With the increasing use of electronic devices, online handwriting verification has become crucial for biometricsbased identity authentication. Traditional methods, which rely on content-dependent verification of the writer's name, are vulnerable to forgery. This paper introduces a content-independent handwriting authentication system, Ph-Wri, designed for commodity smartphones. The core innovation is a multi-path attention feature fusion network that combines both static features (image of the handwritten text) and dynamic features (time-dependent properties during writing), to abstract the handwriting style instead of specific content for recognition, enabling robust user authentication. To extract handwriting style from dynamic writing features, we propose a polarity-aware attention strategy during training. This strategy incorporates Style Channel Attention (SCA) to capture direction-sensitive stylistic features, and Trajectory Spatial Attention (TSA) to highlight key handwriting trajectory regions. In the fine-tuning stage, the Correlation-Aware Attention (CAA) module models inter-channel structural correlations, mitigating the influence of content and enhancing style-consistent representations. By linking content-independent handwriting style to user identity, the system achieves accurate authentication. Extensive experiments on both the self-built CIEHD dataset and the public BiosecurID dataset demonstrate exceptional performance, achieving a 99% Verification Accuracy on CIEHD. Compared to state-of-theart methods that utilize only static or dynamic data, Ph-Wri significantly reduces the Equal Error Rate, showcasing the effectiveness and practicality of the proposed approach.

Video-based multimodal depression estimation is a challenging task that relies on comprehensive video understanding and analysis to predict depression severity. This process involves capturing discriminative information from multimodal features, including auditory, textual, and visual signals in the video data. Existing foundation models primarily focus on fusing multimodal features but often overlook two essential aspects: (1) the alignment of intra- and inter-modality and (2) preserving modality-specific semantic information while eliminating redundancy . Hence, these issues inevitably result in semantic misalignment between modalities, leading the model to learn redundant or even conflicting representations. To tackle these challenges, we propose a Contrastive Modality Alignment and Fusion (CMAF) framework. Specifically, each modality is divided into multiple segments, and a dynamic normalization strategy is applied to align the temporal context within each modality. Simultaneously, contrastive learning is utilized to achieve semantic alignment across different modalities. Together, these components form the Contextual Modality Alignment (CMA) module. Furthermore, a Cross-Attention Contrastive Fusion (CCF) module is presented, where cross-attention is used to achieve complementarity and enhancement between modalities, while contrastive learning further promotes modal consistency and preserves modality-specific information without redundancy. Therefore, the fused representation can integrate the features of each modality without losing the specific information of the unimodal. Extensive experiments on public datasets, including CMDC, DAIC-WOZ, and E-DAIC, demonstrate that CMAF outperforms advanced depression estimation methods and highlights the essence of effective alignment and fusion.

Depression is a prevalent mental health disorder that significantly impacts individuals' lives and well-being. Early detection and intervention are crucial for effective treatment and management of depression. Recently, there are many end-to-end deep learning methods leveraging the facial expression features for automatic depression detection. However, most current methods overlook the temporal dynamics of facial expressions. Although very recent 3DCNN methods remedy this gap, they introduce more computational cost due to the selection of CNN-based backbones and redundant facial features. To address the above limitations, by considering the timing correlation of facial expressions, we propose a novel framework called FacialPulse, which recognizes depression with high accuracy and speed. By harnessing the bidirectional nature and proficiently addressing long-term dependencies, the Facial Motion Modeling Module (FMMM) is designed in FacialPulse to fully capture temporal features. Since the proposed FMMM has parallel processing capabilities and has the gate mechanism to mitigate gradient vanishing, this module can also significantly boost the training speed. Besides, to effectively use facial landmarks to replace original images to decrease information redundancy, a Facial Landmark Calibration Module (FLCM) is designed to eliminate facial landmark errors to further improve recognition accuracy. Extensive experiments on the AVEC2014 dataset and MMDA dataset (a depression dataset) demonstrate the superiority of FacialPulse on recognition accuracy and speed, with the average MAE (Mean Absolute Error) decreased by 21% compared to baselines, and the recognition speed increased by 100% compared to state-of-the-art methods. Codes are released at https://github.com/volatileee/FacialPulse.

Graph Contrastive Learning (GCL) has achieved great success in self-supervised representation learning throughout positive and negative pairs based on graph neural networks (GNNs), where one critical issue lies in how to handle the false hard negatives that share the large similarity to the same referenced class as the anchor, which is critical to message passing of GNNs to exploit the graph structure. However, the existing arts either mistakenly identify or miss the false hard negatives, hence resulting into poor node representation. Building on this, there are several crucial bottlenecks —Where do false hard negatives exist upon the anchor? How to well seek false hard negatives? Whether are more false hard negatives better?To answer these questions, in this paper, we propose a novel Locally Weighted Graph Contrastive Learning method, named LocWGCL, while revealing that false hard negatives are primarily distributed in the first-order and second-order neighborhoods of the anchor. Benefiting from the tightness between the first-order nodes and the anchor, representation similarity is calculated to select false hard negatives. For the second-order case, false hard negatives are identified, such that they share the similar passed message with the anchor over the common first-order nodes, along with the large similarity. Upon the seeking process, we devise a weighted strategy to false hard negatives for better node representation. Empirical studies verify the advantages of LocWGCL over the state-of-the-arts on six benchmarks.

As a popular way of representing holographic video or volumetric video, point cloud video can provide users with a highly immersive viewing experience of 6 degrees of freedom (6DoF) and is expected to become the mainstream video format of the future. However, the real-time transmission of point cloud video faces many challenges due to the huge amount of data and the large search space of the optimization problem with constraints. To this end, we propose Horizon, a novel Dynamic Adaptive Streaming over HTTP (DASH) based real-time point cloud video streaming system, which aims to maximize the user's viewing experience by predicting the next several steps through a rolling framework and uses a Deep Reinforcement Learning (DRL) based algorithm to achieve a real-time solution to the rolling optimization problem. We have prototyped this system and demonstrated its performance on a state-of-the-art wireless network.