Constrained multiobjective optimization problems often have complex feasible regions and constrained Pareto fronts. These factors bring great challenges to current constrained multiobjective optimization evolutionary algorithms (CMOEAs). To solve this problem and further balance the objective optimization and constraint satisfaction, we propose an indexes-based and partial restart-based constrained multiobjective optimization (IRCMO) algorithm. In IRCMO, a two-stage (i.e., development and enhancement) and tri-population framework is designed. IRCMO adopts the aggregative indexes-based evaluation and adaptive collaborative partial restart strategy to assist the evolution of the first and second populations. The third population is obtained by directed sampling, which is mostly located at the boundary of the feasible region and enhances the exploration ability of extreme solutions. At the end of each generation, a progressive dual-archive strategy is designed to screen the solutions distributed uniformly from three populations. Experimental results demonstrate that IRCMO is superior to the other six state-of-the-art CMOEAs on several constraint benchmark suites and real-world problems.

Intelligent Transportation System (ITS) is a critical component of smart cities, however, certain issues significantly limit the construction of ITS. On the one hand, as the core resource of ITS, traffic data often suffers from missing values due to sensor failure, communication interruption, and so on. On the other hand, traffic flow change is a complex dynamic process that resulted from periodic changes caused by social activities, which increases the difficulty of data completion. To address these issues, the Message Passing Period-Aware Imputation Network (MPPAIN) is proposed. Firstly, the spatial-temporal information is transmitted sequentially in time order by the Message Passing Block based on gated recurrent unit, and the missing values are preliminarily estimated. Then, the output is fed to the Period-Aware Block to find the main frequency components that represent the changes of the traffic flow in the frequency domain through the Fourier transform. Subsequently, the traffic flow is divided according to the major periods, and the second time estimation is completed by extracting the intra-periodic and inter-periodic features simultaneously through the convolutional neural network. Finally, a bi-directional structure is designed by reversing the input traffic flow in time order to further extract the spatial-temporal and periodic features from the future to the past. Experiments demonstrate that the proposed model has excellent data imputation capabilities in various simulated missing rates and missing scenarios on several real traffic datasets.

Conventional federated learning (FL) assumes the homogeneity of models, necessitating clients to expose their model parameters to enhance the performance of the server model. However, this assumption cannot reflect real-world scenarios. Sharing models and parameters raises security concerns for users, and solely focusing on the server-side model neglects clients' personalization requirements, potentially impeding expected performance improvements of users. On the other hand, prioritizing personalization may compromise the generalization of the server model, thereby hindering extensive knowledge migration. To address these challenges, we put forth an important problem: How can FL ensure both generalization and personalization when clients' models are heterogeneous? In this work, we introduce FedTED, which leverages a twin-branch structure and data-free knowledge distillation (DFKD) to address the challenges posed by model heterogeneity and diverse objectives in FL. The employed techniques in FedTED yield significant improvements in both personalization and generalization, while effectively coordinating the updating process of clients' heterogeneous models and successfully reconstructing a satisfactory global model. Our empirical evaluation demonstrates that FedTED outperforms many representative algorithms, particularly in scenarios where clients' models are heterogeneous, achieving a remarkable 19.37% enhancement in generalization performance and up to 9.76% improvement in personalization performance.

As 5-th Generation (5G) mobile communication and edge computing technologies mature, Intelligent Transportation System (ITS) are gradually becoming a reality. In the 5G heterogeneous network, resources such as computing, storage, and communication are allocated to each Road Side Unit (RSU) to provide intelligent services for vehicles. However, the existing average allocation method based on historical experience can easily lead to over-concentration or insufficient resources, which causes waste and reduces the Quality of Service (QoS). To solve this problem, this paper proposes a Multi-Objective Neural Time-series Prediction (M-ONTP) scheme for resource pre-allocation scenario in ITS. The scheme takes into account the complexity and diversity of service resource, innovatively treats the number of vehicles and communication power as joint optimization metrics, and proposes a multi-objective learning model. Benefiting from the vehicle data collected by RSUs in real time, we utilize historical traffic information to predict future road load and rely on Software Defined Network (SDN) to design a flexible resource pre-allocated architecture for ITS. To enhance the effectiveness of feature capture, M-ONTP also organically integrates various neural networks, which can appropriately handle large-scale time-series traffic flow. And we choose two layers of road data for fitting, which ensures that the model has a wide horizon to receive sufficient information. SUMO-based simulation experiments show that our scheme accurately realizes the prediction of joint objective and has significant performance advantage over other models. Meanwhile, our pre-allocation strategy reduces the total resource consumption by about 7%, increases the sufficiency rate by about 7%, and decreases the redundancy by about 12% while ensuring enough service resource to maintain normal QoS, which validates the effectiveness of M-ONTP.

The combination of mobile communication and the Internet of Things (IoT) has made physical devices more intelligent, bringing great convenience to our lives. However, the deep integration of personal information and the Internet increases the risk of data leakage and is easily exploited maliciously. In addition, due to limited system resources, smart devices with lightweight design are required. Therefore, it is necessary to realize low-energy and effective abnormal behavior detection of IoT devices, but the existing detection methods have disadvantages such as leakage of user privacy, low accuracy, and difficulty in dynamically improving the effect. To address these issues, this paper proposes a dynamic interactive minor anomaly detection scheme called ADONIS based on Swarm Learning (SL). The scheme combines the concept of swarm defense and utilizes SL to achieve local data fusion, which improves the detection effect and protects user privacy. Moreover, the decentralized structure of SL can cope with the impact of single node damage to enhance the robustness of IoT services. Furthermore, we propose training and detection decoupling framework to achieve high accuracy, low energy consumption, and low latency. It improves the performance by fitting the training model with full data, and simplifies the complexity of the detection model using knowledge distillation. We also design a self-enhancing dynamic strategy based on the decoupling framework to maintain powerful detection capability through human-computer interaction (HCI) and continuous learning. The framework relies on traffic data to keep the model sensitive to new behavior through iterative training without disturbing the user. Finally, simulation experiments show that our proposed scheme can achieve 82.2% accuracy, reduce the average detection time to 8.22$ ms$, and simplify the model complexity by 15.9%. Compared with existing methods, ADONIS can provide lighter, safer and more accurate anomaly detection.

Abstract As a special case of common object removal, image person removal is playing an increasingly important role in social media and criminal investigation domains. Due to the integrity of person area and the complexity of human posture, person removal has its own dilemmas. In this paper, a novel idea is proposed to tackle these problems from the perspective of data synthesis. Concerning the lack of a dedicated dataset for image person removal, two dataset production methods are proposed to automatically generate images, masks and ground truths, respectively. Then, a learning framework similar to local image degradation is proposed so that the masks can be used to guide the feature extraction process and more texture information can be gathered for final prediction. A coarse‐to‐fine training strategy is further applied to refine the details. The data synthesis and learning framework combine well with each other. Experimental results verify the effectiveness of the method quantitatively and qualitatively, and the trained network proves to have good generalization ability either on real or synthetic images.

This article proposes a CNN-based born-again Takagi–Sugeno–Kang (TSK) fuzzy classifier denoted as CNNBaTSK. CNNBaTSK achieves the following distinctive characteristics: 1) CNNBaTSK provides a new perspective of knowledge distillation with a noniterative learning method (least learning machine with knowledge distillation, LLM-KD) to solve the consequent parameters of fuzzy rule, where consequent parameters are trained jointly on the ground-truth label loss, knowledge distillation loss, and regularization term; 2) with the inherent advantage of the fuzzy rule, CNNBaTSK has the capability to express the dark knowledge acquired from the CNN in an interpretable manner. Specifically, the dark knowledge (soft label information) is partitioned into five fixed antecedent fuzzy spaces. The centers of each soft label information in different fuzzy rules are {0, 0.25, 0.5, 0.75, 1}, which may have corresponding linguistic explanations: {very low, low, medium, high, very high}. For the consequent part of the fuzzy rule, the original features are employed to train the consequent parameters that ensure the direct interpretability in the original feature space. The experimental results on the benchmark datasets and the CHB-MIT EEG dataset demonstrate that CNNBaTSK can simultaneously improve the classification performance and model interpretability.

Aiming at low efficiency of agricultural machinery scheduling, this paper proposes an adaptive immune-following algorithm (AIFA) based on immune algorithm and artificial fish swarm algorithm. The adaptive crossover operator is used to accelerate convergence, and adaptive mutation operator ensures good diversity of population. After the adaptive evolution operations are performed, the following operator based on the following behavior of artificial fish swarm algorithm is embedded into the algorithm, which improves the convergence precision and obtains the promising optimization results. Experiments on scheduling considering the breakdown of agricultural machinery are performed based on multiple regions and multiple agricultural machineries. Compared with the immune algorithm and genetic algorithm, the simulation results demonstrate that AIFA can converge faster and achieve a better optimal solution.

With the development of smart devices, the computing capabilities of portable end devices such as mobile phones have been greatly enhanced. Meanwhile, traditional cloud computing faces great challenges caused by privacy-leakage and time-delay problems, there is a trend to push models down to edges and end devices. However, due to the limitation of computing resource, it is difficult for end devices to complete complex computing tasks alone. Therefore, this article divides the model into two parts and deploys them on multiple end devices and edges, respectively. Meanwhile, an early exit is set to reduce computing resource overhead, forming a hierarchical distributed architecture. In order to enable the distributed model to continuously evolve by using new data generated by end devices, we comprehensively consider various data distributions on end devices and edges, proposing a hierarchical federated learning framework FLEE , which can realize dynamical updates of models without redeploying them. Through image and sentence classification experiments, we verify that it can improve model performances under all kinds of data distributions, and prove that compared with other frameworks, the models trained by FLEE consume less global computing resource in the inference stage.

Accurate estimation of short-term traffic flow, which can help to assist travelers make better route choices, is a significant research field of intelligent transportation system. In order to extract complex spatiotemporal features from a small amount of available traffic data, in this paper we propose a novel Deep Graph Gaussian Processes (DGGPs) for short-term traffic flow prediction. First, in order to accurately describe the relationship between vertices in time series, this paper proposes an attention kernel. Based on this, the Aggregation Gaussian Process uses attention kernel as the covariance function, which overcomes the problem that the existing Gaussian processes and the deep Gaussian processes cannot effectively obtain dynamic spatial features. Second, DGGPs are constructed by the Aggregation Gaussian Process (AGP), the Temporal Convolutional Gaussian Process (TCGP) and the Gaussian process with linear kernel, to solve the existing short-term traffic flow forecasting models cannot obtain complex spatiotemporal features from a small amount of available data. We verify that the attention kernel helps to the proposed model convergence on the three data sets. At the same time, the proposed DGGP can obtain spatiotemporal features from the situation with less available spatial information or temporal information, accurately predict short-term traffic flow, and quantify temporal uncertainty.

Generative adversarial networks (GANs) have shown great success in deep representations learning, data generation, and security enhancement. With the development of the Internet of Things, 5th generation wireless systems (5G), and other technologies, the large volume of data collected at the edge of networks provides a new way to improve the capabilities of GANs. Due to privacy, bandwidth, and legal constraints, it is not appropriate to upload all the data to the cloud or servers for processing. Therefore, this article focuses on deploying and training GANs at the edge rather than converging edge data to the central node. To address this problem, we designed a novel distributed learning architecture for GANs, called DANCE. DANCE can adaptively perform communication compression based on the available bandwidth, while supporting both data and model parallelism training of GANs. In addition, inspired by the gossip mechanism and Stackelberg game, a compatible algorithm, AC-GAN is proposed. The theoretical analysis guarantees the convergence of the model and the existence of approximate equilibrium in AC-GAN. Both simulation and prototype system experiments show that AC-GAN can achieve better training effectiveness with less communication overhead than the SOTA algorithms, i.e., FL-GAN and MD-GAN.

For many application scenarios where raw and even multidomain training data can be easily collected, and at the same time, validation data (as ground-truth data) are available, it becomes naturally desirable for us to perform an enhanced classification/prediction on only validation data with the appropriate leverage of training data. In this article, a novel ensemble framework EP-TSK-FK of Takagi–Sugeno–Kang (TSK) fuzzy subclassifiers, is proposed to achieve the following distinctive characteristics: 1) each interpretable TSK fuzzy subclassifier on each training subset can be quickly built in parallel such that its outputs provide the values of the corresponding augmented features of the original validation data space; 2) as a novel ensemble method of fuzzy subclassifiers, EP-TSK-FK trains all the interpretable TSK fuzzy subclassifiers only once and does not explicitly reuse them while predicting a testing sample, which thereby reduces the computational complexity of the ensemble process for prediction; 3) after running the proposed iterative fuzzy c-means clustering algorithm iterative fuzzy C-means clustering (IFCM) on the augmented validation data to obtain the representative centroids, the fast classification/prediction of EP-TSK-FK on the testing samples is realized by using the$k$-nearest neighbor (KNN) method on the representative centroids with the original features; and 4) enhanced classification performance by the IFCM & KNN method is theoretically revealed, and the experimental results on the benchmarking datasets indicate the effectiveness of EP-TSK-FK and its parallel learning method in the sense of enhanced classification performance, running time, and interpretability.

It is well known that deep learning is one of the most important methods for data mining. With the development of the fifth-generation mobile networks (5G) and the Internet of Things (IoT), the large volume of data collected in IoTs provides a new way to improve the capability of deep learning. Due to privacy, bandwidth, and legal concerns, it is impractical to send the data to a server or the cloud. The computing power of mobile devices makes it possible to process the data. Therefore, this article focuses on training these models in mobile devices. To solve the challenges, including unreliable networks, constrained resources, and slow convergence, we let multiple mobile devices learn a shared model collaboratively. We propose a novel architecture, GREAT, where each node chooses partners to share local model parameters according to link reliability. To balance the constrained resources and learning effectiveness, an optimization problem is developed by taking the reliability threshold as the variable of controlling the resources’ overhead. To implement this architecture, a dynamic control algorithm called Alpha-GossipSGD has been proposed. Its performance is evaluated by extensive experiments, which show that Alpha-GossipSGD can realize stable learning effectiveness over unreliable networks with constrained resources.

Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph‐based extreme learning machine method (G‐ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.

In this paper, an interpretable fuzzy deep belief network (DBN)-based classifier called deep belief networks-based Takagi-Sugeno-Kang fuzzy classifier (DBN-TSK-FC) is created for indoor user movement prediction in ambient assisted living applications. With its promising classification performance, DBN-TSK-FC features sharing both the powerful neural representation ability of a DBN and the strong uncertainty-handling capability of an interpretable fuzzy representation. On the one hand, DBN-TSK-FC builds its interpretable fuzzy representation in a hierarchical way by applying the classical fuzzy clustering algorithm FCM to obtain fuzzy partitions on the training dataset. Then, it forms interpretable antecedent parts of fuzzy rules as the corresponding fuzzy representation. On the other hand, DBN-TSK-FC builds its DBN-based neural representation in the other hierarchical way. That is, it applies the existing unsupervised DBN pretraining on the training dataset and then takes the neural representation of all the hidden nodes in the top layer of the corresponding DBN as the set of consequent variables of fuzzy rules. In this approach, both the interpretable fuzzy representation and the DBN-based neural representation are further fused to form the corresponding fuzzy rules quickly by using the least learning machine (LLM) on both the fuzzy rules and the labeling information of the original dataset. Therefore, DBN-TSK-FC is essentially a deep TSK fuzzy classifier from the perspective of fuzzy rules, and it indeed avoids the very slow fine-tuning training required after the unsupervised pretraining of the existing DBN learning. The experimental results on theMovementAAL_RSSdataset indicate the effectiveness of the proposed classifier DBN-TSK-FC.