The traditional image quality assessment (IQA) methods are usually based on convolutional neural networks (CNNs). For these IQA methods using CNNs, limited by the feature size of the fully connected layer, the input image needs be tailored to a pre-defined size, which usually results in destroying the original structure and content of the input image and thus reduces the accuracy of the quality assessment. In this paper, a blind image quality assessment method (named CSPP-IQA), which is based on multi-scale spatial pyramid pooling, is proposed. CSPP-IQA allows inputting the original image when assessing the image quality without any image adjustment. Moreover, by facilitating the convolutional block attention module and image understanding module, CSPP-IQA achieved better accuracy, generalization and efficiency than traditional IQA methods. The result of experiments running on real-scene IQA datasets in this study verified the effectiveness and efficiency of CSPP-IQA.

Abstract Thin‐film interference is a significant optical phenomenon. In this study, we employed the transfer matrix method to pre‐calculate the reflectance of thin‐films at visible light wavelengths. The reflectance is saved as a texture through color space transformation. This advancement has made real‐time rendering of thin‐film interference feasible. Furthermore, we proposed the implementation of shallow water equations to simulate the morphological evolution of liquid thin‐films. This approach facilitates the interpretation and prediction of behaviors and thickness variations in liquid thin‐films. We also introduced a viscosity term into the shallow water equations to more accurately simulate the behavior of thin‐films, thus facilitating the creation of authentic interference patterns.

As a high mortality disease, cancer seriously affects people's life and well-being. Reliance on pathologists to assess disease progression from pathological images is inaccurate and burdensome. Computer aided diagnosis (CAD) system can effectively assist diagnosis and make more credible decisions. However, a large number of labeled medical images that contribute to improve the accuracy of machine learning algorithm, especially for deep learning in CAD, are difficult to collect. Therefore, in this work, an improved few-shot learning method is proposed for medical image recognition. In addition, to make full use of the limited feature information in one or more samples, a feature fusion strategy is involved in our model. On the dataset of BreakHis and skin lesions, the experimental results show that our model achieved the classification accuracy of 91.22% and 71.20% respectively when only 10 labeled samples are given, which is superior to other state-of-the-art methods.

Detecting Out-of-Scope(OOS) intents in dialogue systems is a challenging technique with practical applications. As for OOS intent detection, it not only ensures the accuracy of classifying known intents but detecting OOS intents is also crucial. Current related models are limited in learning decision boundaries or setting the threshold of confidence score, which all neglect that a well-formed intent representation is a key point. Meanwhile, text extractors trained by traditional cross-entropy loss merely focus on reducing the error rate of the class to which the sample is classified. In this paper, we propose an effective feature extraction method based on deep metric learning to construct the triplet network with prior knowledge. With the constructed triplet loss, mining hard samples, which refers to the far-apart intents between the same class and close intent representations among different classes, can further obtain discriminative intent representations. In addition, we also introduce adversarial training to make intent representations more robust. Experiments on three public datasets prove the effectiveness of our proposed method of learning discriminative intent representations.

Nowadays, the concept of Internet of Everything (IoE) is becoming a hotly discussed topic, which is playing an increasingly indispensable role in modern intelligent applications. These applications are known for their real-time requirements under limited network and computing resources, thus it becomes a highly demanding task to transform and compute tremendous amount of raw data in a cloud center. The edge–cloud computing infrastructure allows a large amount of data to be processed on nearby edge nodes and then only the extracted and encrypted key features are transmitted to the data center. This offers the potential to achieve an end–edge–cloud-based big data intelligence for IoE in a typical two-stage data processing scheme, while satisfying a data security constraint. In this study, a deep-reinforcement-learning-enhanced two-stage scheduling (DRL-TSS) model is proposed to address the NP-hard problem in terms of operation complexity in end–edge–cloud Internet of Things systems, which is able to allocate computing resources within an edge-enabled infrastructure to ensure computing task to be completed with minimum cost. A presorting scheme based on Johnson’s rule is developed and applied to preprocess the two-stage tasks on multiple executors, and a DRL mechanism is developed to minimize the overall makespan based on a newly designed instant reward that takes into account the maximal utilization of each executor in edge-enabled two-stage scheduling. The performance of our method is evaluated and compared with three existing scheduling techniques, and experimental results demonstrate the ability of our proposed algorithm in achieving better learning efficiency and scheduling performance with a 1.1-approximation to the targeted optimal IoE applications.

A deep transfer learning framework adapting mixed subdomains is proposed for cross-species plant disease diagnosis. Most existing deep transfer learning studies focus on knowledge transfer between highly correlated domains. These methods may fail to deal with domains that are poorly correlated. In this study, mixed domain images were generated from source and target image groups for improving the correlation between the mixed domain (training dataset) and the target domain (testing dataset). A subdomain alignment mechanism is employed to transfer knowledge from the mixed domain to the target domain. The proposed framework captures the fine-grained information more effectively. Extensive experiments were conducted and prove that the proposed method produces a more effective result compared with existing deep transfer learning technologies for poorly related subdomains.

Physics theory integrated machine learning models enhance the interpretability and performance of artificial intelligence (AI) techniques to real-world industrial applications, such as the fault detection and diagnosis (FDD) of air handling units (AHU). Traditional machine learning-based automated FDD model demonstrates a high classification accuracy with sufficient training data samples, however, suffers from physical interpretation of the machine learning models. In this article, a physical model integrated Wasserstain generative adversarial network (WGAN) model is presented for AHU FDD with a scenario of insufficient training data samples. The proposed solution tackles the real-world problem of AHU FDD and enhances the model interpretability significantly. A transformer-WGAN model is designed to further improve the proposed FDD framework. Experimental results show that the proposed method outperforms existing AHU FDD methods with imbalanced real-world training data samples.

Inkjet printing technology can make tiles with very rich and realistic patterns, so it is widely adopted in the ceramic industry. However, the frequent nozzle blockage and inconsistent inkjet volume by inkjet printing devices, usually leads to defects such as stayguy and color blocks in the tile surface. Especially, the stayguy in complex pattern is difficult to identify by naked eyes due to it is easily covered by complex patterns and becomes invisible, this brings great challenge to tile quality inspection. Nowadays, the machine learning is employed to address the issues. The existing machine learning methods based on hand-crafted features are capable of stayguy detection of the tiles with a simple pattern, but not applicable for complex patterns due to the interference of pattern in feature extraction. The emerging deep-learning-based methods have the potential to be applied for stayguy detection with complex patterns, but cannot achieve real-time detection due to high complexity. In this paper, a lightweight hand-crafted feature enhanced convolutional neural network (named HFENet) is proposed for rapid defect detection of tile surface. First, we perform data enhancement on the original image by global histogram equalization and image addition. Second, for the special shape of stayguy which is usually vertical, we embed the extended vertical edge detection operator (Prewitt) as convolution kernel into HFENet to extract the hand-crafted vertical edge features of the test image and eliminate the interference of complex pattern in the feature extraction. Third, the 5 × 1 asymmetric convolution kernel with a dilation rate of 2 is used to improve the utilization of convolution kernel and reduce the complexity of the model. Fourth, to reach the real-time requirements, a memory access cost-aware design is proposed, which can orchestrate the number of shallow convolution layers and deep convolution layers in feature extraction. The experiments were performed on the ceramic tile image data set captured by high-resolution industrial cameras in ceramic tile production line. Experimental results show that the HFENet outperforms the state-of-the-art semantic segmentation networks (i.e., UNet, FCN-8s, SegNet, DeepLabV3+, etc.) and lightweight networks (i.e., ShuffleNet, MobileNet, and SqueezeNet). All the code and data are available at a GitHub repository (https://github.com/RobotvisionLab/HFENet).

The advancement of Internet of Things (IoT) technologies leads to a wide penetration and large-scale deployment of IoT systems across an entire city or even country. While IoT systems are capable of providing intelligent services, the large amount of data collected and processed in IoT systems also raises serious security concerns. Many research efforts have been devoted to design intelligent network intrusion detection system (NIDS) to prevent misuse of IoT data across smart applications. However, existing approaches may suffer from the issue of limited and imbalanced attack data when training the detection model, which make the system vulnerable especially for those unknown type attacks. In this study, a novel hierarchical adversarial attack (HAA) generation method is introduced to realize the level-aware black-box adversarial attack strategy, targeting the graph neural network (GNN)-based intrusion detection in IoT systems with a limited budget. By constructing a shadow GNN model, an intelligent mechanism based on a saliency map technique is designed to generate adversarial examples by effectively identifying and modifying the critical feature elements with minimal perturbations. A hierarchical node selection algorithm based on random walk with restart (RWR) is developed to select a set of more vulnerable nodes with high attack priority, considering their structural features, and overall loss changes within the targeted IoT network. The proposed HAA generation method is evaluated using the open-source data set UNSW-SOSR2019 with three baseline methods. Comparison results demonstrate its ability in degrading the classification precision by more than 30% in the two state-of-the-art GNN models, GCN and JK-Net, respectively, for NIDS in IoT environments.

Artificial intelligence (AI)-enhanced automated fault diagnosis (AFD) has become increasingly popular for chiller fault diagnosis with promising classification performance. In practice, a sufficient number of fault samples are required by the AI methods in the training phase. However, faulty training samples are generally much more difficult to be collected than normal training samples. Data augmentation is introduced in these scenarios to enhance the training data set with synthetic data. In this study, a variational autoencoder-based conditional Wasserstein GAN with gradient penalty (CWGAN-GP-VAE) is proposed to diagnose various faults for chillers. A detailed comparative study has been conducted with real-world fault data samples to verify the effectiveness and robustness of the proposed methodology.Note to Practitioners—This work attacks the fact that faulty training samples are usually much harder to be collected than the normal training samples in the practice of chiller automated fault diagnosis (AFD). Modern supervised learning chiller AFD relies on a sufficient number of faulty training samples to train the classifier. When the number of faulty training samples is insufficient, the conventional AFD methods fail to work. This study proposed a variational autoencoder-based conditional Wasserstein GAN with gradient penalty (CWGAN-GP-VAE) framework for generating synthetic faulty training samples to enrich the training data set for machine learning-based AFD methods. The proposed algorithm has been carefully designed, implemented, and practically proved to be more effective than the existing methods in the literature.

Sleep staging is an important step in analyzing sleep quality. Traditional manual analysis by psychologists is time-consuming. In this paper, we propose an automatic sleep staging model with an improved attention module and hidden Markov model (HMM). The model is driven by single-channel electroencephalogram (EEG) data. It automatically extracts features through two convolution kernels with different scales. Subsequently, an improved attention module based on Squeeze-and-Excitation Networks (SENet) will perform feature fusion. The neural network will give a preliminary sleep stage based on the learned features. Finally, an HMM will apply sleep transition rules to refine the classification. The proposed method is tested on the sleep-EDFx dataset and achieves excellent performance. The accuracy on the Fpz-Cz channel is 84.6%, and the kappa coefficient is 0.79. For the Pz-Oz channel, the accuracy is 82.3% and kappa is 0.76. The experimental results show that the attention mechanism plays a positive role in feature fusion. And our improved attention module improves the classification performance. In addition, applying sleep transition rules through HMM helps to improve performance, especially N1, which is difficult to identify.

The papers in this special section focus on machine learning for artificial intelligent-enhances healthcare and medical services. These services are always among the top concerns for humans, especially under the special situation of COVID-19 pandemic, started from early 2020. In the field of computational biology and bioinformatics, scientists seek various possibilities using computer technologies, especially artificial intelligence (AI) enhanced methods, for healthcare services and medical diagnoses. For example, over the past few years, scientists have been working hard to identify the internal relationships between gene microarrays, cells, tissues, organisms, diseases, etc., and apply the AI, machine learning and deep learning technologies looking for more innovative solutions for new diseases, such as COVID-19. In fact, nowadays, AI technology, such as the convolutional neural network (CNN), is considered has one of the most important computer technologies and has been widely applied in the fields of healthcare engineering, medical research, disease diagnosis, cancer/tumor analysis and etc.

Artificial intelligence-based air quality index (AQI) forecasting is a hot research topic in the fields of sustainable and smart industrial environment design. There are mainly two obstacles that hinder the existing machine learning (ML) and deep learning (DL) technologies providing accurate forecasting results to protect the environment, which include the intercorrelation between different AQI components and the highly volatile AQI pattern changes. In this article, a novel DL framework combining multiple nested long short term memory networks (MTMC-NLSTM) is proposed for accurate AQI forecasting enlightened with the federated learning. The performance of the proposed MTMC-NLSTM model is compared with conventional ML models, DL methods, as well as hybrid DL models. The experimental results show that the performance of the proposed method is superior to those of all compared models.

Short‐term photovoltaic (PV) energy generation forecasting models are important, stabilizing the power integration between the PV and the smart grid for artificial intelligence‐ (AI‐) driven internet of things (IoT) modeling of smart cities. With the recent development of AI and IoT technologies, it is possible for deep learning techniques to achieve more accurate energy generation forecasting results for the PV systems. Difficulties exist for the traditional PV energy generation forecasting method considering external feature variables, such as the seasonality. In this study, we propose a hybrid deep learning method that combines the clustering techniques, convolutional neural network (CNN), long short‐term memory (LSTM), and attention mechanism with the wireless sensor network to overcome the existing difficulties of the PV energy generation forecasting problem. The overall proposed method is divided into three stages, namely, clustering, training, and forecasting. In the clustering stage, correlation analysis and self‐organizing mapping are employed to select the highest relevant factors in historical data. In the training stage, a convolutional neural network, long short‐term memory neural network, and attention mechanism are combined to construct a hybrid deep learning model to perform the forecasting task. In the testing stage, the most appropriate training model is selected based on the month of the testing data. The experimental results showed significantly higher prediction accuracy rates for all time intervals compared to existing methods, including traditional artificial neural networks, long short‐term memory neural networks, and an algorithm combining long short‐term memory neural network and attention mechanism.

The papers in this special section focus on new methodologies and applications in artificial intelligence (AI) and machine learning (ML). With the recent development of machine learning (ML), artificial intelligence (AI) and cyber technologies in the field of industrial informatics, it is important to migrate the traditional businesses and services in the physical world to the digital cyber-enabled world. Cyber intelligence technologies, such as the fifth-generation (5G) mobile communication network, big data, Internet of Things (IoT), cloud computing, cognitive computing, ubiquitous computing and blockchains, enable goods, houses, services, information, and capitalization to be shared through the Internet of the Appendix]. Industrial applications, including various mechanical systems, utilities, supply chains, energy systems, power grids, infrastructures, manufactures, traffics, healthcare, and environmental issues, are partially operated or managed remotely with the influence of AI and cyber intelligence technology.

BACKGROUND: Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights. RESULTS: In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches. CONCLUSIONS: Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.

A large‐scale multi‐projector display system offers high‐resolution, high‐brightness and immersive visualisation for realistic experience to end users. It has been demonstrated to be effective tackling the conflict between the increasing demands of super‐resolution display and the resolution limitation of a single display system. However, there is still no standardisation method for curved‐surface projection screen. In this study, we propose a novel approach for calibrating multi‐projector display systems, which have curved surfaces. First, based on a detailed analysis on arbitrarily curved surfaces, we present a three‐dimensional reconstruction algorithm based on Bezier surface models. Then, for fully utilising the projection area of each projector, we propose a novel curved‐surface stitching algorithm to achieve geometry seamlessness of multi‐projector display systems. Experimental results show that by constructing local Bessel models for the curved screen, the proposed method performs better than traditional approaches, i.e. the new method achieves geometric calibration with higher accuracy. The proposed method of modelling projection screen and the corresponding automatic geometric correction scheme effectively increase the utilisation ratio of the original projection area of each projector and improve the calibration accuracy of multi‐projector system with continuous curved surface.

Discord search is an important technique for time series analysis, especially for anomaly detection . In recent years, many computational approaches of discord search were studied; however, limitation exists while only the problems with univariate time series data can be well addressed. In this study, we proposed a novel computational framework to identify discords from multivariate time series (MTS) data, namely, LRRDS (Local Recurrence Rate based Discord Search). LRRDS accurately identifies the discords by analyzing a recurrence plot, which is transformed from the original time series data. An innovative strategy was employed to improve the efficiency for pair-wise distance comparison of two subsequences . In the experimental simulations, LRRDS was applied to an extensive number of MTS datasets. Results show that the proposed approach is more efficient than existing methods, such as GDS. In conclusion, the LRRDS approach solves the adaptability problem of discord sequences in multi-dimensional space and guarantees the computational effectiveness and efficiency.

The extreme learning machine (ELM) is famous for its single hidden-layer feed-forward neural network which results in much faster learning speed comparing with traditional machine learning techniques. Moreover, extensions of ELM achieve stable classification performances for imbalanced data. In this paper, we introduce a hybrid method combining the extended Kalman filter (EKF) with cost-sensitive dissimilar ELM (CS-D-ELM). The raw data are preprocessed by EKF to produce inputs for the CS-D-ELM classifier. Experimental results show that the proposed method is more suitable for real-time fault diagnosis of air handling units than traditional approaches.

Feature selection is an important research topic for hyperspectral images (HSIs). It helps to remove the noisy or redundant features. Traditional feature selection algorithms are mostly performed within a single HSI scene (dataset). However, appearance of massive HSIs requires the feature selection problems to be considered across different HSI scenes, e.g., two HSI scenes obtained from different spots or at different time. In this case, the features are not identically distributed within two scenes due to spectral shift. To solve this problem, a cross-scene feature selection algorithm is proposed in this work for HSIs, which is based on cross-domain information gain (CDIG). The main motivation includes two factors, one is the discriminant of selected features to separate different land-cover classes, while the other is the consistency of the selected features between different scenes. Consequently, the proposed CDIG reaches a compromise between aforementioned two factors. Experimental results on two cross-scene HSI datasets show the advantages of the proposed CDIG in cross-scene feature selection problems.

Abstract Inverse kinematics (IK) equations are usually solved through approximated linearizations or heuristics. These methods lead to character animations that are unnatural looking or unstable because they do not consider both the motion coherence and limits of human joints. In this paper, we present a method based on the formulation of multi‐variate Gaussian distribution models (MGDMs), which precisely specify the soft joint constraints of a kinematic skeleton. Each distribution model is described by a covariance matrix and a mean vector representing both the joint limits and the coherence of motion of different limbs. The MGDMs are automatically learned from the motion capture data in a fast and unsupervised process. When the character is animated or posed, a Gaussian process synthesizes a new MGDM for each different vector of target positions, and the corresponding objective function is solved with Jacobian‐based IK. This makes our method practical to use and easy to insert into pre‐existing animation pipelines. Compared with previous works, our method is more stable and more precise, while also satisfying the anatomical constraints of human limbs. Our method leads to natural and realistic results without sacrificing real‐time performance.