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The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality. The reference codes and data can be found at https://github.com/Lattle-y/AI-recognition-for-lq-ed.git Future work will focus on improving the generalizability of the proposed framework to different quality degradation scenarios and extrapolating the application to different engineering domains. [ABSTRACT FROM AUTHOR]

Object detection is one of the most important domains in computer vision tasks, which is an important branch of artificial intelligence. It aims at finding and locating the accurate position of objects in given pictures or videos. With the development of deep learning techniques, more powerful and robust algorithms have emerged to deal with multi-scale, high-level features to overcome the limitations of traditional pipeline of object detectors. The popularity of transformer framework enables larger capacity datasets by processing self-attention mechanism, and the object detection methods have evolved into a new era. This paper first reviews traditional object detection pipeline and brief history of deep learning, afterwards it focuses on the classification of deep learning-based object detection methods covering Convolution Neural Network based and transformer-based methods. Commonly used datasets and metrics are also covered in the next part. The Convolution Neural Network based methods mainly contain two-stage and one-stage detectors, Convolution Neural Network is the underlying structure of these methods convolutional stages are fundamental parts. Transformer-based models convert traditional object detection issues into end-to-end detection, which is widely used in dealing with images. Finally, the promising future of object detection areas are listed to show guidance on future work. [Display omitted] [ABSTRACT FROM AUTHOR]

The significant gap between the assumed and actual degradation model will undoubtedly lead to a serious performance decrease for deep learning based image super-resolution (SR) methods. To solve this shortcoming, this paper presents an enhanced blind image super-resolution (EBSR) network based on unsupervised degradation representation learning. The proposed EBSR mainly is made up of two branching subnetworks. The first subnetwork is a residual-based degradation encoder, which is responsible to learn a high-dimensional abstract degradation representation vector from the input low-resolution (LR) image patches using contrast learning. Another branch is the degradation-aware fusion SR (DAFSR) network that completes the image SR task with the aid of the degradation representation vector learned by the encoder. To obtain an improved SR performance under various degradation settings, the degradation-aware fusion block (DAFB), the core block of DAFSR, has been elaborated, which embeds the degradation model information learned by the encoder into every layer of the network. Additionally, a shallow feature extraction block (SFEB) is constructed for DAFSR to extract global information from the LR image. The proposed EBSR can flexibly adapt to various degradation models. Extensive experimental results on synthetic datasets and real images show that the proposed EBSR is able to achieve a leading reconstruction performance over other blind SR algorithms. • A boosted degradation representation learning is proposed for blind image SR. • A new degradation encoder is designed using residual connection. • A degradation-aware fusion block is designed to obtain improved SR performance. • A shallow feature extraction block is adopted to extract global informative features. [ABSTRACT FROM AUTHOR]

Image steganography is a technique of hiding secret information inside another image, so that the secret is not visible to human eyes and can be recovered when needed. Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion. Such as Gaussian noise and lossy compression. This paper proposed a practical robust invertible network for image steganography (PRIS) to improve the robustness of image steganography, it based on invertible neural networks, and put two enhance modules before and after the extraction process with a 3-step training strategy. Moreover, rounding error is considered which is always ignored by existing methods, but actually it is unavoidable in practical. A gradient approximate function (GAF) is also proposed to overcome the undifferentiable issue of rounding distortion. Experimental results show that our method outperforms the state-of-the-art robust image steganography method in both robustness and practicability, achieved an average Peak Signal to Noise Ratio value of 34.28/32.96 on container/secret pairs under 5 different attacks. Codes are available at https://github.com/yanghangAI/PRIS , demonstration of our model in practical at http://yanghang.site/hide/. [ABSTRACT FROM AUTHOR]

In this paper, we propose the use of deep learning-based image super-resolution to accelerate structural topology optimization. Topology optimization suffers from iterative computation and a time cost that increases with the number of elements. Recently, there have been attempts to accelerate topology optimization using deep learning-based models, but they often do not address a wide range of physical conditions or require extensive training data. This highlights the need for an approach that can effectively solve different problem conditions with less training data. Our approach first starts topology optimization at a low resolution to quickly obtain an optimized structure. It is converted to the structure at a higher resolution by using an image super-resolution model. Then, the final structure is obtained by performing topology optimization at this high resolution by using the converted structure as the starting configuration. The super-resolution model learns how to transform low-resolution structural features into high-resolution ones regardless of optimization conditions. As a result, it can be applied to any problems different from the problem used to generate the data for training the model. The proposed approach's excellent acceleration and generalization performance is demonstrated for three representative problems in structural topology optimization with a small number of training datasets. Furthermore, it has been shown to be effective in the deck arch bridge problem and the natural frequency maximization problem. Finally, the proposed approach is also found to be effective on the three-dimensional cantilever beam problem. [ABSTRACT FROM AUTHOR]

Executing various production tasks is critical to the safe operation and efficient production of industrial processes. As one of them, the detection task of key quality variables directly affects the operation optimization and decision-making of industrial processes, but it is severely limited by the harsh environment and detection instruments. Therefore, the real-time prediction task of key quality variables becomes the basis for optimal control of industrial processes. To address this issue, this paper proposes a task-oriented deep learning framework based on a target-related transformer (TR-Former) network for industrial quality prediction tasks. Specifically, a new target-related self-attention (TR-SA) mechanism is developed to guide feature learning by adding attention scores between task-related target variables and other variables. As a result, the learned features in this instance will be guaranteed to be relevant to the target variable and useful for the quality prediction task. Moreover, the long-range dynamics of industrial process data can also be captured, which can further improve the prediction performance of the model. Finally, extensive experiments were conducted on two industrial processes to validate the superiority of the proposed method in terms of quality prediction tasks. The experimental results demonstrate that the proposed TR-Former method exhibits an improvement ranging from 3% to 13% in the mean absolute error indicator compared to the traditional transformer and other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the semi-supervised person re-identification (Re-ID) task. Under the semi-supervised setting, only a subset of individuals in large-scale datasets need to be labeled for training to reduce the manual annotation cost. To enhance recognition performance under this setting, we propose a novel three-stage, two-branch semi-supervised Re-ID framework. Within this framework, we implement a progressive training process that integrates three methods for training unlabeled data: identification learning based on the pseudo-label estimation technique, metric learning based on mining positive or negative pairwise relationships between samples, and feature consistency learning between views generated through various data augmentations. To make full use of the unlabeled data, the above three methods are gradually added in each training stage, and the defects of these methods are avoided. Moreover, to further improve the reliability of training samples, we design two modules for selecting reliable samples to determine which method to adopt for each unlabeled sample to reduce model performance deterioration due to incorrect pseudo-labels or incorrect sample pairs. Our framework can be combined with existing supervised methods and has less performance cost. Moreover, different components in our proposed framework can be used as plug-ins to integrate into existing semi-supervised Re-ID methods and improve their performance. Extensive experiments were conducted on two public Re-ID benchmarks that demonstrate the effectiveness of our proposed framework. [ABSTRACT FROM AUTHOR]

As the important decomposition components of insulating gas sulfur hexafluoride, the accurate quantification of SO 2 , CS 2 and H 2 S is important to determine the type and severity of internal faults in the insulating equipment. In this paper, a method combining symmetry dot pattern and deep learning is proposed for quantitative detection of multi-component gas mixtures on the ultraviolet absorption spectroscopy platform. The method is based on symmetry dot pattern analysis to fuse the local ultraviolet spectral information which is useful for identifying trace gas mixtures with overlapping absorption peaks. A deep network based on transfer learning is established to recognize the symmetry dot pattern diagrams, achieving feature representation from different scales. The outstanding performance of this method is demonstrated and compared to the existing models by receiver operating characteristic curve. Experimental results on the platform show that the proposed method can quantitatively detect the concentrations of SO 2 , H 2 S and CS 2 ternary gas mixture online. [ABSTRACT FROM AUTHOR]

Recently, research based on the camouflaged object detection (COD) task has achieved great progress, while the collaborative camouflaged object detection (CoCOD) task is still lacking. Our research focuses on the simultaneous detection and localization of the collaborative camouflaged objects, i.e., CoCOD task. We use the cooperative information between a single image and a group of camouflage images to discover cooperative camouflaged objects effectively. In this paper, we propose a collaborative cross-scale feature learning network (CCNet). Our model is characterized by two innovative constructions: We proposed an edge augmentation module (EAM), which effectively extracts edge information of the camouflaged object and integrates it with the collaborative information employed in auxiliary supervision. In addition, we design a group decoder module (GDM) to extract and merge co-camouflage information. Extensive experiments on CoCOD8K datasets demonstrate that our CCNet significantly outperforms the existing 13 state-of-the-art COD and 6 state-of-the-art collaborative salient object detection (CoSOD) methods under six widely used evaluation metrics. The code will be available at: https://github.com/zc199823/CCNet--CoCOD. [ABSTRACT FROM AUTHOR]

The existing deep learning-based image dehazing algorithms commonly employ an encoder–decoder structure to learn a direct mapping from hazy images to haze-free images. However, these state-of-the-art methods often fail to consider the varying contents of hazy images across different scenes, resulting in unsatisfactory dehazing outcomes. To address this issue, this paper attempts to integrate a novel instance-aware subnet into the classic encoder–decoder structure in order to achieve a clear separation between figure and background, conducting the selective incorporation of instance features into the dehazing network. Specifically, we introduce a novel architecture called the hybrid residual attention network, which is capable of separately extracting full-image features and instance-level features. This architecture incorporates attention mechanisms and a multi-scale dilated convolution structure, enabling adaptive perception of haze density in different scenes. Additionally, we introduce a global feature fusion subnet that employs a pixel attention structure to fuse features from the entire image and multiple individual instances, thus being aware of instance features. Compared to existing methods, our approach offers a major advantage in accurately estimating the haze density of individual instances, reducing color distortion, and mitigating noise amplification in the output images. Experimental results demonstrate that our method outperforms existing methods across different evaluation metrics and testing benchmarks. Therefore, we believe that our method will serve as a valuable addition to the current collection of artificial intelligence models and will benefit engineering applications in video surveillance and high-level computer vision tasks. [ABSTRACT FROM AUTHOR]

Accurate stock movement prediction is essential to profit from the stock market. However, this task is challenging due to the complexity and non-stationary nature of the market. Deep learning methods have obtained more attention and success in mining price movement patterns.However, some limitations affect their performances. In general, the stock market is ever-changing, and many factors affect stock movement, so capturing the stock movement patterns is hard without enough prior information. To tackle it, we consider employing economic facts to help improve the deep learning method. In this paper, we propose a novel Hierarchical Deep learning Model that fuses Economic Facts (HDMEF) to predict stock movement from the micro to the macro tiers: the individual, industry, and whole market tiers. Specifically, we present three well-designed modules to separately model them based on the Capital Asset Pricing Model (CAPM), the herding effects, and the holiday effects in the stock market. Experiments on the A-share CSI300 and CSI500 indexes demonstrate that our proposed method performs best on all test phases compared with previous competitive baselines, even an absolute improvement of 2%–3% on some test phases where all the baselines act poor, proving our method is more efficient and robust in different market conditions. In addition, we do an ablation study to analyze the role of various economic effects used in our model, and the results prove that each module is helpful for prediction. [ABSTRACT FROM AUTHOR]

Accurate positioning of curtain wall frames is crucial for the automated installation of curtain wall modules. However, the current robot-based installation methods overly depend on visual guidance from operators, resulting in high costs and limiting construction efficiency. The development of deep learning has introduced an image segmentation approach that offers a new solution for the visual positioning of curtain wall frames. This paper proposes a context collaboration pyramid network to automatically segment curtain wall frames by incorporating context interaction and channel guided pyramid structure. The model adopts an "encoder-decoder" architecture with a feature interaction block strategically inserted between the encoder and decoder. Specifically, the encoder utilizes the pyramid pooling Transformer as a backbone to extract multi-level features from original RGB images. The decoder employs a channel guided pyramid convolution module to integrate multi-scale features and achieve finer prediction. Meanwhile, a context interaction fusion module between the features of adjacent levels was designed carefully to enhance the collaboration of the architecture. In addition, a benchmark dataset for the curtain wall frame segmentation task, consisting of 1547 images, was established. The dataset incorporates challenging scenarios, including strong lights, low contrast, and cluttered backgrounds. This method is evaluated on the collected dataset, and achieves an impressive accuracy of 97.30% and an F1-Score of 88.95%, outperforming other segmentation networks. Overall, the proposed method can extract target information accurately and efficiently and provide critical visual guidance for the robot, so as to promote the automatic installation level of the curtain wall module. [ABSTRACT FROM AUTHOR]

The harmful utilization of DeepFake technology poses a significant threat to public welfare, precipitating a crisis in public opinion. Existing detection methodologies, predominantly relying on convolutional neural networks and deep learning paradigms, focus on achieving high in-domain recognition accuracy amidst many forgery techniques. However, overseeing the intricate interplay between textures and artifacts results in compromised performance across diverse forgery scenarios. This paper introduces a groundbreaking framework, denoted as Texture and Artifact Detector (TAD), to mitigate the challenge posed by the limited generalization ability stemming from the mutual neglect of textures and artifacts. Specifically, our approach delves into the similarities among disparate forged datasets, discerning synthetic content based on the consistency of textures and the presence of artifacts. Furthermore, we use a model ensemble learning strategy to judiciously aggregate texture disparities and artifact patterns inherent in various forgery types, thereby enabling the model's generalization ability. Our comprehensive experimental analysis, encompassing extensive intra-dataset and cross-dataset validations along with evaluations on both video sequences and individual frames, confirms the effectiveness of TAD. The results from four benchmark datasets highlight the significant impact of the synergistic consideration of texture and artifact information, leading to a marked improvement in detection capabilities. • We build an artifact and texture inconsistency detector for deepfake detection. • Texture coherence is analyzed via a novel mask extraction method and an autoencoder. • The use of an ensemble training strategy improved the ability to generalize. • Extensive experiments verified the effectiveness of the model we built. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a well-studied topic and one of the most challenging tasks in computer vision applications, such as autonomous driving. Deep learning approaches based on convolutional neural networks (CNN) have demonstrated exceptional success on this task in recent years. Despite this success, existing approaches are plagued by higher-order inconsistencies between the ground truth images and the ones predicted by the segmentation model. This paper proposes a novel post-processing scheme based on adversarial learning to counter these inconsistencies. Such a scheme can be combined with a variety of existing CNN-based semantic segmentation networks to improve their segmentation performances. The proposed scheme is a Two-Stream Conditional Generative Adversarial Network (TScGAN), with one stream having initial semantic segmentation masks predicted by an existing CNN, while the other stream utilizes scene images to retain high-level information under a supervised residual network structure. In addition, TScGAN incorporates a novel dynamic weighting mechanism, which leads to significant and consistent gains in segmentation performance. Several comparative tests on public benchmark driving databases, including Cityscapes, Mapillary, and Berkeley DeepDrive100K, demonstrate the effectiveness of the proposed method when used with state-of-the-art CNN-based semantic segmentation models. Furthermore, the ablation experiment proved the structural rationality of our two-stream structure. The code for TScGAN could be found at https://github.com/epan-utbm/TScGAN-for-Improving-Semantic-Predictions • Explores various post-processing techniques for enhancing semantic segmentation. • Novel Two-Stream Conditional GAN enhances urban drive scene segmentation. • Enhances feature extraction by implementing dynamic weighting within streams. • Evaluates the model against existing semantic segmentation methodologies. • Tests on Cityscapes, Mapillary, BDD100K reveal major IoU gains with proposed model. [ABSTRACT FROM AUTHOR]

Cortical neural connectivity exhibits a small-world (SW) network topology. However, its role in neural information processing remains unclear. In this study, we investigate the learning performance of a Deep Echo State Network (DeepESN) that incorporates the Newman-Watts-Strogatz (NWS) graph topology as a reservoir. The short path length of the SW topology allows input information to reach output nodes faster, and the clustered arrangement of this topology can expand the range of the echo state property. Additionally, in this paper, we seek to identify the best set of these key factors, such as the spectral radius of the reservoir's weight matrix and the hyperparameters neighbors' and probabilities. We evaluate the NWS-DeepESNs on four Speech Emotion Recognition (SER) tasks, which are RAVDESS, Emo-db, SAVEE, and TESS databases. By examining the NWS-DeepESN's performance on SER tasks, we aim to determine the role of the NWS graph topology in enhancing neural information processing. The obtained results exhibited significant improvements in recognition accuracies (weighted and unweighted) with (76.45%, 75.79%), (87.89%, 87.14%), (99%, 98.62%), and (63%, 62.24%), respectively, for RAVDESS, Emo-db, SAVEE, and TESS databases. The proposed approach improves emotion recognition capabilities and yields more accurate results compared to existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Accurate exchange rate forecasting is important for the better realization of international economic transactions and international currency investments. However, it is challenging to forecast exchange rates due to their high volatility, nonlinearity, and noisy characteristics. This paper creatively proposes a novel interpretable deep learning multi-dimensional integration framework for foreign exchange rate forecasting based on shallow and deep feature selection and snapshot ensemble technology. The main factors affecting exchange rates are considered at a deep and shallow level, and the feature selection project is implemented. A cluster-based multidimensional learning paradigm is proposed. The framework clusters data and constructs predictive sub-models and then embeds snapshot ensemble techniques into models to improve the robustness of single models. Finally, the results are nonlinearly integrated by a deep learning model. In addition, three datasets and twelve comparative models were used to demonstrate the performance of the models. The empirical results show that the proposed model has the smallest mean absolute percentage error (0.407859%, 0.47134%, and 0.470167%, respectively). [ABSTRACT FROM AUTHOR]

In overhead power transmission lines, particularly in regions like natural parks where establishing a safe zone is difficult, the adoption of cross-linked polyethylene insulated covered conductors (CCs) helps prevent outages due to vegetation contact. However, these CCs are susceptible to partial discharge (PD) activity, which can degrade insulation and lead to system failures. Detecting and analyzing PD are essential for maintaining power system reliability and safety. A key challenge in PD monitoring is transmitting the large volumes of PD signal data over unreliable 2G networks, as existing compression methods either compromise on data integrity or are ineffective. This paper introduces a novel lossy compression technique utilizing an autoencoder with skip connections and correction data to address this issue. Unlike previous algorithms that struggle with noisy time series data and fail to preserve crucial anomaly information, our method reconstructs the signal without anomalies, which are subsequently restored using correction data. Achieving a compression factor of about 25 (reducing data to 4.1% of its original size), this approach maintains essential PD signal features for analysis. The effectiveness of our method is validated by three classification algorithms, showing promise for future fault detection, diagnosis, and memory space reduction. This innovative compression solution marks a significant advancement in PD data processing, offering a balanced trade-off between compression efficiency and data fidelity, and paving the way for enhanced remote monitoring in power transmission systems. [Display omitted] • New method: 1D ResNet autoencoder + correction data preserves signal anomalies. • Custom loss function to reconstruct anomaly-free signals. • High compression for large signal transmission & storage with PD • Low reconstruction error, similar classification results on reconstructed data • Optimized for single-byte signals, outperforms general scientific data compression. [ABSTRACT FROM AUTHOR]

The image noise generated by the welding process, such as arc light, splash, and smoke, brings significant challenges for the laser vision sensor-based welding robot to locate the weld seam and accurately conduct automatic welding. Currently, deep learning-based approaches surpass traditional methods in flexibility and robustness. However, their significant computational cost leads to a mismatch with the real-time requirement of automated welding. In this paper, we propose an efficient hybrid architecture of Convolutional Neural Network (CNN) and transformer, referred to as Dynamic Squeeze Network (DSNet), for real-time weld seam segmentation. More precisely, a lightweight segmentation framework is developed to fully leverage the advantages of the transformer structure without significantly increasing computational overhead. In this respect, an efficient encoder, which aims to increase its features diversity, has been designed and resulted in substantial improvement of encoding performance. Moreover, we propose a plug-and-play lightweight attention module that generates more effective attention weights by exploiting statistical information of weld seam data and introducing linear priors. Extensive experiments on weld seam images using NVIDIA GTX 1050Ti show that our approach reduces the number of parameters by 54x, decreases computational complexity by 34x, and improves inference speed by 33x compared to the baseline method TransUNet. DSNet achieves superior accuracy (78.01% IoU, 87.64% Dice) and speed performance (100 FPS) with lower model complexity and computational burden than most state-of-the-art methods. The code is available at https://github.com/hackerschen/DSNet. [ABSTRACT FROM AUTHOR]

Data-driven prognostics of systems exploit sensor measurements to predict the degradation evolution and anticipate failures, corresponding to the estimation of the remaining useful life (RUL). This task uses feature engineering to build prognostic indicators (HI) and machine learning (ML) to estimate the RUL. However, high variability in data coming from similar systems operating under different conditions negatively affects the RUL performance. Hence, this paper presents a new methodology that combines feature and ML engineering methods to provide an explainable RUL prediction. The key contributions lie in constructing efficient prognostic indicators that isolate distinct profile trajectories, enabling adaptive RUL extraction for each system. An ensemble of heterogeneous ML predictors is also trained using these indicators and RUL trajectories, effectively addressing variability issues and enhancing RUL performance. The proposed methodology is rigorously validated using NASA-provided turbofan engine data (C-MAPSS), demonstrating its performance, compared to state-of-the-art results, with improved score and accuracy of prediction. • Explainability of RUL calculation considering the variability of degradation trajectories. • Enhancing the adaptability of predictions to different operating conditions. • Handling epistemic uncertainties in RUL estimation. • High accuracy and precision of the estimated remaining useful life. [ABSTRACT FROM AUTHOR]

Owing to the development of deep learning, indoor personnel detection methods based on deep neural networks have been extensively investigated in recent years. However, more complex and deeper network structures may consume more computational resources, which seriously limits the deployment of large-scale deep neural networks on lightweight devices. In view of this, a lightweight anchor-free one-level feature indoor personnel detection method based on transformer (LAOF-IPDT) is proposed in this paper, which is deployed on two embedded devices and achieves good detection accuracy. In the feature extraction backbone, an enhanced cross-stage partial ghost convolution block with ghost convolution and channel shuffle is designed to extract shallow features. Additionally, to obtain more comprehensive global features in the high-level semantic structure, an embedded vision transformer cross-stage partial block is constructed by embedding a lightweight mobile-friendly vision transformer. For the path-aggregation neck, a lightweight feature pyramid network is proposed, which integrates multi-scale feature maps to obtain richer one-level feature representations. Subsequently, a dilated convolution group block is applied to expand the one-level feature receptive field and detection is accomplished using a one-level feature map. For the detection head, an anchor-free mechanism is applied to reduce the hyper-parameter interference of anchor boxes. Extensive experiments on four datasets indicated that the LAOF-IPDT outperforms other lightweight networks in terms of accuracy, speed, model parameters, and model size. For example, the frames per second of the LAOF-IPDT are 8.39 and 14.67 on CPU devices and Jetson Nano devices, respectively, and the mean average precision is 84.49%. [ABSTRACT FROM AUTHOR]

Precise real-time traffic flow prediction is crucial for route guidance and traffic fine control. With the development of autonomous driving, the mixed traffic flow state composed of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HVs) provides new insight into traffic flow prediction. In this paper, we innovatively consider the interaction between heterogeneous traffic flow as well as the mutual effect of traffic flow on different lanes and develop a hybrid model based on deep learning for urban expressway lane-level mixed traffic flow prediction, including three modules. First, the feature selection module is applied to screen the features with a high spatio-temporal correlation to the prediction object and construct the input matrix. Then, it is input to the feature attention module to quantify the importance of the input features on the prediction object, thereby assigning attention weights. Finally, the spatio-temporal information fusion module is adopted to capture the global spatio-temporal dynamics of traffic flow at horizontal and vertical spatial scales, as well as learn the complex coupling characteristics of heterogeneous traffic flow, thus obtaining predictions. An urban expressway mixed traffic flow simulation environment is built to collect experimental datasets for prediction accuracy evaluation. The results indicate that the proposed model outperforms the benchmarks in single-step and multi-step mixed traffic flow predictions on each lane. Furthermore, the proposed model shows the best performance and strong robustness under different penetration rates of connected automated vehicles. [ABSTRACT FROM AUTHOR]

Ship Time Headway (STH) is used in maritime navigation to describe the time interval between the arrivals of two consecutive ships in the same water area. This measurement may offer a straightforward way to gauge the frequency of ship traffic and the likelihood of congestion in a particular area. STH is an important factor in understanding and managing the dynamics of ship movements in busy waterways. This paper introduces a hybrid deep learning method for predicting STH in time domain. The method integrates the Seasonal-Trend Decomposition using Loess (STL), Multi-head Self-Attention (MSA) mechanism into Long Short-Term Memory (LSTM) neural network. The STH dataset was extracted from the Automatic Identification System (AIS) through ship trajectory spatial motion, and the seasonal, trend and residual components of the decomposition were then determined from the STH dataset using the STL algorithms. MSA-LSTM is adopted to comprehensively capture the evolving patterns of STH from the sequence. Comparison studies with existing methods demonstrate the accuracy and robustness of the predictions provided by this method, indicating that the proposed method outperforms other models in terms of prediction performance and learning capabilities. By predicting STH, the method offers potential to assist maritime traffic managers and navigators in assessing ship flow, thereby enabling them to make informed decisions on navigation safety and efficiency. [ABSTRACT FROM AUTHOR]

In the face of uncontrolled offensive content on social media, automated detection emerges as a critical need. This paper tackles this challenge by proposing a novel approach for identifying offensive language in multilingual, code-mixed, and script-mixed settings. The study presents a novel multilingual hybrid dataset constructed by merging diverse monolingual and bilingual resources. Further, we systematically evaluate the impact of input representations (Word2Vec, Global Vectors for Word Representation (or GloVe), Bidirectional Encoder Representations from Transformers (or BERT), and uniform initialization) and deep learning models (Convolutional Neural Network (or CNN), Bidirectional Long Short Term Memory (or Bi-LSTM), Bi-LSTM-Attention, and fine-tuned BERT) on detection accuracy. Our comprehensive experiments on a dataset of 42,560 social media comments from five languages (English, Hindi, German, Tamil, and Malayalam) reveal the superiority of fine-tuned BERT. Notably, it achieves a macro average F 1 -score of 0.79 for monolingual tasks and an impressive 0.86 for code-mixed and script-mixed tasks. These findings significantly advance offensive language detection methodologies and shed light on the complex dynamics of multilingual social media, paving the way for more inclusive and safer online communities. [ABSTRACT FROM AUTHOR]

Efficient forecasting of ship order books holds immense significance in the maritime industry, enabling companies to optimize their operations, allocate resources effectively, and make informed decisions. However, volatile characteristics within historical order books pose challenges in achieving reliable, intelligent, and precise forecasts. This paper presents a novel ensemble deep random vector functional link (edRVFL) algorithm to anticipate future ship order book dynamics. The edRVFL leverages deep feature extraction and ensemble learning to enhance forecasting performance. To further elevate its capabilities, we introduce a discontinuous and parsimonious embedding strategy, which deviates from the conventional dense collection of continuous time steps used in vanilla edRVFL. This parsimonious embedding approach limits the model's complexity and boosts its generalization ability. We extensively evaluate the proposed method using ship order book data, and comparative studies demonstrate its superiority over alternative approaches. Our proposed edRVFL offers a promising solution for accurate and efficient ship order book forecasting, making it a valuable asset in the maritime industry's decision-making processes. The source codes utilized in this research are openly available on GitHub at the following link: https://github.com/crkkkaa/Ship-order-book-forecasting-by-an-ensemble-deep-parsimonious-random-vector-functional-link-network-. • A novel ensemble deep RVFL to anticipate ship order book dynamics is proposed. • We propose a parsimonious embedding algorithm to optimize temporal patterns. • The direct links ablation study is conducted for comparison. [ABSTRACT FROM AUTHOR]

Premium maize varieties are the focus of attention of farmers, breeders, food manufacturers, and people in other industries. Maize seed fraud causes huge financial losses to these industries and many varieties are difficult to distinguish due to their similar appearance. Hyperspectral imaging, as a powerful tool for rapid non-destructive testing, combined with traditional pattern recognition/classification algorithms, has had many successful reports in seed variety identification. In practice, however, fake varieties are too complex to enumerate and a new fake variety may be developed at any time, posing a serious challenge to existing variety identification models. In view of this, this paper proposes a deep one-class learning (OCL) network for seed fraud detection. Specifically, it trains a hypersphere with minimum-volume that can enclose the real variety and isolate all fake varieties outside the hypersphere. In order to improve the performance and stability of the model, the spectral and spatial information of seeds are fused, and a band attention module is used to amplify the weights of the effective bands to suppress the interference from redundant bands. The experimental results show that our method has a mean accuracy of 93.70% for receiving real varieties and 94.28% for rejecting fake varieties, which is superior to several existing state-of-the-art OCL models. • Hyperspectral imaging combined with one-class learning for seed fraud detection. • A one-class classifier (OCC) base on spatial and spectral information is proposed. • The OCC trains a hypersphere to receive the real variety and reject fake varieties. • Experimental results show that our method is more stable and performs better. • Our method is also suitable for fraud detection of other food and agro-products. [ABSTRACT FROM AUTHOR]

Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to analyze data. In both, the data is stored centrally. The data involved may be sensitive and leakage may incur consequences. Applications dealing with intimate data, with critical results, cannot afford this risk and are termed Data-Sensitive Applications (DSA). Some examples are healthcare, finance, etc. The data required for DSA cannot be stored centrally due to large amounts, or isolated data islands. The ML and DL techniques following a data-centralized approach have difficulties in handling the scattered data frequently associated with DSA. Federated Learning (FL) acknowledges the scattered data and provides a more secure and efficient way to analyze such data. This motivates previously reluctant entities like banks to collaborate for variety and quantity of data. Most DSA transitioned to FL, but the migration is not without concerns. These include communication costs, heterogeneity, and malicious attacks. In this paper, we deeply analyze the role of FL in DSA and provide a taxonomy for the studies and implementations of FL. Then we provide an insight into DSA covering works in healthcare and finance. A glance is provided at attempts in non-DSA with possible DSA applications. Finally, we discuss FL's open issues and challenges with their possible solutions. [ABSTRACT FROM AUTHOR]

Deep learning has gained significant success in fault diagnosis. However, the number of gearbox health samples is inevitably much larger than that of fault samples in real-world engineering, which severely limits the diagnostic performance of such methods. Thus, this paper put forward a dynamic normalization supervised contrastive network (DNSCN) with a multiscale compound attention mechanism to recognize imbalanced gearbox faults. First, a multiscale adaptive feature extractor (MAFE) possessing branch weight adjustment capability has been devised to serve as a contrastive learning backbone to effectively mine signal features. Second, a multiscale compound attention mechanism is designed to reweight the features from the MAFE, thus improving the accuracy and confidence of fault recognition. Third, a dynamic normalized supervised contrastive loss function for imbalanced scenarios is presented. It balances the contributions of minority and hard-to-classify samples in the loss function using class normalization and dynamic adjustment based on the training accuracy, respectively. DNSCN achieved accuracies of 91.58% and 90.96% on two gearbox datasets with extreme imbalance ratios, which proved the superior performance of this approach. • The supervised contrastive learning is modified to deal with the gearbox class imbalance problem from a new perspective • A multiscale adaptive feature extractor is designed as the backbone of contrastive learning to efficiently mine features in imbalanced signals. • A multiscale attention compound mechanism is presented to enhance further the importance of critical features extracted from the contrastive learning backbone, thereby improving fault identification accuracy. • A dynamic normalized supervised contrastive learning method is devised to cope with the effect of class-imbalanced samples in fault diagnosis. • Two gearbox experiments have been implemented to demonstrate that our method outperforms the most current advanced intelligent methods under severe data type imbalance. [ABSTRACT FROM AUTHOR]

Operational forecasting often requires predicting collections of related, multivariate time series data that are high-dimensional in nature. This can be tackled by fitting a single function to all series (global approach) or assuming each series as a separate prediction problem and fitting one function to each (local approach) – the global–local trade-off. Deep learning models inspired by different data generation processes aim to combine the benefits of global and local approaches. Specifically, these frequently propose feeding the statistical expressiveness of classical local models into more complex global networks. Following recent trends in neural networks, the theoretical foundations of these hybrid models can also explain the surge of Transformer-based time series forecasting applications, which showcase potential benefits for the global and local equilibrium. Dynamic reinforcement learning (RL) models have also been explored to optimize the balance of global and local signals in general prediction problems, frequently through Q-learning algorithms. RL models can be proposed to dynamically adjust the influence of global versus local information to improve predictive performance at both scales. This paper conducted a concise literature review focused on these two research streams to optimize the balance between globality and locality in forecasting collections of time series. It focuses on their evolution across time and hints at opportunities to close some of the research gaps by intersecting both propositions. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyzes (PRISMA) guidelines and achieved a selection of 143 publications since 2000. The main findings reveal that global models have achieved strong expressiveness in capturing the most complex structural patterns while still enabling probabilistic outcomes to be delivered through uncertainty estimates. On the other hand, RL based methods depict great benefits in mitigating the risks of generalization by imprinting contextual diversity when predicting each step ahead for each series. Within those, the adoption of other computational learning or evolutionary-based methods (e.g. Genetic Programming) to improve the parametrization of the learning policies is also highlighted as an area of future work yet to be uncovered. This review advances knowledge at the intersection of two distinct yet potentially complementary research areas, identifying opportunities to combine different methodological approaches in addressing the global–local trade-off in forecasting collections of time series. This is achieved by surfacing shared limitations in current research and presenting avenues for integrating distinct methodologies, namely by further developing the theoretical underpinnings of reinforcement learning techniques. With this work we seek to enhance understanding of the relevant research landscape and help inform future solutions by establishing a foundation for collaborative work. [Display omitted] • Review of global and local time series forecasting approaches including Deep Learning (DL) models combining global and local signals and Dynamic Reinforcement Learning (RL) methods optimizing this trade-off • Identification of opportunities to combine distinct forecasting methodologies like global DL models and RL based local modeling to address the global–local trade-off. • Search for ways to integrate RL techniques for time series forecasting by further developing their theoretical underpinnings based on limitations found in current research. • Main findings show global models achieve strong expressiveness capturing complex patterns while enabling probabilistic outcomes, and RL methods mitigate risks when predicting each step for each series [ABSTRACT FROM AUTHOR]

Image steganography is a technique of hiding secret information inside another image, so that the secret is not visible to human eyes and can be recovered when needed. Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion. Such as Gaussian noise and lossy compression. This paper proposed a practical robust invertible network for image steganography (PRIS) to improve the robustness of image steganography, it based on invertible neural networks, and put two enhance modules before and after the extraction process with a 3-step training strategy. Moreover, rounding error is considered which is always ignored by existing methods, but actually it is unavoidable in practical. A gradient approximate function (GAF) is also proposed to overcome the undifferentiable issue of rounding distortion. Experimental results show that our method outperforms the state-of-the-art robust image steganography method in both robustness and practicability, achieved an average Peak Signal to Noise Ratio value of 34.28/32.96 on container/secret pairs under 5 different attacks. Codes are available at https://github.com/yanghangAI/PRIS , demonstration of our model in practical at http://yanghang.site/hide/. [ABSTRACT FROM AUTHOR]

In this paper, we propose the use of deep learning-based image super-resolution to accelerate structural topology optimization. Topology optimization suffers from iterative computation and a time cost that increases with the number of elements. Recently, there have been attempts to accelerate topology optimization using deep learning-based models, but they often do not address a wide range of physical conditions or require extensive training data. This highlights the need for an approach that can effectively solve different problem conditions with less training data. Our approach first starts topology optimization at a low resolution to quickly obtain an optimized structure. It is converted to the structure at a higher resolution by using an image super-resolution model. Then, the final structure is obtained by performing topology optimization at this high resolution by using the converted structure as the starting configuration. The super-resolution model learns how to transform low-resolution structural features into high-resolution ones regardless of optimization conditions. As a result, it can be applied to any problems different from the problem used to generate the data for training the model. The proposed approach's excellent acceleration and generalization performance is demonstrated for three representative problems in structural topology optimization with a small number of training datasets. Furthermore, it has been shown to be effective in the deck arch bridge problem and the natural frequency maximization problem. Finally, the proposed approach is also found to be effective on the three-dimensional cantilever beam problem. [ABSTRACT FROM AUTHOR]

As the important decomposition components of insulating gas sulfur hexafluoride, the accurate quantification of SO 2 , CS 2 and H 2 S is important to determine the type and severity of internal faults in the insulating equipment. In this paper, a method combining symmetry dot pattern and deep learning is proposed for quantitative detection of multi-component gas mixtures on the ultraviolet absorption spectroscopy platform. The method is based on symmetry dot pattern analysis to fuse the local ultraviolet spectral information which is useful for identifying trace gas mixtures with overlapping absorption peaks. A deep network based on transfer learning is established to recognize the symmetry dot pattern diagrams, achieving feature representation from different scales. The outstanding performance of this method is demonstrated and compared to the existing models by receiver operating characteristic curve. Experimental results on the platform show that the proposed method can quantitatively detect the concentrations of SO 2 , H 2 S and CS 2 ternary gas mixture online. [ABSTRACT FROM AUTHOR]

Executing various production tasks is critical to the safe operation and efficient production of industrial processes. As one of them, the detection task of key quality variables directly affects the operation optimization and decision-making of industrial processes, but it is severely limited by the harsh environment and detection instruments. Therefore, the real-time prediction task of key quality variables becomes the basis for optimal control of industrial processes. To address this issue, this paper proposes a task-oriented deep learning framework based on a target-related transformer (TR-Former) network for industrial quality prediction tasks. Specifically, a new target-related self-attention (TR-SA) mechanism is developed to guide feature learning by adding attention scores between task-related target variables and other variables. As a result, the learned features in this instance will be guaranteed to be relevant to the target variable and useful for the quality prediction task. Moreover, the long-range dynamics of industrial process data can also be captured, which can further improve the prediction performance of the model. Finally, extensive experiments were conducted on two industrial processes to validate the superiority of the proposed method in terms of quality prediction tasks. The experimental results demonstrate that the proposed TR-Former method exhibits an improvement ranging from 3% to 13% in the mean absolute error indicator compared to the traditional transformer and other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the semi-supervised person re-identification (Re-ID) task. Under the semi-supervised setting, only a subset of individuals in large-scale datasets need to be labeled for training to reduce the manual annotation cost. To enhance recognition performance under this setting, we propose a novel three-stage, two-branch semi-supervised Re-ID framework. Within this framework, we implement a progressive training process that integrates three methods for training unlabeled data: identification learning based on the pseudo-label estimation technique, metric learning based on mining positive or negative pairwise relationships between samples, and feature consistency learning between views generated through various data augmentations. To make full use of the unlabeled data, the above three methods are gradually added in each training stage, and the defects of these methods are avoided. Moreover, to further improve the reliability of training samples, we design two modules for selecting reliable samples to determine which method to adopt for each unlabeled sample to reduce model performance deterioration due to incorrect pseudo-labels or incorrect sample pairs. Our framework can be combined with existing supervised methods and has less performance cost. Moreover, different components in our proposed framework can be used as plug-ins to integrate into existing semi-supervised Re-ID methods and improve their performance. Extensive experiments were conducted on two public Re-ID benchmarks that demonstrate the effectiveness of our proposed framework. [ABSTRACT FROM AUTHOR]

The existing deep learning-based image dehazing algorithms commonly employ an encoder–decoder structure to learn a direct mapping from hazy images to haze-free images. However, these state-of-the-art methods often fail to consider the varying contents of hazy images across different scenes, resulting in unsatisfactory dehazing outcomes. To address this issue, this paper attempts to integrate a novel instance-aware subnet into the classic encoder–decoder structure in order to achieve a clear separation between figure and background, conducting the selective incorporation of instance features into the dehazing network. Specifically, we introduce a novel architecture called the hybrid residual attention network, which is capable of separately extracting full-image features and instance-level features. This architecture incorporates attention mechanisms and a multi-scale dilated convolution structure, enabling adaptive perception of haze density in different scenes. Additionally, we introduce a global feature fusion subnet that employs a pixel attention structure to fuse features from the entire image and multiple individual instances, thus being aware of instance features. Compared to existing methods, our approach offers a major advantage in accurately estimating the haze density of individual instances, reducing color distortion, and mitigating noise amplification in the output images. Experimental results demonstrate that our method outperforms existing methods across different evaluation metrics and testing benchmarks. Therefore, we believe that our method will serve as a valuable addition to the current collection of artificial intelligence models and will benefit engineering applications in video surveillance and high-level computer vision tasks. [ABSTRACT FROM AUTHOR]

Recently, research based on the camouflaged object detection (COD) task has achieved great progress, while the collaborative camouflaged object detection (CoCOD) task is still lacking. Our research focuses on the simultaneous detection and localization of the collaborative camouflaged objects, i.e., CoCOD task. We use the cooperative information between a single image and a group of camouflage images to discover cooperative camouflaged objects effectively. In this paper, we propose a collaborative cross-scale feature learning network (CCNet). Our model is characterized by two innovative constructions: We proposed an edge augmentation module (EAM), which effectively extracts edge information of the camouflaged object and integrates it with the collaborative information employed in auxiliary supervision. In addition, we design a group decoder module (GDM) to extract and merge co-camouflage information. Extensive experiments on CoCOD8K datasets demonstrate that our CCNet significantly outperforms the existing 13 state-of-the-art COD and 6 state-of-the-art collaborative salient object detection (CoSOD) methods under six widely used evaluation metrics. The code will be available at: https://github.com/zc199823/CCNet--CoCOD. [ABSTRACT FROM AUTHOR]

Accurate stock movement prediction is essential to profit from the stock market. However, this task is challenging due to the complexity and non-stationary nature of the market. Deep learning methods have obtained more attention and success in mining price movement patterns.However, some limitations affect their performances. In general, the stock market is ever-changing, and many factors affect stock movement, so capturing the stock movement patterns is hard without enough prior information. To tackle it, we consider employing economic facts to help improve the deep learning method. In this paper, we propose a novel Hierarchical Deep learning Model that fuses Economic Facts (HDMEF) to predict stock movement from the micro to the macro tiers: the individual, industry, and whole market tiers. Specifically, we present three well-designed modules to separately model them based on the Capital Asset Pricing Model (CAPM), the herding effects, and the holiday effects in the stock market. Experiments on the A-share CSI300 and CSI500 indexes demonstrate that our proposed method performs best on all test phases compared with previous competitive baselines, even an absolute improvement of 2%–3% on some test phases where all the baselines act poor, proving our method is more efficient and robust in different market conditions. In addition, we do an ablation study to analyze the role of various economic effects used in our model, and the results prove that each module is helpful for prediction. [ABSTRACT FROM AUTHOR]

Accurate positioning of curtain wall frames is crucial for the automated installation of curtain wall modules. However, the current robot-based installation methods overly depend on visual guidance from operators, resulting in high costs and limiting construction efficiency. The development of deep learning has introduced an image segmentation approach that offers a new solution for the visual positioning of curtain wall frames. This paper proposes a context collaboration pyramid network to automatically segment curtain wall frames by incorporating context interaction and channel guided pyramid structure. The model adopts an "encoder-decoder" architecture with a feature interaction block strategically inserted between the encoder and decoder. Specifically, the encoder utilizes the pyramid pooling Transformer as a backbone to extract multi-level features from original RGB images. The decoder employs a channel guided pyramid convolution module to integrate multi-scale features and achieve finer prediction. Meanwhile, a context interaction fusion module between the features of adjacent levels was designed carefully to enhance the collaboration of the architecture. In addition, a benchmark dataset for the curtain wall frame segmentation task, consisting of 1547 images, was established. The dataset incorporates challenging scenarios, including strong lights, low contrast, and cluttered backgrounds. This method is evaluated on the collected dataset, and achieves an impressive accuracy of 97.30% and an F1-Score of 88.95%, outperforming other segmentation networks. Overall, the proposed method can extract target information accurately and efficiently and provide critical visual guidance for the robot, so as to promote the automatic installation level of the curtain wall module. [ABSTRACT FROM AUTHOR]

Precise real-time traffic flow prediction is crucial for route guidance and traffic fine control. With the development of autonomous driving, the mixed traffic flow state composed of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HVs) provides new insight into traffic flow prediction. In this paper, we innovatively consider the interaction between heterogeneous traffic flow as well as the mutual effect of traffic flow on different lanes and develop a hybrid model based on deep learning for urban expressway lane-level mixed traffic flow prediction, including three modules. First, the feature selection module is applied to screen the features with a high spatio-temporal correlation to the prediction object and construct the input matrix. Then, it is input to the feature attention module to quantify the importance of the input features on the prediction object, thereby assigning attention weights. Finally, the spatio-temporal information fusion module is adopted to capture the global spatio-temporal dynamics of traffic flow at horizontal and vertical spatial scales, as well as learn the complex coupling characteristics of heterogeneous traffic flow, thus obtaining predictions. An urban expressway mixed traffic flow simulation environment is built to collect experimental datasets for prediction accuracy evaluation. The results indicate that the proposed model outperforms the benchmarks in single-step and multi-step mixed traffic flow predictions on each lane. Furthermore, the proposed model shows the best performance and strong robustness under different penetration rates of connected automated vehicles. [ABSTRACT FROM AUTHOR]

Ship Time Headway (STH) is used in maritime navigation to describe the time interval between the arrivals of two consecutive ships in the same water area. This measurement may offer a straightforward way to gauge the frequency of ship traffic and the likelihood of congestion in a particular area. STH is an important factor in understanding and managing the dynamics of ship movements in busy waterways. This paper introduces a hybrid deep learning method for predicting STH in time domain. The method integrates the Seasonal-Trend Decomposition using Loess (STL), Multi-head Self-Attention (MSA) mechanism into Long Short-Term Memory (LSTM) neural network. The STH dataset was extracted from the Automatic Identification System (AIS) through ship trajectory spatial motion, and the seasonal, trend and residual components of the decomposition were then determined from the STH dataset using the STL algorithms. MSA-LSTM is adopted to comprehensively capture the evolving patterns of STH from the sequence. Comparison studies with existing methods demonstrate the accuracy and robustness of the predictions provided by this method, indicating that the proposed method outperforms other models in terms of prediction performance and learning capabilities. By predicting STH, the method offers potential to assist maritime traffic managers and navigators in assessing ship flow, thereby enabling them to make informed decisions on navigation safety and efficiency. [ABSTRACT FROM AUTHOR]

In the face of uncontrolled offensive content on social media, automated detection emerges as a critical need. This paper tackles this challenge by proposing a novel approach for identifying offensive language in multilingual, code-mixed, and script-mixed settings. The study presents a novel multilingual hybrid dataset constructed by merging diverse monolingual and bilingual resources. Further, we systematically evaluate the impact of input representations (Word2Vec, Global Vectors for Word Representation (or GloVe), Bidirectional Encoder Representations from Transformers (or BERT), and uniform initialization) and deep learning models (Convolutional Neural Network (or CNN), Bidirectional Long Short Term Memory (or Bi-LSTM), Bi-LSTM-Attention, and fine-tuned BERT) on detection accuracy. Our comprehensive experiments on a dataset of 42,560 social media comments from five languages (English, Hindi, German, Tamil, and Malayalam) reveal the superiority of fine-tuned BERT. Notably, it achieves a macro average F 1 -score of 0.79 for monolingual tasks and an impressive 0.86 for code-mixed and script-mixed tasks. These findings significantly advance offensive language detection methodologies and shed light on the complex dynamics of multilingual social media, paving the way for more inclusive and safer online communities. [ABSTRACT FROM AUTHOR]

Efficient forecasting of ship order books holds immense significance in the maritime industry, enabling companies to optimize their operations, allocate resources effectively, and make informed decisions. However, volatile characteristics within historical order books pose challenges in achieving reliable, intelligent, and precise forecasts. This paper presents a novel ensemble deep random vector functional link (edRVFL) algorithm to anticipate future ship order book dynamics. The edRVFL leverages deep feature extraction and ensemble learning to enhance forecasting performance. To further elevate its capabilities, we introduce a discontinuous and parsimonious embedding strategy, which deviates from the conventional dense collection of continuous time steps used in vanilla edRVFL. This parsimonious embedding approach limits the model's complexity and boosts its generalization ability. We extensively evaluate the proposed method using ship order book data, and comparative studies demonstrate its superiority over alternative approaches. Our proposed edRVFL offers a promising solution for accurate and efficient ship order book forecasting, making it a valuable asset in the maritime industry's decision-making processes. The source codes utilized in this research are openly available on GitHub at the following link: https://github.com/crkkkaa/Ship-order-book-forecasting-by-an-ensemble-deep-parsimonious-random-vector-functional-link-network-. • A novel ensemble deep RVFL to anticipate ship order book dynamics is proposed. • We propose a parsimonious embedding algorithm to optimize temporal patterns. • The direct links ablation study is conducted for comparison. [ABSTRACT FROM AUTHOR]

Premium maize varieties are the focus of attention of farmers, breeders, food manufacturers, and people in other industries. Maize seed fraud causes huge financial losses to these industries and many varieties are difficult to distinguish due to their similar appearance. Hyperspectral imaging, as a powerful tool for rapid non-destructive testing, combined with traditional pattern recognition/classification algorithms, has had many successful reports in seed variety identification. In practice, however, fake varieties are too complex to enumerate and a new fake variety may be developed at any time, posing a serious challenge to existing variety identification models. In view of this, this paper proposes a deep one-class learning (OCL) network for seed fraud detection. Specifically, it trains a hypersphere with minimum-volume that can enclose the real variety and isolate all fake varieties outside the hypersphere. In order to improve the performance and stability of the model, the spectral and spatial information of seeds are fused, and a band attention module is used to amplify the weights of the effective bands to suppress the interference from redundant bands. The experimental results show that our method has a mean accuracy of 93.70% for receiving real varieties and 94.28% for rejecting fake varieties, which is superior to several existing state-of-the-art OCL models. • Hyperspectral imaging combined with one-class learning for seed fraud detection. • A one-class classifier (OCC) base on spatial and spectral information is proposed. • The OCC trains a hypersphere to receive the real variety and reject fake varieties. • Experimental results show that our method is more stable and performs better. • Our method is also suitable for fraud detection of other food and agro-products. [ABSTRACT FROM AUTHOR]

Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to analyze data. In both, the data is stored centrally. The data involved may be sensitive and leakage may incur consequences. Applications dealing with intimate data, with critical results, cannot afford this risk and are termed Data-Sensitive Applications (DSA). Some examples are healthcare, finance, etc. The data required for DSA cannot be stored centrally due to large amounts, or isolated data islands. The ML and DL techniques following a data-centralized approach have difficulties in handling the scattered data frequently associated with DSA. Federated Learning (FL) acknowledges the scattered data and provides a more secure and efficient way to analyze such data. This motivates previously reluctant entities like banks to collaborate for variety and quantity of data. Most DSA transitioned to FL, but the migration is not without concerns. These include communication costs, heterogeneity, and malicious attacks. In this paper, we deeply analyze the role of FL in DSA and provide a taxonomy for the studies and implementations of FL. Then we provide an insight into DSA covering works in healthcare and finance. A glance is provided at attempts in non-DSA with possible DSA applications. Finally, we discuss FL's open issues and challenges with their possible solutions. [ABSTRACT FROM AUTHOR]

Crude oil prices are susceptible to a variety of elements, including military, political, financial, and diplomatic forces, resulting in price fluctuations that are random, abrupt, and chaotic. Based on the chaotic nature of crude oil price series, this paper proposes a model for predicting crude oil prices that incorporates variational modal decomposition algorithm (VMD), phase space reconstruction technique (PSR), convolutional neural network (CNN), and bidirectional long and short-term memory network (BiLSTM). Specifically, the noises in the original data are removed using VMD. Next, the crude oil price is reconstructed using PSR. Then the reconstructed and denoised phase space is fed into the CNN-BiLSTM model for multi-step predictions. The empirical results show that the proposed model achieves the lowest MAPEs as 0.662%, 2.283%, 3.829%, and 4.44% and the lowest MSEs as 0.335, 3.942, 10.047 and 13.681. The Diebold Mariano (DM) test also demonstrates the proposed model's superiority in the domain of crude oil price forecasting. [ABSTRACT FROM AUTHOR]

Deep learning has gained significant success in fault diagnosis. However, the number of gearbox health samples is inevitably much larger than that of fault samples in real-world engineering, which severely limits the diagnostic performance of such methods. Thus, this paper put forward a dynamic normalization supervised contrastive network (DNSCN) with a multiscale compound attention mechanism to recognize imbalanced gearbox faults. First, a multiscale adaptive feature extractor (MAFE) possessing branch weight adjustment capability has been devised to serve as a contrastive learning backbone to effectively mine signal features. Second, a multiscale compound attention mechanism is designed to reweight the features from the MAFE, thus improving the accuracy and confidence of fault recognition. Third, a dynamic normalized supervised contrastive loss function for imbalanced scenarios is presented. It balances the contributions of minority and hard-to-classify samples in the loss function using class normalization and dynamic adjustment based on the training accuracy, respectively. DNSCN achieved accuracies of 91.58% and 90.96% on two gearbox datasets with extreme imbalance ratios, which proved the superior performance of this approach. • The supervised contrastive learning is modified to deal with the gearbox class imbalance problem from a new perspective • A multiscale adaptive feature extractor is designed as the backbone of contrastive learning to efficiently mine features in imbalanced signals. • A multiscale attention compound mechanism is presented to enhance further the importance of critical features extracted from the contrastive learning backbone, thereby improving fault identification accuracy. • A dynamic normalized supervised contrastive learning method is devised to cope with the effect of class-imbalanced samples in fault diagnosis. • Two gearbox experiments have been implemented to demonstrate that our method outperforms the most current advanced intelligent methods under severe data type imbalance. [ABSTRACT FROM AUTHOR]

Operational forecasting often requires predicting collections of related, multivariate time series data that are high-dimensional in nature. This can be tackled by fitting a single function to all series (global approach) or assuming each series as a separate prediction problem and fitting one function to each (local approach) – the global–local trade-off. Deep learning models inspired by different data generation processes aim to combine the benefits of global and local approaches. Specifically, these frequently propose feeding the statistical expressiveness of classical local models into more complex global networks. Following recent trends in neural networks, the theoretical foundations of these hybrid models can also explain the surge of Transformer-based time series forecasting applications, which showcase potential benefits for the global and local equilibrium. Dynamic reinforcement learning (RL) models have also been explored to optimize the balance of global and local signals in general prediction problems, frequently through Q-learning algorithms. RL models can be proposed to dynamically adjust the influence of global versus local information to improve predictive performance at both scales. This paper conducted a concise literature review focused on these two research streams to optimize the balance between globality and locality in forecasting collections of time series. It focuses on their evolution across time and hints at opportunities to close some of the research gaps by intersecting both propositions. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyzes (PRISMA) guidelines and achieved a selection of 143 publications since 2000. The main findings reveal that global models have achieved strong expressiveness in capturing the most complex structural patterns while still enabling probabilistic outcomes to be delivered through uncertainty estimates. On the other hand, RL based methods depict great benefits in mitigating the risks of generalization by imprinting contextual diversity when predicting each step ahead for each series. Within those, the adoption of other computational learning or evolutionary-based methods (e.g. Genetic Programming) to improve the parametrization of the learning policies is also highlighted as an area of future work yet to be uncovered. This review advances knowledge at the intersection of two distinct yet potentially complementary research areas, identifying opportunities to combine different methodological approaches in addressing the global–local trade-off in forecasting collections of time series. This is achieved by surfacing shared limitations in current research and presenting avenues for integrating distinct methodologies, namely by further developing the theoretical underpinnings of reinforcement learning techniques. With this work we seek to enhance understanding of the relevant research landscape and help inform future solutions by establishing a foundation for collaborative work. [Display omitted] • Review of global and local time series forecasting approaches including Deep Learning (DL) models combining global and local signals and Dynamic Reinforcement Learning (RL) methods optimizing this trade-off • Identification of opportunities to combine distinct forecasting methodologies like global DL models and RL based local modeling to address the global–local trade-off. • Search for ways to integrate RL techniques for time series forecasting by further developing their theoretical underpinnings based on limitations found in current research. • Main findings show global models achieve strong expressiveness capturing complex patterns while enabling probabilistic outcomes, and RL methods mitigate risks when predicting each step for each series [ABSTRACT FROM AUTHOR]

This paper introduces a novel parallel deep neural network for fault diagnosis of drilling pumps. It integrates the Convolutional Block Attention Module with the AlexNet and synchronizes with the Anomaly Transformer model to delve meticulously into both the time and time-frequency domains of signals. The method prioritizes the singular extraction and subsequent amalgamation of features, facilitating a detailed view of diagnostic data and mitigating the risk of interference and overfitting. The integration of the anomaly attention of the Anomaly Transformer with the features of the Convolutional Block Attention Module results in a distinctive dual attention mechanism that is critical to the methodology. This mechanism emphasizes essential features in both the time domain and the time-frequency domain, improving the accuracy of fault diagnosis. Verification with on-site data underscores the preeminence of the approach over existing models, signaling improved reliability and accuracy in diagnosing faults in drilling pumps. This meticulous approach offers promising advances in the study and application of fault diagnosis in energy equipment, demonstrating increased efficiency and refined accuracy. • A novel Parallel Deep Neural Network-Based Fault Diagnosis Framework is proposed. • Time and time-frequency domains of signals are extracted and fused for Fault Diagnosis. • Integrating the Transformer with CBAM to consider the essential features of signals. [ABSTRACT FROM AUTHOR]

This paper addresses a crucial issue in the airline industry by tackling a dynamic pricing problem in the presence of patient customers, a scenario that has gained significance due to the revenue loss of airlines caused by customers' non-myopic decision-making. To effectively capture this non-myopic characteristic, we propose a Markov decision process (MDP) including a history of offered prices as a state variable. In contrast to previous studies, distributions of customers' properties are assumed to be unknown in advance for a more realistic representation of real-world scenarios. To deal with the new challenges of the problem, we propose utilizing a specific learning framework (i.e., deep exploration-based RL) that is unexplored in this domain. The numerical experiments demonstrate that its performance can be improved on the MDP we designed and show that it outperforms the benchmark algorithm. The structures of pricing policies generated from the bootstrapped deep Q-network algorithm imply that airlines should offer high and low prices alternately from the beginning of the sales period rather than increasing prices as time goes on. We also ascertain that more frequent consecutive high-priced periods can increase airlines' revenue in environments with higher customer patience levels. [ABSTRACT FROM AUTHOR]

Fault testing in the production line of automotive traction machines is essential to ensure the desired lifetime. Since repetitive partial discharges (PDs) caused by anomalies in the insulation system lead to premature breakdowns of electrical machines, a reliable PD detection is of great importance. This paper proposes deep learning (DL) methods to improve the discrimination of PD from background noise in comparison with the state-of-the-art amplitude based PD detection in the production line. First, a systematic data extraction and labeling procedure is introduced to obtain correctly labeled datasets from arbitrary PD measurements. In addition, datasets are enhanced with low signal-to-noise ratio PD pulses by applying a special data augmentation approach. 13 different convolutional, recurrent and fully connected neural networks are compared for various time-frequency representations of the input signals. Hyperparameters for input transform, network topology and solver are optimized for all 13 combinations to ensure a fair case study. As a result, the two-dimensional convolutional neural network with continuous wavelet transform achieves the best accuracy of around 99.76% on a test dataset of PD signals originating from previously not utilized test objects. All DL models considered in this comparison outperform the state-of-the-art threshold-based PD classification. Even for PD events with an amplitude close to the noise level, the detection rate is still around 95% for the best network. Furthermore, without applying the proposed data augmentation procedure, the DL models investigated are not able to distinguish small PD pulses from noise. • Deep learning to improve partial discharge classification of electrical machines. • Rise of partial discharge detection sensitivity and decrease in false alarm rate. • Data extraction, labeling and augmentation procedure to generate reliable datasets. • Comprehensive comparison of time-frequency transforms and network architectures. [ABSTRACT FROM AUTHOR]

As the durations of the short utterances are small, it is difficult to learn sufficient information to distinguish the person, thus, short utterance speaker recognition is highly challenging. In this paper, we propose a multi-domain frame-level feature joint learning method to aggregate the discriminative information from multiple dimensions and domain, which is different domains of the speech, time-domain, frequency-domain, and spectral-domain, represent distinct physical characteristics and provide different dimension information, the time domain captures information about the temporal aspect of the physical signal, the frequency domain represents the signal strength in different frequency ranges, and the spectral domain reflects the overall information of the speech, then, based on the extracted multi-domain frame-level features, using the Multi-Fisher criterion aggregates feature parameters categorically and match the corresponding Multi-Fisher ratio weights to the feature parameters as a way to achieve effective feature aggregation and to preserve more effective information, termed Firm-Domain. Extensive experiments are carried out on short-duration text-independent speaker verification datasets derived from the VoxCeleb, SITW, and NIST SRE corpora, which contain speech samples of varying lengths and scenarios. The results demonstrate that the proposed method outperforms the state-of-the-art deep learning architectures by at least 13%, respectively, in the test set. The results of the ablation experiments demonstrate that our proposed methods can significantly outperform previous approaches. [ABSTRACT FROM AUTHOR]