Existing backdoor attacks on multivariate time series (MTS) forecasting enforce strict temporal and dimensional coupling between triggers and target patterns, requiring synchronous activation at fixed positions across variables. However, realistic scenarios often demand delayed and variable-specific activation. We identify this critical unmet need and propose TDBA, a temporally decoupled backdoor attack framework for MTS forecasting. By injecting triggers that encode the expected location of the target pattern, TDBA enables the activation of the target pattern at any positions within the forecasted data, with the activation position flexibly varying across different variable dimensions. TDBA introduces two core modules: (1) a position-guided trigger generation mechanism that leverages smoothed Gaussian priors to generate triggers that are position-related to the predefined target pattern; and (2) a position-aware optimization module that assigns soft weights based on trigger completeness, pattern coverage, and temporal offset, facilitating targeted and stealthy attack optimization. Extensive experiments on real-world datasets show that TDBA consistently outperforms existing baselines in effectiveness while maintaining good stealthiness. Ablation studies confirm the controllability and robustness of its design.

In the digital age, software security is essential for the stability of information systems and data protection, yet increasing complexity in software systems has made vulnerabilities a significant cybersecurity threat, leading to data breaches, system crashes, and service disruptions. Traditional vulnerability assessments usually analyze vulnerabilities in isolation, ignoring their time relations and the risk of attackers exploiting multiple vulnerabilities simultaneously, known as co-exploitation. This paper proposes an innovative time-aware cybersecurity knowledge graph (TCG) reasoning method called TCGFormer, which is designed to address these challenges. TCGFormer comprises four modules: (1) an entity encoding module that adjusts attention based on positional information and interaction frequency, (2) a novel attention mechanism for encoding relational topology graphs, (3) a joint sequence encoding module for extracting temporal representations and node relations from historical interactions, and (4) a parameter learning module for predicting entities and relations. Extensive experiments on three public temporal datasets demonstrate that TCGFormer significantly outperforms existing baseline methods, and validation on a cybersecurity knowledge graph dataset—including NVD, CVE details, CWE database, and EDB—further confirms its efficacy in identifying co-exploitation behaviors.

The geographic location of social media users is crucial for understanding user behavior, optimizing advertising, and supporting location-based services such as emergency awareness and event monitoring services. However, existing Twitter user geolocation methods primarily focus on explicit social relationships between users while overlooking implicit location correlations, which affects the accuracy of user geolocation. To address this, this article proposes a Twitter user geolocation method (GeoICMF) based on implicit location correlations and multi-scale feature fusion. GeoICMF introduces a novel location association graph construction method to effectively capture implicit location correlations among users, an innovative multi-scale feature fusion model to dynamically fuse multi-scale features and generate richer user representations, and a pioneering geographic partitioning method to better adapt to user location distributions and enhance geolocation accuracy. Extensive experiments on three real-world datasets demonstrate that GeoICMF outperforms state-of-the-art baseline methods in Twitter user geolocation tasks, validating the effectiveness and superiority of the proposed method.

The multi-window mode in Android has greatly improved productivity and usability by allowing multiple apps to run concurrently. However, alongside the advantages, such mode also introduces unforeseen risks in both functionality and security. In this work, we present the first systematic study to identify a previously unexplored class of issues, termed Multi-window Resource Conflicts (MRCs). Such conflicts occur when multiple app windows access the same system resource concurrently, potentially leading to crashes, functionality failures or unintended behaviors. To enhance the robustness and security of Android multi-window execution, we conduct a systematic and in-depth empirical study on the MRCs. We begin with a comprehensive root cause analysis, categorizing MRCs into three fundamental types based on their triggering patterns and affected resource states. To enable large-scale detection, we develop MRC-Detector, a static analysis framework that automatically identifies MRC issues in Android apps. Our manual verification confirms its high accuracy and effectiveness. We apply the MRC-Detector to the detection of over 150k real-world apps from F-droid and Google Play, uncovering the prevalence of MRC risks. Additionally, the distribution of MRC issues is analyzed in depth across multiple dimensions, including MRC type, APK size, app source and security classification. We further investigated the recognition and confirmation from developers and received 14 positive responses from vendors and project maintainers. Finally, comprehensive mitigation strategies are discussed. The materials of the study are available at: https://github.com/Huimilia/MRC.

The geolocation of social users is crucial for understanding user behavior, optimizing advertisement placement, and enhancing public safety.However, existing methods tend to show some deficiencies when handling sparse datasets and may not fully capture the natural clustering characteristics of user locations, thereby resulting in inadequate geolocation accuracy.This paper proposes a novel social user geolocation method (MILGCN) that innovatively integrates Mean Shift Clustering, Incremental Learning, and Graph Convolutional Networks.Specifically, Mean Shift performs fine-grained clustering of user locations based on density peak characteristics, ensuring that geographically close users are grouped into the same cluster.Introducing an incremental learning mechanism into graph convolutional networks enables MILGCN to have progressive learning ability.As a result, the problem of incomplete feature extraction from sparse data is alleviated, resulting in more comprehensive user features and improved geolocation accuracy.Extensive experiments proved that the proposed method significantly outperforms the state-of-the-art baselines on the real Twitter datasets, demonstrating a substantial improvement in geolocation performance.

Stance detection, which focuses on users' deep attitudes, is an important way to understand the online public opinion. This paper presents an overview of stance detection. First, we present a general framework for stance detection, and the main steps of the framework are introduced in detail. The state-of-the-art stance detection methods are categorized into three classes: feature-based methods, deep learning-based methods, and ensemble learning-based methods. Moreover, the advantages and limitations of the existing methods are analyzed. The survey findings show that hybrid-neural network-based methods are superior to the other methods. In addition, existing methods still need to pay more attention to the sentiment information, user-interaction, and attempt to merge more external knowledge to help improve the effect of stance detection.

Cross-network anchor link discovery is an important research problem and has many applications in heterogeneous social network. Existing schemes of cross-network anchor link discovery can provide reasonable link discovery results, but the quality of these results depends on the features of the platform. Therefore, there is no theoretical guarantee to the stability. This article employs user embedding feature to model the relationship between cross-platform accounts, that is, the more similar the user embedding features are, the more similar the two accounts are. The similarity of user embedding features is determined by the distance of the user features in the latent space. Based on the user embedding features, this article proposes an embedding representation-based method Con&Net(Content and Network) to solve cross-network anchor link discovery problem. Con&Net combines the user’s profile features, user-generated content (UGC) features, and user’s social structure features to measure the similarity of two user accounts. Con&Net first trains the user’s profile features to get profile embedding. Then it trains the network structure of the nodes to get structure embedding. It connects the two features through vector concatenating, and calculates the cosine similarity of the vector based on the embedding vector. This cosine similarity is used to measure the similarity of the user accounts. Finally, Con&Net predicts the link based on similarity for account pairs across the two networks. A large number of experiments in Sina Weibo and Twitter networks show that the proposed method Con&Net is better than state-of-the-art method. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve predicted by the anchor link is 11% higher than the baseline method, and Precision@30 is 25% higher than the baseline method.

Social network user location inference technology has been widely used in various geospatial applications like public health monitoring and local advertising recommendation. Due to insufficient consideration of relationships between users and location indicative words, most of existing inference methods estimate label propagation probabilities solely based on statistical features, resulting in large location inference error. In this paper, a Twitter user location inference method based on representation learning and label propagation is proposed. Firstly, the heterogeneous connection relation graph is constructed based on relationships between Twitter users and relationships between users and location indicative words, and relationships unrelated to geographic attributes are filtered. Then, vector representations of users are learnt from the connection relation graph. Finally, label propagation probabilities between adjacent users are calculated based on vector representations, and the locations of unknown users are predicted through iterative label propagation. Experiments on two representative Twitter datasets - GeoText and TwUs, show that the proposed method can accurately calculate label propagation probabilities based on vector representations and improve the accuracy of location inference. Compared with existing typical Twitter user location inference methods - GCN and MLP-TXT+NET, the median error distance of the proposed method is reduced by 18% and 16%, respectively.