Zero-shot event extraction (ZSEE) remains a significant challenge for large language models (LLMs) due to the need for complex reasoning and domain-specific understanding. Direct prompting often yields incomplete or structurally invalid outputs—such as misclassified triggers, missing arguments, and schema violations. To address these limitations, we present Agent-Event-Coder (AEC), a novel multi-agent framework that treats event extraction like software engineering: as a structured, iterative code-generation process. AEC decomposes ZSEE into specialized subtasks—retrieval, planning, coding, and verification—each handled by a dedicated LLM agent. Event schemas are represented as executable class definitions, enabling deterministic validation and precise feedback via a verification agent. This programming-inspired approach allows for systematic disambiguation and schema enforcement through iterative refinement. By leveraging collaborative agent workflows, AEC enables LLMs to produce precise, complete, and schema-consistent extractions in zero-shot settings. Experiments across five diverse domains and six LLMs demonstrate that AEC consistently outperforms prior zero-shot baselines, showcasing the power of treating event extraction like code generation.

Marked Temporal Point Processes (MTPPs) provide a principled framework for modeling asynchronous event sequences by conditioning on the history of past events. However, most existing MTPP models rely on channel-mixing strategies that encode information from different event types into a single, fixed-size latent representation. This entanglement can obscure type-specific dynamics, leading to performance degradation and increased risk of overfitting. In this work, we introduce ITPP, a novel channel-independent architecture for MTPP modeling that decouples event type information using an encoder-decoder framework with an ODE-based backbone. Central to ITPP is a type-aware inverted self-attention mechanism, designed to explicitly model inter-channel correlations among heterogeneous event types. This architecture enhances effectiveness and robustness while reducing overfitting. Comprehensive experiments on multiple real-world and synthetic datasets demonstrate that ITPP consistently outperforms state-of-the-art MTPP models in both predictive accuracy and generalization.

In Internet of Things (IoT) scenarios, vision-language models (VLMs) are increasingly employed for visual perception and reasoning. However, their inherent tendency toward hallucinated and post-hoc rationalized reasoning often produces false positives (FPs), where the final answer is correct but supported by unreliable reasoning paths, typically manifested as self-inconsistent reasoning. Existing methods primarily focus on forward enhancement of reasoning ability, while neglecting reverse verification of reasoning consistency. To address this issue, we propose ViFP, a training-free framework for visual FP detection and correction. ViFP identifies FPs through consistency analysis between direct reasoning and structured multi-step reasoning, uncovering self-inconsistent reasoning without relying on superior supervisory models. Based on detected FPs, ViFP adaptively optimizes reasoning paths by refining question types and reasoning chain templates. Furthermore, we introduce a novel reliability metric, Value of Correction (VoC), which quantitatively measures the benefit of FP correction by jointly considering accuracy improvement, true negative rate, and FP reduction. VoC provides an interpretable indicator of reasoning reliability beyond conventional accuracy metrics. ViFP is designed as a cloud/edge-side reasoning reliability service for IoT systems and is compatible with leading closed-source VLMs. Experiments on A-OKVQA, OK-VQA, and FVQA demonstrate that ViFP significantly improves reasoning accuracy and reduces FPs, achieving up to 5.4% accuracy gain and surpassing previous state-of-the-art performance.

Blockchain-based Federated Learning (BFL) facilitates collaborative model training with guaranteed transparency and accountability across distributed participants. However, BFL encounters a critical security vulnerability: advanced free-riders can exploit the public on-chain model histories to synthesize fabricated gradients without performing actual local training, which is indistinguishable from legitimate contributions. Previous efforts to defense free-rider attack mostly focus on anomaly detection, but they struggle to detect some advanced free-riders. To address this fundamental challenge, we propose BFLGR (Blockchain-based Federated Learning with Gresham's Reversal), which is a novel framework integrating reverse auction mechanisms and a dynamic reputation system. BFLGR employs a dynamic selection algorithm that can eliminate advanced free-riders by exploiting their profit-seeking motives and parasitic dependency on honest participants' contributions. This framework ensures genuine contributions prevail over deceptive submissions, and achieves a paradigmatic shift in BFL security, transforming the transparency-exploitation vulnerability into a strategic advantage for detecting and eliminating malicious participants. Theoretical proofs and extensive experiments show that BFLGR performs well in advanced free-rider attack situation and outperforms our baselines.

Community detection is a crucial task in complex network analysis, and existing game-theoretic-based community detection methods struggle to achieve a balance between local and global competition and cooperation. This paper introduce a Dual-scale Game theoretical Framework (Dual-GTF) for community detection. First, a new modularity function is proposed, enabling each node to incorporate additional expansion information at the community scale by collaborating closely with the individual scale modularity function. Subsequently, leveraging cooperative and competitive strategies informed by realistic empiricism, this paper integrate community-scale information to facilitate effective role division within communities. Evaluations of real-world datasets demonstrate that this approach, Dual-GTF, achieves superior convergence and performance results in most cases.

The development of Internet of Things systems (IoTs) and 5G technology has allowed image and text information to be collected and spread at an unprecedentedly high speed. To improve the data processing capabilities of IoTs, the semantic relations between images and text should be extracted efficiently and accurately. Therefore, to reduce the enormous semantic differences between images and text, existing methods introduce consensus knowledge graphs into image–text matching tasks. However, these methods result in noisy edges during the graph construction stage and overlook detailed knowledge extraction, leading to reduced performance in semantic matching. In this article, a two-layer heterogeneous knowledge graph network is proposed to solve the above problems. The proposed model incorporates category knowledge and local knowledge for improved data representation. Specifically, a category-based hierarchical knowledge graph is constructed to learn representations of knowledge concepts through a hierarchical correlation graph embedding (HCGE) module. Then, a globally guided local attention (GLA) module is used to extract fine-grained local knowledge. Finally, the similarity between the input image and text is calculated based on knowledge-fused features to complete the matching process. Extensive experiments show that the proposed model can learn more effective knowledge features to improve the efficacy of image–text matching in.

Gather prediction is an indispensable part of smart city projects. The city government can respond in advance based on gather predictions and greatly reduce the loss and risks caused by vicious gatherings. Compared with other trajectory prediction tasks (i.e., the recommendation of point of interest), gather prediction pay more attention to real‐time trajectory data and requests stronger spatial‐temporal dependence. At the same time, gather prediction is more focused on scenes with multiple types of trajectories. And the existing methods majorly rely on the trajectory data and ignore the great influence of geographical environment (i.e., road network structure). Therefore, this paper transforms the gather prediction into the trajectory prediction task with strong real‐time condition in a certain city and conducts the gathering situations by predicting users’ aggregated movements in next minutes or hours. A novel Spatiotemporal Gate Recurrent Unit (STGRU) model is proposed, where spatiotemporal gates and road network gate are introduced to capture the spatiotemporal relationships between trajectories. Compared with existing methods, we improve the performance of the model by adding road network structure and external knowledges, as well as time and distance gates to reduce model parameters. The proposed STGRU is evaluated on three real‐world trajectory datasets, and the experimental results demonstrate the effectiveness of the proposed model.

Local differential privacy has been considered the standard measurement for privacy preservation in distributed data collection. Corresponding mechanisms have been designed for multiple types of tasks, like the frequency estimation for categorical values and the mean value estimation for numerical values. However, the histogram publication of numerical values, containing abundant and crucial clues for the whole dataset, has not been thoroughly considered under this measurement. To simply encode data into different intervals upon each query will soon exhaust the bandwidth and the privacy budgets, which is infeasible for real scenarios. Therefore, this paper proposes a highly efficient framework for differentially private histogram publication of numerical values in a distributed environment. The proposed algorithms can efficiently adopt the correlations among multiple queries and achieve an optimal resource consumption. We also conduct extensive experiments on real‐world data traces, and the results validate the improvement of proposed algorithms.