Mental disorders are among the foremost contributors to the global healthcare challenge. Research indicates that timely diagnosis and intervention are vital in treating various mental disorders. However, the early somatization symptoms of certain mental disorders may not be immediately evident, often resulting in their oversight and misdiagnosis. Additionally, the traditional diagnosis methods incur high time and cost. Deep learning methods based on fMRI and EEG have improved the efficiency of the mental disorder detection process. However, the cost of the equipment and trained staff are generally huge. Moreover, most systems are only trained for a specific mental disorder and are not general-purpose. Recently, physiological studies have shown that there are some speech and facial-related symptoms in a few mental disorders (e.g., depression and ADHD). In this paper, we focus on the emotional expression features of mental disorders and introduce a multimodal mental disorder diagnosis system based on audio-visual information input. Our proposed system is based on spatial-temporal attention networks and innovative uses a less computationally intensive pre-train audio recognition network to fine-tune the video recognition module for better results. We also apply the unified system for multiple mental disorders (ADHD and depression) for the first time. The proposed system achieves over 80% accuracy on the real multimodal ADHD dataset and achieves state-of-the-art results on the depression dataset AVEC 2014.

This paper presents a novel method for differentiating Attention Deficit Hyperactivity Disorder subjects from control participants by multimodal data fusion, including video observations and questionnaire responses. By exploiting the well known Video Vision Transformer model, we analyse the video modality to identify the complex spatial-temporal information of ADHD symptoms. Simultaneously, a Multi-Layer Perceptron model is applied to evaluate structured questionnaire data by capturing key cognitive and emotional indicators of the ADHD symptoms. To fuse the two modalities, a cross-modal attention mechanism assigns adaptive weights to each feature based on its classification relevance. The targeted weighting significantly refines the proposed model’s decision-making capability by concentrating on the most critical elements of the aggregated information. For training and testing, our novel Multimodal ADHD dataset recorded under the Intelligent Sensing ADHD Trial in collaboration with Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust UK is evaluated. The proposed model, ADViQ-AL achieves a 98.18% classification accuracy, 97.83% sensitivity, and 98.53% specificity in classifying ADHD and control groups.

Due to maritime transportation being the most crucial mode in international trade, maritime traffic safety significantly influences global economic development. Detecting anomalous ship behaviors (DASB) serves as a critical measure to safeguard maritime traffic safety. In recent years, data-driven deep learning technologies have witnessed remarkable advancements, and the introduction of high-quality DASB datasets facilitates the rapid and effective transformation of traditional DASB methods into intelligent ones. In this paper, we initially present a labeled DASB dataset named NCL-DASB, recorded at the Tynemouth port in Newcastle, UK. Subsequently, we propose a standard framework for processing vessel AIS data, enabling the transformation of AIS data into vessel trajectory feature information suitable for deep learning through preprocessing. Finally, we open-source an API for annotating vessel trajectory data in the NCL-DASB dataset, intended for the use of future researchers in their studies.

The use of multiple data sources (measurements) has been recently demonstrated to improve the accuracy and reliability of a tracking system as it is capable of providing redundancy in different aspects, and also eliminating interferences of individual sources. This paper focuses on addressing the multiple human tracking problem from a multi-detector approach. This approach integrates two detectors with different characteristics (full-body and body-parts) to perform robust collaborative fusion based on data-driven Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters. To leverage the maximum strengths from multiple detectors, we propose a robust fusion center at the track level, which manages to perform Generalized Intersection Covariance (GCI) fusions for survival and birth tracks independently, and also eliminates false tracks caused by a cluttered environment. Moreover, an identity reassignment mechanism is also developed to address the identity mismatching problem in the target birth process, so as to enhance the fusion performance and track consistency. Experimental results on two challenging benchmark video sequences confirm the effectiveness of the proposed approach.