Lei Yang, Yang Liu^*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.073529 - 28 January 2026

Abstract Background: This study aims to investigate the underlying mechanisms between parental marital conflict and adolescent short video dependence by constructing a chain mediation model, focusing on the mediating roles of experiential avoidance and emotional disturbance (anxiety, depression, and stress). Methods: Conducted in January 2025, the research recruited 4125 adolescents from multiple Chinese provinces through convenience sampling; after data cleaning, 3957 valid participants (1959 males, 1998 females) were included. Using a cross-sectional design, measures included parental marital conflict, experiential avoidance, anxiety, depression, stress, and short video dependence. Results: Pearson correlation analysis revealed significant positive correlations among all variables.… More >

Yue Cui¹, Ziqing Yang², Hao Gao^1,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.072986 - 28 January 2026

Abstract Background: In the Chinese context, the impact of short video applications on the psychological well-being of older adults is contested. While often examined through a pathological lens of addiction, this perspective may overlook paradoxical, context-dependent positive outcomes. Therefore, the main objective of this study is to challenge the traditional Compensatory Internet Use Theory by proposing and testing a chained mediation model that explores a paradoxical pathway from social support to life satisfaction via problematic social media use. Methods: Data were collected between July and August 2025 via the Credamo online survey platform, yielding 384 valid responses… More >

Beike Yu, Dafang Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.076439 - 29 January 2026

Abstract Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving, owing to its efficiency and applicability in both training and evaluating algorithms. Consequently, there has been increasing attention on generating highly realistic and consistent driving videos, particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles. However, current reconstruction approaches, such as Neural Radiance Fields and 3D Gaussian Splatting, frequently suffer from limited generalization and depend on substantial input data. Meanwhile, 2D generative models, though capable of producing unknown scenes, still have room for improvement in terms… More >

Weishan Gao^1,2, Ye Wang^1,2, Xiaoyin Wang^1,2, Xiaochuan Jing^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073850 - 12 January 2026

Abstract In the field of intelligent surveillance, weakly supervised video anomaly detection (WSVAD) has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels. Although multiple instance learning (MIL) has dominated the WSVAD for a long time, its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events. In addition, insufficient temporal modeling obscures causal relationships between events, making anomaly decisions reactive rather than reasoning-based. To overcome the limitations above, this paper proposes an adaptive knowledge-based guidance method that integrates external structured… More >

Jixin Wu^1,2, Mingtao Zhou^2,3, Di Wu^2,3, Wenqi Ren⁴, Jiatian Mei^2,3, Shu Zhang^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072964 - 12 January 2026

Abstract End-to-end Temporal Action Detection (TAD) has achieved remarkable progress in recent years, driven by innovations in model architectures and the emergence of Video Foundation Models (VFMs). However, existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs, which become particularly pronounced when processing long video sequences. Moreover, the need for precise temporal boundary annotations makes data labeling extremely expensive. In low-resource settings where annotated samples are scarce, direct fine-tuning tends to cause overfitting. To address these challenges, we introduce Dynamic Low-Rank Adapter (DyLoRA), a lightweight fine-tuning framework tailored specifically… More >

Tai Liu^1,2, Mao Ye^2,*, Feng Wu³, Chao Zhu², Bo Chen², Guoyan Zhang^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072692 - 12 January 2026

Abstract With the continuous advancement of unmanned technology in various application domains, the development and deployment of blind-spot-free panoramic video systems have gained increasing importance. Such systems are particularly critical in battlefield environments, where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles (UGVs). However, conventional video surveillance systems suffer from several limitations, including limited field of view, high processing latency, low reliability, excessive resource consumption, and significant transmission delays. These shortcomings impede the widespread adoption of UGVs in battlefield settings. To overcome these… More >

Jalees Ur Rahman¹, Muhammad Hanif¹, Usman Haider^2,*, Saeed Mian Qaisar^3,*, Sarra Ayouni⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071251 - 12 January 2026

Abstract Deep neural networks have achieved excellent classification results on several computer vision benchmarks. This has led to the popularity of machine learning as a service, where trained algorithms are hosted on the cloud and inference can be obtained on real-world data. In most applications, it is important to compress the vision data due to the enormous bandwidth and memory requirements. Video codecs exploit spatial and temporal correlations to achieve high compression ratios, but they are computationally expensive. This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos. However, contrary… More >

Dao Phuc Minh Huy¹, Gia Nhu Nguyen¹, Dac-Nhuong Le^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070948 - 12 January 2026

Abstract Online examinations have become a dominant assessment mode, increasing concerns over academic integrity. To address the critical challenge of detecting cheating behaviours, this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification. The methodology utilises object detection models—You Only Look Once (YOLOv12), Faster Region-based Convolutional Neural Network (RCNN), and Single Shot Detector (SSD) MobileNet—integrated with classification models such as Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Unit (Bi-GRU), and CNN-LSTM (Long Short-Term Memory). Two distinct datasets were used: the Online Exam Proctoring (EOP) dataset from Michigan State University and… More >

Tahira Khalil¹, Sadeeq Jan^2,*, Rania M. Ghoniem³, Muhammad Imran Khan Khalil¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.072655 - 09 December 2025

Abstract The contemporary era is characterized by rapid technological advancements, particularly in the fields of communication and multimedia. Digital media has significantly influenced the daily lives of individuals of all ages. One of the emerging domains in digital media is the creation of cartoons and animated videos. The accessibility of the internet has led to a surge in the consumption of cartoons among young children, presenting challenges in monitoring and controlling the content they view. The prevalence of cartoon videos containing potentially violent scenes has raised concerns regarding their impact, especially on young and impressionable minds.… More >

Xiaorui Zhang^1,2,*, Chunlin Yuan³, Wei Sun⁴, Ting Wang⁵

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.071043 - 09 December 2025

Abstract Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression, but existing models have significant shortcomings. On the one hand, Transformer multi-head self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity. On the other hand, fixed-size convolution kernels are often used, which have weak perception ability for emotional regions of different scales. Therefore, this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling. In terms of space, multi-branch large-kernel stripe convolution is used to More >