Zhicong Qiu^1,2, Xianmin Wang^1,*, Huawei Ma¹, Songcao Hou¹, Jing Li^1,2,*, Zuoyong Li²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1243-1256, 2023, DOI:10.32604/iasc.2023.038241

Abstract Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks, which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights. The probability margin (PM) method is a promising approach to continuously and path-independently measuring such closeness between the example and decision boundary. However, the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories, where the latter is closer to… More >

Mingyong Li, Zheng Jiang^*, Zongwei Zhao, Longfei Ma

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2387-2406, 2023, DOI:10.32604/iasc.2023.037900

Abstract As an essential category of public event management and control, sentiment analysis of online public opinion text plays a vital role in public opinion early warning, network rumor management, and netizens’ personality portraits under massive public opinion data. The traditional sentiment analysis model is not sensitive to the location information of words, it is difficult to solve the problem of polysemy, and the learning representation ability of long and short sentences is very different, which leads to the low accuracy of sentiment classification. This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text based on the pre-training model… More >

Tianzi Zhao^1,2,3,4, Liang Jin^1,2,3,*, Xiaofeng Zhou^1,2,3, Shuai Li^1,2,3, Shurui Liu^1,2,3,4, Jiang Zhu^1,2,3

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 329-346, 2023, DOI:10.32604/cmc.2023.038595

Abstract The widespread usage of Cyber Physical Systems (CPSs) generates a vast volume of time series data, and precisely determining anomalies in the data is critical for practical production. Autoencoder is the mainstream method for time series anomaly detection, and the anomaly is judged by reconstruction error. However, due to the strong generalization ability of neural networks, some abnormal samples close to normal samples may be judged as normal, which fails to detect the abnormality. In addition, the dataset rarely provides sufficient anomaly labels. This research proposes an unsupervised anomaly detection approach based on adversarial memory autoencoders for multivariate time series… More >

Yea-Sul Kim¹, Ye-Eun Kim¹, Hwankuk Kim^2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1125-1147, 2023, DOI:10.32604/csse.2023.039550

Abstract With the commercialization of 5th-generation mobile communications (5G) networks, a large-scale internet of things (IoT) environment is being built. Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service (DDoS) attacks across vast IoT devices. Recently, research on automated intrusion detection using machine learning (ML) for 5G environments has been actively conducted. However, 5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data. If this data is used to train an ML model, it will likely suffer from generalization errors due to… More >

H. Zarglayoun, C. Arbour, J. Delage, S. Pierre, M. Tremblay, D. Hjeij, P. Rainville, D. Ogez

Psycho-Oncologie, Vol.16, No.4, pp. 375-379, 2022, DOI:10.3166/pson-2022-0202

Abstract Objective: This project aimed to train nurses on an oncology unit in hypnotic communication to reduce treatment-related pain and anxiety in their patients. A pilot study was conducted to assess changes in hypnotic communication behaviors associated with the training.
Methods: Nurses were recruited and their interactions during a simulated patient admission for treatment (before and after training) were recorded. Hypnotic communication skills were assessed by independent reviewers using a training checklist listing different hypnotic communication techniques and a validated assessment scale (Sainte-Justine Hypnotic Communication Assessment Scale, SJ-HCAS).
Results: Seven nurses were evaluated. Wilcoxon pairedsample tests (pre–post) reported significant improvement with… More >

Yingfeng Wang^*

International Journal of Mental Health Promotion, Vol.25, No.6, pp. 735-754, 2023, DOI:10.32604/ijmhp.2023.026440

Abstract A healthy psychological state is the premise for children to carry out various activities. Previous surveys have shown that children with special needs are affected by their own obstacles and are more prone to psychological problems such as sensitivity, low self-esteem, and impulsiveness. Therefore, it is necessary to provide more systematic mental health education support for special children. Mental health education programs are an efficient form of maintaining children’s mental health. However, in the field of special education, the number of mental health education courses developed according to the physical and mental characteristics and developmental needs of special children is… More >

Ren Tao, Chen Shuang^*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6201-6217, 2023, DOI:10.32604/cmc.2023.036178

Abstract A large variety of complaint reports reflect subjective information expressed by citizens. A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary. Therefore, in this paper, a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words. Furthermore, it considers the importance of entity in complaint reports to ensure factual consistency of summary. Experimental results on the customer review datasets (Yelp and Amazon) and complaint report dataset (complaint reports of Shenyang in China) show that the proposed framework outperforms state-of-the-art approaches… More >

Jian Kang¹, Wanshu Fan¹, Yijing Li², Rui Liu¹, Dongsheng Zhou^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 607-629, 2023, DOI:10.32604/cmes.2023.024420

Abstract With the advancement of image sensing technology, estimating 3D human pose from monocular video has become a hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequent action analysis and understanding. It empowers a wide spectrum of potential applications in various areas, such as intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methods for 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extract inter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationship extractions. In this paper, we decompose the 3D joint location regression… More >

Fereydoon Laal^1,2, Amir Hossein Khoshakhlagh³, Esmaeil Zarei^4,5, Rohollah Fallah Madvari⁶, Somayeh Farhang Dehghan^7,*

Sound & Vibration, Vol.56, No.4, pp. 297-306, 2022, DOI:10.32604/sv.2022.028255

Abstract After controls, including engineering and management, the final way to control noise is to use hearing protection devices. Due to the lack of a standardized questionnaire regarding investigating workers’ use of hearing protection devices on the basis of the BASNEF behavioral model, the present study was conducted to investigate the effect of health education based on the BASNEF model on the use of hearing protection devices in workers of an automobile manufacturing plant in Iran. This quasi-experimental and prospective intervention study was performed on 80 workers at an automobile manufacturing plant who are exposed to noise levels above 85 decibels… More >

Wenyuan Wang^1,#, Shuili Luo^1,#, Everett L. Worthington Jr², Haijiang Li^1,3,*

International Journal of Mental Health Promotion, Vol.25, No.4, pp. 539-550, 2023, DOI:10.32604/ijmhp.2023.025634

Abstract Previous studies have found that self-control training was effective in improving an individual’s self-control, which plays an important role in inhibiting negative emotions. However, it is unclear whether self-control training can facilitate refraining from retaliation. This study randomly assigned participants (N = 55) to a training condition (building self-control by avoiding sweets) or a control condition. Before and after training, participants completed the Transgression-Related Interpersonal Motivations Inventory-18 (TRIM-18) and a modified Taylor aggression task once each. Participants in the training condition inflicted more low-intensity penalties on the previous offender compared to control participants. Participants in the training condition reported lower revenge scores… More >