The structure of topology underpins much of the research on performance and robustness, yet available topology data are typically scarce, necessitating the generation of synthetic graphs with desired properties for testing or release. Prior diffusion-based approaches either embed conditions into the diffusion model, requiring retraining for each attribute and hindering real-time applicability, or use classifier-based guidance post-training, which does not account for topology scale and practical constraints. In this paper, we show from a discrete perspective that gradients from a pre-trained graph?level classifier can be incorporated into the discrete reverse diffusion posterior to steer generation toward specified structural properties. Based on this insight, we propose Classifier-guided Conditional Topology Generation with Persistent Homology (CoPHo), which builds a persistent homology filtration over intermediate graphs and interprets features as guidance signals that steer generation toward the desired properties at each denoising step. Experiments on four generic/network datasets demonstrate that CoPHo outperforms existing methods at matching target metrics, and we further validate its transferability on the QM9 molecular dataset. The code is available at https://github.com/Lrbomchz/CoPHo.

The rapid proliferation of IoT-enabled cameras has driven increasing demand for low-latency, intelligent video understanding in real-world applications such as smart cities and industrial automation. While Multimodal Large Language Models (MLLMs) offer unprecedented capabilities in semantic reasoning and natural language-based video comprehension, their deployment in latency-sensitive IoT environments remains challenging due to high computational costs and sequential decoding bottlenecks. Moreover, conventional edge-cloud video analysis frameworks often rely on semantic-agnostic frame sampling, leading to information loss or redundant data transmission. In this paper, we proposeSEC, a semantic-aware edge-cloud collaborative framework for efficient and accurate video analysis.SECintroduces a task-aware key frame selection mechanism at the edge to maximize semantic relevance while minimizing bandwidth usage, and a novel adaptive speculative decoding framework with tree-based parallel generation on the cloud to accelerate MLLM inference. Extensive experiments under realistic edge-cloud deployment settings on four video understanding benchmarks demonstrate that the proposedSECframework achieves superior performance, significantly reducing the end-to-end inference latency while improving the accuracy, a rare win-win in latency-critical IoT systems.

Task scheduling is a critical component in edge computing. Many advanced scheduling strategies select the most suitable execution node based on user-reported resource requirements, thereby enhancing user experience. However, the reliance on user-reported resource demands presents a potential vulnerability, as malicious users could exploit this to disrupt edge servers. In this paper, we propose a hypothetical offloading attack scenario in which an attacker submits tasks with falsified resource demands, directing these tasks to specific nodes. A high volume of malicious tasks leads to congestion at the targeted node, causing delays and potential failures for latency-sensitive tasks. Due to the black-box nature of the offloading strategy and the unknown system environment, we model the task parameter manipulation as a multi-dimensional multi-armed bandit (MAB) process with non-stationary rewards. This approach aims to maximize the attack efficiency within limited attack costs. We introduce UCB algorithm for Multidimensional space with Cost constraints and Non-stationary rewards(UCB-MCN), an extended MAB algorithm designed to optimize the attacker's cumulative reward. In a simulated environment, UCB-MCN is tested against various offloading strategies and defense mechanisms. Evaluation results demonstrate the effectiveness of UCB-MCN and highlight new security concerns within edge computing.

Recently, transformer models have demonstrated superior performance in video tasks. However, a prevalent limitation in most current video Transformers lies in their tendency to overlook inherent temporal regions of interest, such as motion trajectories, leading to susceptibility to redundant information during temporal modeling. Existing methods that pay attention to motion trajectories have high computational demands, lacking in lightweight efficiency. To strike a balance between effective modeling of temporal regions of interest and computational efficiency, we propose a video transformer backbone with deformable temporal attention (DTA). Inspired by the work on deformable receptive fields, DTA employs a lightweight decision network to enhance the flexibility of temporal attention. The decision network computes the offsets of tokens in the input feature map, enabling them to move to temporally relevant regions of interest and efficiently model temporal information. We conducted extensive experiments on three popular datasets and surpassed the baseline. Additionally, we performed ablation experiments specifically targeting the model structure and parameters. These results confirm the effectiveness of the proposed deformable temporal attention mechanism.

Masked image modeling (MIM), as a self-supervised learning paradigm in computer vision, has gained widespread attention among researchers. MIM operates by training the model to predict masked patches of the image. Given the sparse nature of image semantics, it is imperative to devise a masking strategy that steers the model towards reconstructing high-semantic regions. However, conventional mask strategies often miss these high-semantic regions or lack alignment with the masks and semantics. To solve this, we propose the Global Patch-wise Attention (GPA) framework, a transferable and efficient framework for MIM pre-training. We observe that the attention between patches can be the metric of identifying high-semantic regions, which can guide the model to learn more effective representations. Therefore, we firstly define the global patch-wise attention via vision transformer blocks. Then we design the soft-to-hard mask generation to guide the model gradually focusing on high semantic regions identified by GPA (GPA as a teacher). Finally, we design an extra task to predict GPA (GPA as a feature). Experiments conducted under various settings demonstrate that our proposed GPA framework enables MIM to learn better representations, which benefit the model across a wide range of downstream tasks. Furthermore, our GPA framework can be easily and effectively transferred to various MIM architectures.