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  • Open Access

    ARTICLE

    Association between Job Satisfaction and Stress or Depressive Symptom of Employed Persons with Disabilities: Findings from the Panel Survey of Employment for the Disabled 2016–2023

    Jeong Min Yang1, Ji Sung Hong1, Jae Hyun Kim2,*

    International Journal of Mental Health Promotion, Vol.26, No.10, pp. 791-803, 2024, DOI:10.32604/ijmhp.2024.056433 - 31 October 2024

    Abstract Background: This study conducted a longitudinal analysis of the association between job satisfaction and stress or depressive symptoms of employed persons with disabilities (PWDs) based on the data from the 1st to 8th Pannel Survey of Employment for the Disabled (PSED). Methods: After excluding missing values, data on 1614 participants at baseline (1st wave) were analyzed using the chi-square test and generalized estimating equation (GEE) model for data from 1st to 8thPSED. Results: It was found that for each one-unit increase in the job satisfaction score, the stress scale decreased by 0.004 (B: −0.004, 95% CI:… More >

  • Open Access

    REVIEW

    Digital Image Steganographer Identification: A Comprehensive Survey

    Qianqian Zhang1,2,3, Yi Zhang1,2, Yuanyuan Ma3, Yanmei Liu1,2, Xiangyang Luo1,2,*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 105-131, 2024, DOI:10.32604/cmc.2024.055735 - 15 October 2024

    Abstract The rapid development of the internet and digital media has provided convenience while also posing a potential risk of steganography abuse. Identifying steganographer is essential in tracing secret information origins and preventing illicit covert communication online. Accurately discerning a steganographer from many normal users is challenging due to various factors, such as the complexity in obtaining the steganography algorithm, extracting highly separability features, and modeling the cover data. After extensive exploration, several methods have been proposed for steganographer identification. This paper presents a survey of existing studies. Firstly, we provide a concise introduction to the More >

  • Open Access

    REVIEW

    Robust Deep Image Watermarking: A Survey

    Yuanjing Luo, Xichen Tan, Zhiping Cai*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 133-160, 2024, DOI:10.32604/cmc.2024.055150 - 15 October 2024

    Abstract In the era of internet proliferation, safeguarding digital media copyright and integrity, especially for images, is imperative. Digital watermarking stands out as a pivotal solution for image security. With the advent of deep learning, watermarking has seen significant advancements. Our review focuses on the innovative deep watermarking approaches that employ neural networks to identify robust embedding spaces, resilient to various attacks. These methods, characterized by a streamlined encoder-decoder architecture, have shown enhanced performance through the incorporation of novel training modules. This article offers an in-depth analysis of deep watermarking’s core technologies, current status, and prospective More >

  • Open Access

    REVIEW

    Exploring Frontier Technologies in Video-Based Person Re-Identification: A Survey on Deep Learning Approach

    Jiahe Wang1, Xizhan Gao1,*, Fa Zhu2, Xingchi Chen3

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 25-51, 2024, DOI:10.32604/cmc.2024.054895 - 15 October 2024

    Abstract Video-based person re-identification (Re-ID), a subset of retrieval tasks, faces challenges like uncoordinated sample capturing, viewpoint variations, occlusions, cluttered backgrounds, and sequence uncertainties. Recent advancements in deep learning have significantly improved video-based person Re-ID, laying a solid foundation for further progress in the field. In order to enrich researchers’ insights into the latest research findings and prospective developments, we offer an extensive overview and meticulous analysis of contemporary video-based person Re-ID methodologies, with a specific emphasis on network architecture design and loss function design. Firstly, we introduce methods based on network architecture design and loss… More >

  • Open Access

    ARTICLE

    Humor detection using deep learning in 10 years: A survey

    Chengjuan Ren1, Ziyu Guo2, Ping Zhang3, Yuhan Gao4,*

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.1, pp. 1-9, 2024, DOI:10.23967/j.rimni.2024.01.006 - 31 January 2024

    Abstract Humor is an important part of personal communication. How to create a computational model to recognize humor is still a very challenging task in natural language processing and linguistics. In this survey, we applied some rules to leave 29 articles spanning 10 years (2012 to 2023). The main elements covered by this survey include: recent state-of-the-art detection methods using deep learning from years 2012-2023, (2) summarizing features for humor detection from a linguistic perspective, (3) humor detection datasets, evaluation metrics, data domains and languages, (4) some tricks used in humor detection (e.g. Attention mechanism, multimodal), More >

  • Open Access

    ARTICLE

    Net Effect of Short-Term Smoking Cessation on Mental Health Changes: Inverse Probability of Treatment Weighting and Difference-in-Differences Method

    Ji-Su Park1, Tae-Hyeon Lee2, Il-Su Park3,*

    International Journal of Mental Health Promotion, Vol.26, No.9, pp. 745-755, 2024, DOI:10.32604/ijmhp.2024.054630 - 20 September 2024

    Abstract Background: In response to the need for research clearly demonstrating the net effect of smoking cessation on mental health status, considering the causal relationship between smoking cessation and changes in mental health status, this study was undertaken. Thus, this study aimed to examine the net effect of short-term smoking cessation on five specific mental health metrics and the overall mental health status score. Methods: We used data from the first wave of the Korean Health Panel (KHP) Survey (2011–2013, 2016–2018) and focused on men aged 19 years and older, to explore the effects of smoking… More >

  • Open Access

    REVIEW

    Confusing Object Detection: A Survey

    Kunkun Tong1,#, Guchu Zou2,#, Xin Tan1,*, Jingyu Gong1, Zhenyi Qi2, Zhizhong Zhang1, Yuan Xie1, Lizhuang Ma1

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3421-3461, 2024, DOI:10.32604/cmc.2024.055327 - 12 September 2024

    Abstract Confusing object detection (COD), such as glass, mirrors, and camouflaged objects, represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds, leveraging deep learning methodologies. Despite garnering increasing attention in computer vision, the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures. As of now, there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks. To fill this gap, our study presents a pioneering review that covers… More >

  • Open Access

    REVIEW

    Survey on Video Security: Examining Threats, Challenges, and Future Trends

    Ali Asghar1,#, Amna Shifa2,#, Mamoona Naveed Asghar2,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3591-3635, 2024, DOI:10.32604/cmc.2024.054654 - 12 September 2024

    Abstract Videos represent the most prevailing form of digital media for communication, information dissemination, and monitoring. However, their widespread use has increased the risks of unauthorised access and manipulation, posing significant challenges. In response, various protection approaches have been developed to secure, authenticate, and ensure the integrity of digital videos. This study provides a comprehensive survey of the challenges associated with maintaining the confidentiality, integrity, and availability of video content, and examining how it can be manipulated. It then investigates current developments in the field of video security by exploring two critical research questions. First, it… More >

  • Open Access

    REVIEW

    Unsupervised Time Series Segmentation: A Survey on Recent Advances

    Chengyu Wang, Xionglve Li, Tongqing Zhou, Zhiping Cai*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2657-2673, 2024, DOI:10.32604/cmc.2024.054061 - 15 August 2024

    Abstract Time series segmentation has attracted more interests in recent years, which aims to segment time series into different segments, each reflects a state of the monitored objects. Although there have been many surveys on time series segmentation, most of them focus more on change point detection (CPD) methods and overlook the advances in boundary detection (BD) and state detection (SD) methods. In this paper, we categorize time series segmentation methods into CPD, BD, and SD methods, with a specific focus on recent advances in BD and SD methods. Within the scope of BD and SD,… More >

  • Open Access

    REVIEW

    A Comprehensive Survey on Advanced Persistent Threat (APT) Detection Techniques

    Singamaneni Krishnapriya*, Sukhvinder Singh

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2675-2719, 2024, DOI:10.32604/cmc.2024.052447 - 15 August 2024

    Abstract The increase in number of people using the Internet leads to increased cyberattack opportunities. Advanced Persistent Threats, or APTs, are among the most dangerous targeted cyberattacks. APT attacks utilize various advanced tools and techniques for attacking targets with specific goals. Even countries with advanced technologies, like the US, Russia, the UK, and India, are susceptible to this targeted attack. APT is a sophisticated attack that involves multiple stages and specific strategies. Besides, TTP (Tools, Techniques, and Procedures) involved in the APT attack are commonly new and developed by an attacker to evade the security system.… More >

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