As data sets become larger and more complicated, an extreme learning machine (ELM) that runs in a traditional serial environment cannot realize its ability to be fast and effective. Although a parallel ELM (PELM) based on MapReduce to process large-scale data shows more efficient learning speed than identical ELM algorithms in a serial environment, some operations, such as intermediate results stored on disks and multiple copies for each task, are indispensable, and these operations create a large amount of extra overhead and degrade the learning speed and efficiency of the PELMs. In this paper, an efficient ELM based on the Spark framework (SELM), which includes three parallel subalgorithms, is proposed for big data classification. By partitioning the corresponding data sets reasonably, the hidden layer output matrix calculation algorithm, matrix Û decomposition algorithm, and matrix V decomposition algorithm perform most of the computations locally. At the same time, they retain the intermediate results in distributed memory and cache the diagonal matrix as broadcast variables instead of several copies for each task to reduce a large amount of the costs, and these actions strengthen the learning ability of the SELM. Finally, we implement our SELM algorithm to classify large data sets. Extensive experiments have been conducted to validate the effectiveness of the proposed algorithms. As shown, our SELM achieves an 8.71× speedup on a cluster with ten nodes, and reaches a 13.79× speedup with 15 nodes, an 18.74× speedup with 20 nodes, a 23.79× speedup with 25 nodes, a 28.89× speedup with 30 nodes, and a 33.81× speedup with 35 nodes.

Zeroing neural network (ZNN), as a powerful calculating tool, is extensively applied in various computation and optimization fields. Convergence and noise-tolerance performance are always pursued and investigated in the ZNN field. Up to now, there are no unified ZNN models that simultaneously achieve the finite-time convergence and inherent noise tolerance for computing time-varying quadratic optimization problems, although this superior property is highly demanded in practical applications. In this paper, for computing time-varying quadratic optimization within finite-time convergence in the presence of various additive noises, a new framework for ZNN is designed to fill this gap in a unified manner. Specifically, different from the previous design formulas either possessing finite-time convergence or possessing noise-tolerance performance, a new design formula with finite-time convergence and noise tolerance is proposed in a unified framework (and thus called unified design formula). Then, on the basis of the unified design formula, a unified ZNN (UZNN) is, thus, proposed and investigated in the unified framework of ZNN for computing time-varying quadratic optimization problems in the presence of various additive noises. In addition, theoretical analyses of the unified design formula and the UZNN model are given to guarantee the finite-time convergence and inherent noise tolerance. Computer simulation results verify the superior property of the UZNN model for computing time-varying quadratic optimization problems, as compared with the previously proposed ZNN models.

Age estimation is a challenging task, because it can be easily affected by gender, race, and other intrinsic and extrinsic attributes. At the same time, performing age estimation for a narrow age range may lead to better results. In this paper, to achieve robust age estimation, an ensemble structure referred to as CNN2ELM, which includes convolutional neural network (CNN) and extreme learning machine (ELM), is proposed for age estimation. The three-level system includes feature extraction and fusion, age grouping via an ELM classifier, and age estimation via an ELM regressor. Age-Net, Gender-Net, and Race-Net are trained using different targets, such as age class, gender class, and race class, respectively, and the three networks are used to extract features corresponding to age, gender, and race from the same image of a person during validation and test stages. Features related to the age property are enhanced by fusing these of race and gender properties. Then, to achieve a narrow age range, the ELM classifies the fusion results into one of the age groups. Afterward, an age decision is made using an ELM regressor. Our network is pretrained on an ImageNet database and then fine-tuned on the IMDB-WIKI database. The recently released Adience benchmark, ChaLearn Looking at People 2016 (LAP-2016), and MORPH-II are used to verify the performance of “Race-Net + Age-Net + Gender-Net + ELM classifier + ELM regressor (RAGN).” RAGN outperforms the existing state-of-the-art age estimation methods. The mean absolute error of the age estimation of RAGN for MORPH-II is determined to be 2.61 years; the accuracy of the age estimation for the Adience benchmark is 0.6649; and the normal score (ϵ) for the sequestered test set of the LAP-2016 data set is 0.3679.

In recent years, industrial control systems have evolved toward stability and efficiency, increasing industrial control systems interconnected with the Internet, which means that industrial control systems are facing more serious cyber threats. Thus, it is critical for enterprises to consider issues related to data privacy and network security. Digital twin enables real-time synchronization and simulation of data from various physical components of industrial control systems. However, anomaly detection of twin data is still challenging because existing methods are usually multi-stage with tedious training and detection steps. Therefore, we propose a method called end-to-end anomaly detection with the aim to accomplish real-time anomaly detection quickly and accurately. In order to seek key features, multidimensional deconvolutional network and attention mechanism are applied to our model. The results of this study indicate that our method performs well on precision and F1 score in comparison to the state-of-art methods.

Multi-task learning plays an important role in face multi-attribute prediction. At present, most researches excavate the shared information between attributes by sharing all convolutional layers. However, it is not appropriate to treat the low-level and high-level features of the face multi-attribute equally, because the high-level features are more biased toward the specific content of the category. In this article, a novel multi-attribute tensor correlation neural network (MTCN) is used to predict face attributes. MTCN shares all attribute features at the low-level layers, and then distinguishes each attribute feature at the high-level layers. To better excavate the correlations among high-level attribute features, each sub-network explores useful information from other networks to enhance its original information. Then a tensor canonical correlation analysis method is used to seek the correlations among the highest-level attributes, which enhances the original information of each attribute. After that, these features are mapped into a highly correlated space through the correlation matrix. Finally, we use sufficient experiments to verify the performance of MTCN on the CelebA and LFWA datasets and our MTCN achieves the best performance compared with the latest multi-attribute recognition algorithms under the same settings.

Although age estimation is easily affected by smiling, race, gender, and other age-related attributes, most of the researchers did not pay attention to the correlations among these attributes. Moreover, many researchers perform age estimation from a wide range of age; however, conducting an age prediction over a narrow age range may achieve better results. This article proposes a hierarchic approach referred to as EGroupNet for age prediction. The method includes two main stages, i.e., feature enhancement via excavating the correlations among age-related attributes and age estimation based on different age group schemes. First, we apply the multi-task learning model to learn multiple face attributes simultaneously to obtain discriminative features of different attributes. Second, we project the outputs of fully connected layers of several subnetworks into a highly correlated matrix space via the correlation learning process. Third, we classify these enhanced features into narrow age groups using two Extreme Learning Machine models. Finally, we make predictions based on the results of the age groups mergence. We conduct a large number of experiments on MORPH-II, LAP-2016 dataset, and Adience benchmark. The mean absolute errors of the two different settings on MORPH-II are 2.48 and 2.13 years, respectively; the normal score (ε) on the LAP-2016 dataset is 0.3578; and the accuracy of age prediction on Adience benchmark is 0.6978.