The prevalence of real-world multi-view data makes incomplete multi-view clustering (IMVC) a crucial research. The rapid development of Graph Neural Networks (GNNs) has established them as one of the mainstream approaches for multi-view clustering. Despite significant progress in GNNs-based IMVC, some challenges remain: (1) Most methods rely on the K-Nearest Neighbors (KNN) algorithm to construct static graphs from raw data, which introduces noise and diminishes the robustness of the graph topology. (2) Existing methods typically utilize the Mean Squared Error (MSE) loss between the reconstructed graph and the sparse adjacency graph directly as the graph reconstruction loss, leading to substantial gradient noise during optimization. To address these issues, we propose a novel Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss (DGIMVCM). Firstly, we construct a missing-robust global graph from the raw data. A graph convolutional embedding layer is then designed to extract primary features and refined dynamic view-specific graph structures, leveraging the global graph for imputation of missing views. This process is complemented by graph structure contrastive learning, which identifies consistency among view-specific graph structures. Secondly, a graph self-attention encoder is introduced to extract high-level representations based on the imputed primary features and view-specific graphs, and is optimized with a masked graph reconstruction loss to mitigate gradient noise during optimization. Finally, a clustering module is constructed and optimized through a pseudo-label self-supervised training mechanism. Extensive experiments on multiple datasets validate the effectiveness and superiority of DGIMVCM.

Session-based recommendation (SBR) aims to recommend items based on anonymous behavior sequences. Methods based on graph neural networks always ignore two issues. First, they ignore the multiple user interests hidden in sessions. Second, they do not pay much attention to the impact of the over-smoothing phenomenon in graph neural networks. To address these two problems, this paper proposes a novel multi-interest awareness (MIA) model by modeling multiple interests explicitly for SBR. First, we design a multi-interest extraction module to extract user interests hidden in sessions. Specifically, a bilinear mapping matrix models multiple user interests by mapping items in the session to different interest capsules. A gating network allows the session to select different user interests through the gating mechanism. Then, the interest aggregation module uses an interest anchor to aggregate effective user interest information at each layer, so that the over-smoothing problem is alleviated. Experimental results show that our approach is competitive compared to the state-of-the-art models.

Gait-based authentication has risen to prominence for its distinctive advantages, becoming an essential security mechanism for mobile devices. These devices typically employ Inertial Measurement Units (IMUs) to capture intricate gait patterns for confirming the identity of users. However, our research highlights a vulnerability: the user’s gait data on mobile devices is susceptible to interception through a radio frequency (RF) side-channel, potentially allowing unauthorized access. We introduce Gait-Snoop as aproof-of-concept for this novel side-channel attack. Gait-Snoop utilizes the RF signals reflected during a user’s walk to extract gait information. It then correlates these RF signal patterns with IMU-derived gait data and employs a robotic arm to replicate the gait, aiming to deceive and unlock the targeted mobile devices. Our comprehensive evaluation of Gait-Snoop on smartphones demonstrates its capability to mimic IMU gait signals, underscoring the effectiveness and potential risks of such side-channel attacks.