiqing Zhang, Zhuoran Yang, Tamer Başar,kzhang66@illinois.edu,zy6@princeton.edu,basar1@illinois.edu

Journal Article

Federated Learning for 6G: Applications, Challenges, and Opportunities

Zhaohui Yang, Mingzhe Chen, Kai-Kit Wong, H. Vincent Poor, Shuguang Cui

Journal Article

Optimal synchronization control for multi-agent systems with input saturation: a nonzero-sum game

Hongyang LI, Qinglai WEI

Journal Article

DAN: a deep association neural network approach for personalization recommendation

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Journal Article