Explore the vast range of titles available.

The demand for big data talent is rapidly increasing with the growth of the big data industry. However, there has been limited research on what employers seek in recruiting big data talent. This paper aims to apply labor market segmentation theories to the big data labor market and develop a theoretical framework to analyze the distribution of big data talent in different labor market segments. Furthermore, we develop a salary determination model to explain wage differentials. An empirical analysis is conducted using online job advertisements from a Chinese recruitment website to investigate the labor market for big data talent in China. Our findings show that there are significant differences in the demand for big data talent across different types of cities and industries. Different types of enterprises have different requirements for individual characteristics and offer various levels of big data job positions. Furthermore, our results reveal that individual, job-related and organizational characteristics are all significant predictors of salaries. These findings can provide particularly useful insights for organizations and managers in the big data industry.

The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smoke-aware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions.

Electrocardiogram (ECG) is a common non-invasive diagnostic tool for cardiovascular diseases. Adequate data is crucial in utilizing deep learning to achieve intelligent diagnosis of ECG. The existing ECG datasets almost only focus on adults and most of them do not provide cardiovascular disease diagnosis. In this study, we propose an ECG database with cardiovascular disease diagnosis for children aged 0-14 years old. This dataset is acquired from 11643 hospitalized children at the First Affiliated Hospital of Zhengzhou University from 2018 to 2024, including 14190 pediatric ECG records, of which 12334 were 12 lead and 1856 were 9 lead. The sampling rate is 500 Hz and the record length is 5-120 seconds. We followed the recommendations of AHA/ACC/HRS and the diagnostic statements in the consensus of Chinese ECG experts to encode and convert all ECG records. In this dataset, 3516 ECG records were diagnosed with cardiovascular diseases, and these labels were derived from 19 common diseases in the pediatric cardiovascular field, including myocarditis, cardiomyopathy, congenital heart disease, and Kawasaki disease.

Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset.

Biomedical image segmentation is one critical component in computer-aided system diagnosis. However, various non-automatic segmentation methods are usually designed to segment target objects with single-task driven, ignoring the potential contribution of multi-task, such as the salient object detection (SOD) task and the image segmentation task. In this paper, we propose a novel dual-task framework for white blood cell (WBC) and skin lesion (SL) saliency detection and segmentation in biomedical images, called Saliency-CCE. Saliency-CCE consists of a preprocessing of hair removal for skin lesions images, a novel colour contextual extractor (CCE) module for the SOD task and an improved adaptive threshold (AT) paradigm for the image segmentation task. In the SOD task, we perform the CCE module to extract hand-crafted features through a novel colour channel volume (CCV) block and a novel colour activation mapping (CAM) block. We first exploit the CCV block to generate a target object’s region of interest (ROI). After that, we employ the CAM block to yield a refined salient map as the final salient map from the extracted ROI. We propose a novel adaptive threshold (AT) strategy in the segmentation task to automatically segment the WBC and SL from the final salient map. We evaluate our proposed Saliency-CCE on the ISIC-2016, the ISIC-2017, and the SCISC datasets, which outperform representative state-of-the-art SOD and biomedical image segmentation approaches. Our code is available at https://github.com/zxg3017/Saliency-CCE. •A dual-task method is proposed for saliency detection and medical image segmentation.•A colour contextual extractor is proposed to tackle an object localization issue.•A novel adaptive threshold strategy is adopted for biomedical image segmentation.•Experiments show that our method outperforms several methods on three image datasets.

Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification. Specifically, we design a video augmentation module called the Super Augmentation Block (SAB). SAB adds Gaussian noise and randomly masks video frames according to historical loss on the unlabeled data for model optimization. Then, we propose a Video Cross-set Augmentation Module (VCAM) to generate diverse pseudo-label samples from the high-confidence unlabeled samples, which alleviates the mismatch of sampling experience and provides high-quality training data. Additionally, we construct a new industrial accident surveillance video dataset with frame-level annotation, namely ECA9, to evaluate our proposed method. Compared with the state-of-the-art semi-supervised learning based methods, SIAVC demonstrates outstanding video classification performance, achieving 88.76\\% and 89.13\\% accuracy on ECA9 and Fire Detection datasets, respectively. The source code and the constructed dataset ECA9 will be released in \\url{https://github.com/AlchemyEmperor/SIAVC}.

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://github.com/BryantDom/Driver-Drowsiness-Monitoring.

Self-supervised learning achieves superior performance in many domains by extracting useful representations from the unlabeled data. However, most of traditional self-supervised methods mainly focus on exploring the inter-sample structure while less efforts have been concentrated on the underlying intra-temporal structure, which is important for time series data. In this paper, we present SelfTime: a general self-supervised time series representation learning framework, by exploring the inter-sample relation and intra-temporal relation of time series to learn the underlying structure feature on the unlabeled time series. Specifically, we first generate the inter-sample relation by sampling positive and negative samples of a given anchor sample, and intra-temporal relation by sampling time pieces from this anchor. Then, based on the sampled relation, a shared feature extraction backbone combined with two separate relation reasoning heads are employed to quantify the relationships of the sample pairs for inter-sample relation reasoning, and the relationships of the time piece pairs for intra-temporal relation reasoning, respectively. Finally, the useful representations of time series are extracted from the backbone under the supervision of relation reasoning heads. Experimental results on multiple real-world time series datasets for time series classification task demonstrate the effectiveness of the proposed method. Code and data are publicly available at https://haoyfan.github.io/.