The increasing availability of Web APIs has amplified the complexity of mashup creation, where developers must identify compatible and functionally relevant APIs based on often ambiguous natural language descriptions. Traditional methods also fall short in capturing hierarchical semantic cues, modeling compatibility, and aligning with developer intent. Although large language models (LLMs) offer strong generalization capabilities, they remain unreliable in mashup recommendation due to hallucinated outputs, limited controllability, and token-length constraints when dealing with large-scale API repositories. To overcome these limitations, we introduceLIMR, an intent-aware mashup recommendation framework that combines LLM-augmented semantic reasoning with structured, multi-scale neural modeling.LIMRfirst prompts a LLM to extract high-level intent from user requirements, which serves as a global semantic signal. This intent is fused with low-level, multi-scale features extracted by a convolutional encoder, which are designed to capture fine-grained lexical/phrasal patterns at different granularities and provide precise semantic grounding for API matching. These heterogeneous representations are further contextually refined through a Transformer-based interaction module. To handle nonlinear semantic dependencies and compositional complexity,LIMRintegrates a Kolmogorov-Arnold Network (KAN) with learnable activation functions, enhancing the model's capacity to capture intricate feature interactions. The entire framework is optimized via LLM, incorporating auxiliary objectives such as mashup category prediction and API quality estimation to guide generalization and reduce overfitting. Comprehensive experiments on the ProgrammableWeb and APIBench datasets show thatLIMRsignificantly outperforms state-of-the-art baselines, which the ranking-oriented metrics, including NDCG and mAP, achieves improvements of 17.1%–34.2% over the strongest competitors. These results confirm the effectiveness ofLIMR's hybrid design in delivering precise, robust, and intent-aware mashup API recommendations, especially in scenarios where LLMs alone fail to meet accuracy and scalability demands.

Accurate river velocity prediction plays a vital role in water resource management and hydraulic engineering. Seasonal variations and weather conditions introduce complex temporal patterns, making prediction a challenging task. Traditional methods struggle to account for both short-term fluctuations and long-term trends, and they fail to fully exploit temporal information. To overcome these limitations, we propose SeekRiver, a prediction model that integrates both long- and short-term features. We first transform time features into low-dimensional vectors through an embedding layer, enhancing their temporal representation. The model uses two LSTM layers to separately capture short-term and long-term dependencies. An attention mechanism is employed to emphasize crucial features while minimizing the impact of irrelevant ones. A weighted fusion strategy then combines multi-scale features to improve prediction accuracy. Extensive experiments, including comparisons with baseline models and ablation studies, validate SeekRiver’s effectiveness. The model significantly outperforms traditional methods, with the attention mechanism and short-term feature module contributing the most to the performance improvement, which offers a robust and efficient solution for river velocity prediction.

One of the trends in the evolution of Android malware is the increasing diversity of malicious behaviors, such as SMSrelated and Internet-related actions. Traditional binary or familybased classification methods are inadequate for fine-grained detection of these behaviors. Thus, multi-label classification is required to identify various malicious behaviors within a single malware sample. This paper employs an active learning strategy to add multi-behavior labels to large-scale datasets based on expert-annotated small-scale datasets. To address the issue of noisy labels (simulating real-world mislabeling), we propose CalmDroid, an active learning framework utilizing the coreset strategy, instead of the confuse-set strategy for updating the model with out-of-distribution (OOD) points. We evaluate CalmDroid's performance using the Drebin and VirusShare datasets. Experimental results demonstrate that CalmDroid achieves superior detection performance under varying noise conditions, with an accuracy improvement of up to 0.704 compared to the confuse-set strategy. In high-noise environments (15%), it reaches detection accuracy as high as 0.944. Additionally, we validate CalmDroid's capability to detect evolving malware. Despite behavioral evolution in Drebin malware across different time steps, CalmDroid consistently achieves detection rates above 70 % in the newest time step.

Trajectory data analysis is crucial in various applications but presents significant privacy risks, as location data can reveal sensitive information. Existing privacy protection methods, such as spatiotemporal K-anonymity and L-diversity, are vulnerable to semantic inference attacks, where public data is exploited to re-identify users. To address these challenges, we propose dummy-trajectory synthesis (DTS), an efficient privacy protection scheme for location-based social networks (LBSNs). DTS enhances privacy by leveraging users’ frequent behavioral sequences to generate synthetic dummy trajectories. Unlike traditional approaches, DTS considers both geographic and semantic data by segmenting historical trajectories into time periods using the OPTICS clustering algorithm. This enables the identification of regions with specific semantic attributes and the mining of semantic trajectory sequences. DTS optimizes dummy trajectory generation by combining Euclidean distances and semantic similarity, ranking historical points and establishing transition relationships. Experimental results show that DTS significantly improves privacy protection and performance compared to existing methods, without compromising service quality. DTS offers a robust solution for protecting trajectory data privacy in LBSNs against transition probability attack for joint time periods.