Abdulrahman Abdullah Abaoud^*

International Journal of Mental Health Promotion, Vol.24, No.4, pp. 595-601, 2022, DOI:10.32604/ijmhp.2022.021286

Abstract Functional behavior assessment (FBA) and behavioral intervention plans (BIPs) can be effective for students with attention deficit hyperactivity disorder (ADHD); however, teachers may face difficulties when implementing FBA procedures and, in turn, BIPs because of lack of time, insufficient training, and multiplicity of beliefs. Thus, it is important to identify the difficulties teachers may face and the obstacles that can deter them from implementing intervention plans. This is a worthwhile endeavor because nearly all classrooms will have students with behavioral problems who will benefit from specifically designed educational interventions. This study aimed to identify the… More >

Jiaqi Wang¹, Jie Zhang², Genlin Ji^2,*, Bo Sheng³

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1629-1642, 2022, DOI:10.32604/iasc.2022.029535

Abstract The surveillance applications generate enormous video data and present challenges to video analysis for huge human labor cost. Reconstruction-based convolutional autoencoders have achieved great success in video anomaly detection for their ability of automatically detecting abnormal event. The approaches learn normal patterns only with the normal data in an unsupervised way due to the difficulty of collecting anomaly samples and obtaining anomaly annotations. But convolutional autoencoders have limitations in global feature extraction for the local receptive field of convolutional kernels. What is more, 2-dimensional convolution lacks the capability of capturing temporal information while videos change… More >

Chenxi Yu, Xin Li^*

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2023-2034, 2022, DOI:10.32604/iasc.2022.029447

Abstract Machine reading comprehension (MRC) is a task in natural language comprehension. It assesses machine reading comprehension based on text reading and answering questions. Traditional attention methods typically focus on one of syntax or semantics, or integrate syntax and semantics through a manual method, leaving the model unable to fully utilize syntax and semantics for MRC tasks. In order to better understand syntactic and semantic information and improve machine reading comprehension, our study uses syntactic and semantic attention to conduct text modeling for tasks. Based on the BERT model of Transformer encoder, we separate a text… More >

Jianquan Ouyang^1,*, Jing Zhang¹, Tianming Liu²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1707-1723, 2022, DOI:10.32604/iasc.2022.028352

Abstract Joint entity and relation extraction (JERE) is an important foundation for unstructured knowledge extraction in natural language processing (NLP). Thus, designing efficient algorithms for it has become a vital task. Although existing methods can efficiently extract entities and relations, their performance should be improved. In this paper, we propose a novel model called Attention and Span-based Entity and Relation Transformer (ASpERT) for JERE. First, differing from the traditional approach that only considers the last hidden layer as the feature embedding, ASpERT concatenates the attention head information of each layer with the information of the last… More >

Shun Wang¹, Lin Qiao², Wei Fang³, Guodong Jing⁴, Victor S. Sheng⁵, Yong Zhang^1,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 673-687, 2022, DOI:10.32604/cmc.2022.028411

Abstract PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the… More >

Lina Wang^1,2,*, Xilin Deng¹, Peng Ge¹, Changming Dong^2,3, Brandon J. Bethel³, Leqing Yang¹, Jinyue Xia⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2151-2168, 2022, DOI:10.32604/cmc.2022.027415

Abstract Though numerical wave models have been applied widely to significant wave height prediction, they consume massive computing memory and their accuracy needs to be further improved. In this paper, a two-dimensional (2D) significant wave height (SWH) prediction model is established for the South and East China Seas. The proposed model is trained by Wave Watch III (WW3) reanalysis data based on a convolutional neural network, the bi-directional long short-term memory and the attention mechanism (CNN-BiLSTM-Attention). It adopts the convolutional neural network to extract spatial features of original wave height to reduce the redundant information input… More >

Qi He¹, Wenlong Li¹, Zengzhou Hao², Guohua Liu³, Dongmei Huang¹, Wei Song^1,*, Huifang Xu⁴, Fayez Alqahtani⁵, Jeong-Uk Kim⁶

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 51-67, 2022, DOI:10.32604/cmc.2022.026771

Abstract Sea surface temperature (SST) is closely related to global climate change, ocean ecosystem, and ocean disaster. Accurate prediction of SST is an urgent and challenging task. With a vast amount of ocean monitoring data are continually collected, data-driven methods for SST time-series prediction show promising results. However, they are limited by neglecting complex interactions between SST and other ocean environmental factors, such as air temperature and wind speed. This paper uses multi-factor time series SST data to propose a sequence-to-sequence network with two-module attention (TMA-Seq2seq) for long-term time series SST prediction. Specifically, TMA-Seq2seq is an… More >

Yu Xue^*, Ziming Yuan

Journal on Internet of Things, Vol.4, No.1, pp. 57-67, 2022, DOI:10.32604/jiot.2022.025327

Abstract The attention mechanism is one of the most important priori knowledge to enhance convolutional neural networks. Most attention mechanisms are bound to the convolutional layer and use local or global contextual information to recalibrate the input. This is a popular attention strategy design method. Global contextual information helps the network to consider the overall distribution, while local contextual information is more general. The contextual information makes the network pay attention to the mean or maximum value of a particular receptive field. Different from the most attention mechanism, this article proposes a novel attention mechanism with… More >

Yanman Li¹, Jun Zhang¹, Yang Hu¹, Yingnan Zhao^2,*, Yi Cao³

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1219-1230, 2022, DOI:10.32604/csse.2022.028224

Abstract Safety helmet-wearing detection is an essential part of the intelligent monitoring system. To improve the speed and accuracy of detection, especially small targets and occluded objects, it presents a novel and efficient detector model. The underlying core algorithm of this model adopts the YOLOv5 (You Only Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (Complete Intersection Over Union) Loss function, and the Mish activation function. First, it applies the attention mechanism in the feature extraction. The network can learn the weight of… More >

Can Xiao^1,2, Feng Li^1,2,*, Dengyong Zhang^1,2, Pu Huang^1,2, Xiangling Ding³, Victor S. Sheng⁴

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1145-1154, 2022, DOI:10.32604/csse.2022.027249

Abstract Image inpainting based on deep learning has been greatly improved. The original purpose of image inpainting was to repair some broken photos, such as inpainting artifacts. However, it may also be used for malicious operations, such as destroying evidence. Therefore, detection and localization of image inpainting operations are essential. Recent research shows that high-pass filtering full convolutional network (HPFCN) is applied to image inpainting detection and achieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, we introduce the squeezed excitation blocks More >