The development of information gathering and extraction technology has led to the popularity of multi-view data, which enables samples to be seen from numerous perspectives. Multi-view clustering (MVC), which groups data samples by leveraging complementary and consensual information from several views, is gaining popularity. Despite the rapid evolution of MVC approaches, there has yet to be a study that provides a full MVC roadmap for both stimulating technical improvements and orienting research newbies to MVC. In this article, we review recent MVC techniques with the purpose of exhibiting the concepts of popular methodologies and their advancements. This survey not only serves as a unique MVC comprehensive knowledge for researchers but also has the potential to spark new ideas in MVC research. We summarise a large variety of current MVC approaches based on two technical mechanisms: heuristic-based multi-view clustering (HMVC) and neural network-based multi-view clustering (NNMVC). We end with four technological approaches within the category of HMVC: nonnegative matrix factorisation, graph learning, latent representation learning, and tensor learning. Deep representation learning and deep graph learning are two technical methods that we demonstrate in NNMVC. We also show 15 publicly available multi-view datasets and examine how representative MVC approaches perform on them. In addition, this study identifies the potential research directions that may require further investigation in order to enhance the further development of MVC.

The widespread adoption of deep neural networks (DNNs) in speech recognition has introduced significant security vulnerabilities, particularly from backdoor attacks. These attacks allow adversaries to manipulate system behavior through hidden triggers while maintaining normal operation on clean inputs. To address this challenge, we propose a novel defense framework that combines speech enhancement with neural architecture optimization. Our approach consists of three key steps. First, we use a ComplexMTASS-based enhancement network to isolate and remove backdoor triggers by leveraging their unique spectral characteristics. Second, we apply an adaptive fine-pruning algorithm to selectively deactivate malicious neurons while preserving the model’s linguistic capabilities. Finally, we fine-tune the pruned model using clean data to restore and enhance recognition accuracy. Experiments on the AISHELL dataset demonstrate the effectiveness of our method against advanced steganographic attacks, such as PBSM and VSVC. The results show a significant reduction in attack success rate to below 1.5%, while maintaining 99.4% accuracy on clean inputs. This represents a notable improvement over existing defenses, particularly under varying trigger intensities and poisoning rates.

Aim : This study aimed to assess the accuracy of deep learning (DL) models trained and tested on thigh-worn accelerometry from children to estimate their postures and movements and compare their performance metrics against more traditional machine learning (ML) models and a decision tree algorithm. Method : This study utilized data from a laboratory study involving 48 children (3–14 years old), wearing a SENS accelerometer on the thigh during a 1-hr structured laboratory session. Human coders annotated nine different postures and movement categories from video data (30 Hz). The raw accelerometer data was processed using two traditional ML models: Balanced Random Forest and Extreme Gradient Boost; two DL models: a Long Short-Term Memory-Convolutional Neural Network with (a) standard deviation and (b) minmax normalization; and a decision tree algorithm (ActiMotus). The efficacy of each model was compared using accuracy, precision, recall, and F1-scores. Results : Across all movement categories, the Extreme Gradient Boost ML model (F1 = 0.769) was found to slightly outperform the DL models (F1 = 0.743 and 0.728), with all ML and DL models performing statistically better than the widely used decision tree model (F1 = 0.645). These differences in performance were consistent across all postures and movement categories with the exception of standing and walking. Conclusions : The results of this study suggest that DL models are not necessarily superior to ML models for the classification of children’s posture and movements, but both are more accurate than a currently available decision tree algorithm. Future work should continue to refine deep and ML models for classifying children’s physical behaviors.