This paper investigates the problem of dynamical sampling for graph signals influenced by a constant source term. We consider signals evolving over time according to a linear dynamical system on a graph, where both the initial state and the source term are bandlimited. We introduce two random space-time sampling regimes and analyze the conditions under which stable recovery is achievable. While our framework extends recent work on homogeneous dynamics, it addresses a fundamentally different setting where the evolution includes a constant source term. This results in a non-orthogonal-diagonalizable system matrix, rendering classical spectral techniques inapplicable and introducing new challenges in sampling design, stability analysis, and joint recovery of both the initial state and the forcing term. A key component of our analysis is the spectral graph weighted coherence, which characterizes the interplay between the sampling distribution and the graph structure. We establish sampling complexity bounds ensuring stable recovery via the Restricted Isometry Property (RIP), and develop a robust recovery algorithm with provable error guarantees. The effectiveness of our method is validated through extensive experiments on both synthetic and real-world datasets.

With the widespread use of modern browser extensions, user experience has been significantly enhanced via embedding ancillary functionality into the original webpage. The rapid development of Web tracking technology has raised privacy and security concerns, as it generates a unique identifier for users according to the diversity of installed extensions and further prompts the profiling of users. However, due to the ignorance of potential privacy risks, there is no effective method to detect browser extension fingerprinting. In this paper, we propose ExtFPDet, a CNN-based detection framework to recognize browser extension fingerprinting in websites, which fills the gap in this area. Based on the preliminary investigation, the approaches to fingerprint browser extensions can be summarized into 2 categories according to the distinctive behaviors, including resource traversing and side-channel exploring. In order to extract effective features to reflect extensions fingerprinting, ExtFPDet focuses on the structure and content in the program dependency graph of Javascript files. The generated feature vector assists the CNN-based classification model to detect the extension fingerprinting, for which we perform a systematic detection on Tranco top 10K websites. Eventually, the result is evaluated by randomly sampling and manually checking, which shows superior detection capabilities of ExtFPDet.