Yongliang Yang, Linghua Xu^*, Maolin Luo, Xiao Wang, Min Cao

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2741-2765, 2024, DOI:10.32604/cmc.2024.046768

Abstract Due to the complex environment of the university laboratory, personnel flow intensive, personnel irregular behavior is easy to cause security risks. Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed. Therefore, the current management of personnel behavior mainly relies on institutional constraints, education and training, on-site supervision, etc., which is time-consuming and ineffective. Given the above situation, this paper proposes an improved You Only Look Once version 7 (YOLOv7) to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy. First, to better capture the shape features of the target,… More >

Motasem S. Alsawadi^1,*, El-Sayed M. El-kenawy², Miguel Rio¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2767-2782, 2023, DOI:10.32604/iasc.2023.039440

Abstract The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. Skeleton extraction from photos for mobile devices has been made possible by the BlazePose system. A Spatial-Temporal Graph Convolutional Network (STGCN) can then forecast the actions. The Spatial-Temporal Graph Convolutional Network (STGCN) can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph’s topology and then fix it across all input layers and samples.… More >

Tao Liu¹, Kejia Zhang^1,*, Jingsong Yin¹, Yan Zhang¹, Zihao Mu¹, Chunsheng Li¹, Yanan Hu²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2563-2582, 2023, DOI:10.32604/csse.2023.041228

Abstract Spatio-temporal heterogeneous data is the database for decision-making in many fields, and checking its accuracy can provide data support for making decisions. Due to the randomness, complexity, global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions, traditional detection methods can not guarantee both detection speed and accuracy. Therefore, this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks. Firstly, the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the… More >

Meilin Wu^1,2, Lianggui Tang^1,2,*, Qingda Zhang^1,2, Ke Yan^1,2

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 179-198, 2023, DOI:10.32604/iasc.2023.036684

Abstract As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then… More >

Motasem S. Alsawadi^1,*, El-Sayed M. El-kenawy^2,3, Miguel Rio¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 19-36, 2023, DOI:10.32604/cmc.2023.032499

Abstract The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community's attention in the computer vision field. Therefore, finding efficient solutions to extract knowledge from these sources is imperative. Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices. With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action. We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest, it is possible to… More >

Kun Ding¹, Lu Xu², Ming Liu¹, Xiaoxiong Zhang¹, Liu Liu¹, Daojian Zeng^2,*, Yuting Liu^1,3, Chen Jin⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 641-654, 2023, DOI:10.32604/cmc.2023.031052

Abstract Event detection (ED) is aimed at detecting event occurrences and categorizing them. This task has been previously solved via recognition and classification of event triggers (ETs), which are defined as the phrase or word most clearly expressing event occurrence. Thus, current approaches require both annotated triggers as well as event types in training data. Nevertheless, triggers are non-essential in ED, and it is time-wasting for annotators to identify the “most clearly” word from a sentence, particularly in longer sentences. To decrease manual effort, we evaluate event detection without triggers. We propose a novel framework that combines Type-aware Attention and Graph… More >

Binhui Tang^1,3, Junfeng Wang^2,*, Huanran Qiu³, Jian Yu², Zhongkun Yu², Shijia Liu^2,4

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 235-252, 2023, DOI:10.32604/cmc.2023.029135

Abstract The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats (APT). Extracting attack behaviors, i.e., Tactics, Techniques, Procedures (TTP) from Cyber Threat Intelligence (CTI) can facilitate APT actors’ profiling for an immediate response. However, it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature. Based on the Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) of threat behavior description, this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network (HTN) and Graph Convolutional Network (GCN) to solve this… More >

Zhenzhou Wang¹, Shao Cui¹, Xiang Wang^1,*, JiaFeng Tian²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4709-4725, 2022, DOI:10.32604/cmc.2022.027767

Abstract To generate realistic three-dimensional animation of virtual character, capturing real facial expression is the primary task. Due to diverse facial expressions and complex background, facial landmarks recognized by existing strategies have the problem of deviations and low accuracy. Therefore, a method for facial expression capture based on two-stage neural network is proposed in this paper which takes advantage of improved multi-task cascaded convolutional networks (MTCNN) and high-resolution network. Firstly, the convolution operation of traditional MTCNN is improved. The face information in the input image is quickly filtered by feature fusion in the first stage and Octave Convolution instead of the… More >

Chaowei Lin^1,#, Feifei Lee^1,#,*, Jiawei Cai¹, Hanqing Chen¹, Qiu Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 985-1006, 2021, DOI:10.32604/cmes.2021.014522

Abstract Scene recognition is a fundamental task in computer vision, which generally includes three vital stages, namely feature extraction, feature transformation and classification. Early research mainly focuses on feature extraction, but with the rise of Convolutional Neural Networks (CNNs), more and more feature transformation methods are proposed based on CNN features. In this work, a novel feature transformation algorithm called Graph Encoded Local Discriminative Region Representation (GEDRR) is proposed to find discriminative local representations for scene images and explore the relationship between the discriminative regions. In addition, we propose a method using the multi-head attention module to enhance and fuse convolutional… More >

Dengyong Zhang^1,2, Jiawei Hu^1,2, Feng Li^1,2,*, Xiangling Ding³, Arun Kumar Sangaiah⁴, Victor S. Sheng⁵

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1503-1517, 2021, DOI:10.32604/cmc.2021.017089

Abstract In the past several years, remarkable achievements have been made in the field of object detection. Although performance is generally improving, the accuracy of small object detection remains low compared with that of large object detection. In addition, localization misalignment issues are common for small objects, as seen in GoogLeNets and residual networks (ResNets). To address this problem, we propose an improved region-based fully convolutional network (R-FCN). The presented technique improves detection accuracy and eliminates localization misalignment by replacing position-sensitive region of interest (PS-RoI) pooling with position-sensitive precise region of interest (PS-Pr-RoI) pooling, which avoids coordinate quantization and directly calculates… More >