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  • Open Access


    YOLOv3 Attention Face Detector with High Accuracy and Efficiency

    Qiyuan Liu, Shuhua Lu*, Lingqiang Lan

    Computer Systems Science and Engineering, Vol.37, No.2, pp. 283-295, 2021, DOI:10.32604/csse.2021.014086

    Abstract In recent years, face detection has attracted much attention and achieved great progress due to its extensively practical applications in the field of face based computer vision. However, the tradeoff between accuracy and efficiency of the face detectors still needs to be further studied. In this paper, using Darknet-53 as backbone, we propose an improved YOLOv3-attention model by introducing attention mechanism and data augmentation to obtain the robust face detector with high accuracy and efficiency. The attention mechanism is introduced to enhance much higher discrimination of the deep features, and the trick of data augmentation is used in the training… More >

  • Open Access


    Mind-Body Exercises (Yoga/Tai Chi) for Attention-Deficit/Hyperactivity Disorder: A Quantitative Evidence of Experimental Studies

    Erfei Zuo1, Yanjie Zhang2, Qian Yu2, Tianyou Guo2, Can Jiao2, Ye Yu3, Patrick Müller4, Xinli Chi2, Md Mahhub Hossain5, Albert S. Yeung6, Notger G. Müller4, Liye Zou2,*

    International Journal of Mental Health Promotion, Vol.22, No.4, pp. 221-231, 2020, DOI:10.32604/IJMHP.2020.014552

    Abstract Background: Attention-deficit/hyperactivity disorder (ADHD) is a common pediatric psychiatric disorder. Although mindful exercises (Yoga and Tai Chi) have been increasingly accepted as alternative medicine for ADHD, no meta-analytic review has been conducted on this topic. Objective: We systematically and critically evaluated the existing literature regarding the effects of the two most widely practiced mindful exercises on ADHD symptoms and social problems in children and adolescents with ADHD. Methods: Searching literature databases included PubMed, Web of Science, Scope, China National Knowledge Infrastructure and Wanfang. Only randomized controlled trials (RCT) and nonrandomized controlled studies (NRS) that investigated the beneficial effects of Yoga… More >

  • Open Access


    Adversarial Active Learning for Named Entity Recognition in Cybersecurity

    Tao Li1, Yongjin Hu1,*, Ankang Ju1, Zhuoran Hu2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 407-420, 2021, DOI:10.32604/cmc.2020.012023

    Abstract Owing to the continuous barrage of cyber threats, there is a massive amount of cyber threat intelligence. However, a great deal of cyber threat intelligence come from textual sources. For analysis of cyber threat intelligence, many security analysts rely on cumbersome and time-consuming manual efforts. Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber threat intelligence. As the foundation for constructing cybersecurity knowledge graph, named entity recognition (NER) is required for identifying critical threat-related elements from textual cyber threat intelligence. Recently, deep neural network-based models have attained very good results in NER. However, the performance of these… More >

  • Open Access


    Straw Segmentation Algorithm Based on Modified UNet in Complex Farmland Environment

    Yuanyuan Liu1,2, Shuo Zhang1, Haiye Yu3, Yueyong Wang4,*, Yuehan Feng1, Jiahui Sun1, Xiaokang Zhou1

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 247-262, 2021, DOI:10.32604/cmc.2020.012328

    Abstract Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment. Traditional pattern recognition has some problems, such as low precision and a long processing time, when segmenting complex farmland, which cannot meet the conditions of embedded equipment deployment. Based on these problems, we proposed a novel deep learning model with high accuracy, small model size and fast running speed named Residual Unet with Attention mechanism using depthwise convolution (RADw–UNet). This algorithm is based on the UNet symmetric codec model. All the feature extraction modules of the network adopt the residual structure, and the whole network… More >

  • Open Access


    ACLSTM: A Novel Method for CQA Answer Quality Prediction Based on Question-Answer Joint Learning

    Weifeng Ma*, Jiao Lou, Caoting Ji, Laibin Ma

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 179-193, 2021, DOI:10.32604/cmc.2020.011969

    Abstract Given the limitations of the community question answering (CQA) answer quality prediction method in measuring the semantic information of the answer text, this paper proposes an answer quality prediction model based on the question-answer joint learning (ACLSTM). The attention mechanism is used to obtain the dependency relationship between the Question-and-Answer (Q&A) pairs. Convolutional Neural Network (CNN) and Long Short-term Memory Network (LSTM) are used to extract semantic features of Q&A pairs and calculate their matching degree. Besides, answer semantic representation is combined with other effective extended features as the input representation of the fully connected layer. Compared with other quality… More >

  • Open Access


    PGCA-Net: Progressively Aggregating Hierarchical Features with the Pyramid Guided Channel Attention for Saliency Detection

    Jiajie Mai1, Xuemiao Xu2,*, Guorong Xiao3, Zijun Deng2, Jiaxing Chen2

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 847-855, 2020, DOI:10.32604/iasc.2020.010119

    Abstract The Salient object detection aims to segment out the most visually distinctive objects in an image, which is a challenging task in computer vision. In this paper, we present the PGCA-Net equipped with the pyramid guided channel attention fusion block (PGCAFB) for the saliency detection task. Given an input image, the hierarchical features are extracted using a deep convolutional neural network (DCNN), then starting from the highest-level semantic features, we stage-by-stage restore the spatial saliency details by aggregating the lowerlevel detailed features. Since for the weak discriminative ability of the shallow detailed features, directly introducing them to the semantic features… More >

  • Open Access


    A Multi-View Gait Recognition Method Using Deep Convolutional Neural Network and Channel Attention Mechanism

    Jiabin Wang*, Kai Peng

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 345-363, 2020, DOI:10.32604/cmes.2020.011046

    Abstract In many existing multi-view gait recognition methods based on images or video sequences, gait sequences are usually used to superimpose and synthesize images and construct energy-like template. However, information may be lost during the process of compositing image and capture EMG signals. Errors and the recognition accuracy may be introduced and affected respectively by some factors such as period detection. To better solve the problems, a multi-view gait recognition method using deep convolutional neural network and channel attention mechanism is proposed. Firstly, the sliding time window method is used to capture EMG signals. Then, the back-propagation learning algorithm is used… More >

  • Open Access


    Stacked Attention Networks for Referring Expressions Comprehension

    Yugang Li1, *, Haibo Sun1, Zhe Chen1, Yudan Ding1, Siqi Zhou2

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2529-2541, 2020, DOI:10.32604/cmc.2020.011886

    Abstract Referring expressions comprehension is the task of locating the image region described by a natural language expression, which refer to the properties of the region or the relationships with other regions. Most previous work handles this problem by selecting the most relevant regions from a set of candidate regions, when there are many candidate regions in the set these methods are inefficient. Inspired by recent success of image captioning by using deep learning methods, in this paper we proposed a framework to understand the referring expressions by multiple steps of reasoning. We present a model for referring expressions comprehension by… More >

  • Open Access


    An Attention-Based Friend Recommendation Model in Social Network

    Chongchao Cai1, 2, Huahu Xu1, *, Jie Wan2, Baiqing Zhou2, Xiongwei Xie3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2475-2488, 2020, DOI:10.32604/cmc.2020.011693

    Abstract In social networks, user attention affects the user’s decision-making, resulting in a performance alteration of the recommendation systems. Existing systems make recommendations mainly according to users’ preferences with a particular focus on items. However, the significance of users’ attention and the difference in the influence of different users and items are often ignored. Thus, this paper proposes an attention-based multi-layer friend recommendation model to mitigate information overload in social networks. We first constructed the basic user and item matrix via convolutional neural networks (CNN). Then, we obtained user preferences by using the relationships between users and items, which were later… More >

  • Open Access


    A Modified Method for Scene Text Detection by ResNet

    Shaozhang Niu1, *, Xiangxiang Li1, Maosen Wang1, Yueying Li2

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2233-2245, 2020, DOI:10.32604/cmc.2020.09471

    Abstract In recent years, images have played a more and more important role in our daily life and social communication. To some extent, the textual information contained in the pictures is an important factor in understanding the content of the scenes themselves. The more accurate the text detection of the natural scenes is, the more accurate our semantic understanding of the images will be. Thus, scene text detection has also become the hot spot in the domain of computer vision. In this paper, we have presented a modified text detection network which is based on further research and improvement of Connectionist… More >

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