Stereo matching recovers 3D scene information based on the correlation between corresponding pixels. Despite impressive progress, existing methods lack sufficient correlation priors in ill-posed regions such as occlusions, detailed and reflective regions. In this paper, we propose Geometry Aware Stereo Matching Network (GEAStereo) to enhance geometric structure perception and address this issue. We adaptively incorporate the Monocular Disparity Distribution Prior into the stereo cost volume, building Mono-Stereo Fusion Volume (MSFV), which effectively captures global geometric structures and rectifies the correlation information in ill-posed regions. Furthermore, we introduce rich detail information from gradient features and construct a Detail-Aware Volume (DAV) by aggregating the group-wise cost volume under the guidance of gradient spatial attention, thus enhancing the correlation modeling in detailed structures. Jointly, MSFV and DAV provide rich correlation priors for disparity iterative optimization. Experimental results show that our method achieves competitive results on the ETH3D and KITTI2015 benchmarks. Compared with the state-of-the-art methods, our method demonstrates stronger performance in zero-shot generalization.

In web-based systems, elements are commonly organized within a graph structure, with each node collecting essential spatio-temporal data. Examples include websites on the World Wide Web, traffic monitors in transportation networks, or sensors in the Internet of Things (IoT). However, sensors are typically deployed sparsely and unevenly, leaving the remaining nodes unobserved. The spatio-temporal kriging task, which infers values at unobserved nodes from observed ones, has thus attracted significant research interest. Due to limitations such as reliance on static graph structures and iterative Graph Convolution Network (GCN) frameworks, accurate kriging remains challenging. To address these issues, we propose a Diffusion-based Kriging Model with Graph-enhanced Attention (DKM-GA). Our approach first introduces a graph-enhanced attention mechanism that dynamically learns more accurate graph structures by combining predefined graph knowledge with global node value similarities. It is then integrated into a diffusion-based framework, which is tailored for the reliance of attention on known values. Therefore, the framework progressively refines the target values using correlated nodes, and the graph-enhanced attention selects more relevant neighbors based on the refined values. Furthermore, a node-based rescaling strategy is introduced to align the inference phase graphs to the training ones. Experiments on eight real-world datasets demonstrate that DKM-GA achieves superior performance, reducing estimation errors by up to 12.66%. Moreover, our analysis identifies three practical scenarios where the model delivers greater performance gains, even achieving 19.51% improvements on datasets that show minor gains under standard settings. These results highlight the effectiveness and potential of our model, while the scenarios provide settings for more comprehensive evaluations in terms of performance and robustness.

Spatio-temporal time series (STTS) play a crucial role in domains such as traffic forecasting, energy scheduling, and financial analysis. However, accurate and efficient prediction remains challenging due to the complex dynamic dependencies across temporal and spatial dimensions. Existing Graph Neural Networks (GNNs) often struggle to balance effectiveness and efficiency when modeling dynamic spatio-temporal relations. Meanwhile, Large Language Models (LLMs) exhibit strong capabilities in modeling long-range dependencies and generalization under few-shot and zero-shot conditions, yet their ability to capture spatio-temporal structures remains limited. To address this, we propose LAD-SGNN, a unified framework that integrates the advantages of graph-based and language-based modeling. Specifically, LAD-SGNN employs Spectral Graph Convolution on the Stiefel manifold (SGSC) together with Linear Dynamic Graph Optimization (LDGOSM) to efficiently extract dynamic spatio-temporal features with nearly linear complexity. Structured prompts and a spatio-temporal alignment mechanism are then designed to fuse the extracted dynamic spectral information with task semantics, which are fed into a lightweight LLM to achieve unified modeling of spatio-temporal structures and semantic reasoning. In this framework, SGSC efficiently represents dynamic spatial dependencies in the spectral domain, while the prompt-based alignment strategy explicitly injects spatio-temporal information into the LLM, thereby enhancing generalization across datasets. Extensive experiments on multiple real-world spatio-temporal datasets demonstrate that LAD-SGNN significantly improves prediction accuracy in both zero-shot and few-shot scenarios.

As blockchain technology grows more prevalent, its performance limitations have become a critical barrier to large-scale adoption. The rise of optimized heterogeneous blockchain systems has significantly increased the demand for fair performance testing frameworks. However, existing work, whether simulator-based or system-based, often relies on default settings, overlooking the impact of detailed configuration parameters, which can affect the fairness of performance evaluations. To fill this gap, we propose BlockAlign, a configuration alignment tool based on a rule tree, designed to enhance the fairness of performance testing across heterogeneous blockchain systems. Firstly, based on architectural analysis, we design a rule tree to filter, classify, and semantically align configurations. Secondly, we introduce a metric called fluctuation rate to measure performance differences before and after configuration alignment. Finally, we conduct experiments on Geth, Besu, and Conflux, demonstrating that BlockAlign significantly improves the fairness and credibility of performance comparisons.

Recently, k-vertex connected component (k-VCC) detection has gained significant attention in graph analysis owing to its ability to capture structural cohesion. A k-VCC remains connected even after the removal of any k-1 vertices from itself. The k-VCC has broad applications across multiple domains, such as social network analysis, cybersecurity, and bioinformatics. Yet, the existing exact k-VCC detection algorithms require repeated computation of minimum vertex cuts, imposing a prohibitive computational cost for large scale graphs. In this paper, we present an approximate algorithm for k-VCC detection that leverages Monte Carlo sampling to accelerate minimum vertex cut computation with theoretical guarantee. Further, we design a distributed algorithm for mining all k-VCCs, named DkVCC. DkVCC adopts a divide-and-conquer strategy, decomposing the problem into smaller subgraph mining tasks that can be executed concurrently. Specifically, we generate tasks from individual vertices to construct initial subgraphs, and then iteratively expand and merge the subgraphs to form the final k-VCCs. Extensive experiments on 5 large real datasets demonstrates the efficiency of our proposed algorithms. For example, we achieve 4× runtime speedup on the LiveJournal dataset with 3.99M vertices and 34.7M edges in a 3-node cluster.

Attributed Heterogeneous Information Networks (AHINs) amalgamate the advantages of attributed graphs (AGs) and heterogeneous information networks (HINs) to model intri-cate systems. Within this context, community search-aiming to identify the most probable community containing the queried ver-tex-has been extensively explored in AGs and HINs. However, existing methodologies fall short in simultaneously accommodating heterogeneous attributes and multiple meta-paths in AHINs, posing a substantial challenge in investigating community search within expansive AHINs. Recent studies highlight the efficacy of machine learning-based community search, offering enhanced flexibility and higher-quality communities in comparison to traditional structural-based methods. Yet, semi-supervised learning methods demand substantial labeled data and incur considerable memory and time costs when applied to large AHINs. To tackle these challenges, we propose a MK (Most-likely; K-sized) community search approach. This approach involves defining an MK community and leveraging Graph Neural Networks (GNNs) to amalgamate structures and attributes into a unified goodness metric. Our methodology involves training on local subgraphs sampled via guided random walks based on multiple meta-paths, circumventing the need for training on the entire graph. Moreover, attention-based GNNs adeptly learn meta-path weights to guide weighted walks in subsequent iterations. Additionally, self-training is employed to alleviate the labeling burden. We also demonstrate that pinpointing the location for the MK community is NP-hard and present a heuristic local search strategy that expedites the resolution process through rewriting. Ultimately, the convergence of iterations yields the solution. Extensive experiments conducted on four real-world datasets underscore that the MK framework significantly enhances both effectiveness and efficiency in community search within AHINs. Our code is publicly available at https://github.com/uucxuu/CSAH.

With the rapid development of artificial intelligence (AI), the Internet of Things (IoT), and Web-related technologies, the demand for data collection, storage, analysis, and utilization has increased significantly, leading to the rise of data economy, which emphasizes to transform data into data services or data products, allowing the user to explore data value and trade data product through interaction and collaboration. To our best knowledge, current research on data ecosystems is still in the initial stage. Problems such as how to effectively manage and share cross-domain data, how to balance the tradeoff between data usage and data security, and how to effectively convert data into data products to unlock their value are yet to be resolved. This paper proposed a logically integrated data ecosystem based on the digital object architecture (DOA), named the Internet of Data (IoD), taking advantage of DOA’s ability in managing heterogeneous data and enhancing data findability. Upon that, we proposed a demand-driven data-sharing mechanism that allows data providers to tailor data properties to meet consumer’s specific needs or preferences through online bilateral negotiation. This paper also proposed a data ontology to package the heterogeneous information in a unified form during the data sharing process, which further facilitates data publishing, data discovery, data matchmaking, data customization, and authorized access.

Few-shot Named Entity Recognition (NER) is the task of identifying and classifying entities under conditions of low resources. The preceding approach relies on meta-learning, wherein the classification model is trained through N-way K-shot. However, It neglects essential information: the model’s input is organized at the sentence level. To tackle the aforementioned issues, we introduce a sampling strategy — the Loose Nway K˜shot sampling algorithm. This approach combines the number of entities and sentence level which is the smallest unit of NER models’ input. Experimental results demonstrate that our strategy currently stands as the most effective method for Few-shot NER. Furthermore, our investigation reveals that various classes of entities within the same sentence can impede the prototype representation of each class. Consequently, we introduce a training method that involves utilizing sentences with single entity class for pre-training purposes. The experimental outcomes substantiate that this training methodology maximizes the utilization of labeled data, enabling the pre-training model to swiftly adapt to new domains. This approach significantly enhances the performance of NER.

Different from the traditional military network model, this paper proposes a new Operation System of Systems (OSoS) model—FINC-E Model (Force, Intelligence, Networking, and C2, Extended Model) which provides the formulization framework for nodes and edges of the OSoS. Secondly, a new index—edge key potential is proposed, which acts as the measurement of the edge's importance, meanwhile the performance metrics for the OSoS are provided, i.e. coordination coefficient, execution capacity coefficient and information support coefficient. In the end, the attack strategy based on edge key potential is put forward, and a practical case, which is aiming at the edge attack rather than node attack, is conducted to demonstrate the effectiveness of our model.