Interactive visual query systems are essential for the exploration and analysis of geospatial data. However, developing such systems has become increasingly challenging in recent years due to the conflict between the unprecedented volume of data and the need for instantaneous feedback. To address this challenge, we present HiVQ, a High-performance Visual Query system for real-time interactive visual query of geospatial big data. HiVQ adopts an innovative “Query as Visualization” paradigm, transforming user interactions into pixel value queries which can be processed efficiently with specialized indices and optimization strategies. Unlike conventional solutions that query and visualize geospatial objects sequentially, HiVQ effectively omits most geospatial objects and unnecessary computations that do not affect the final visualization, ensuring minimal sensitivity to data volume. Experimental results show that HiVQ accelerates visual queries by at least seven times compared to SOTA methods. This demonstration enables users to interactively explore and analyze spatial data with billions of nodes at any scale, receiving responses in milliseconds as they dynamically adjust analysis parameters, query conditions, or map styling. The demonstration video is available at https://gitee.com/kyrie-Bang/HiVQ-Demo.

Monitoring Earth's surface evolution is critical for understanding environmental processes and anthropogenic impacts, necessitating precise analysis of multi-temporal remote sensing data via integrated change detection and semantic interpretation. While existing methods achieve either pixel-level localization or semantic-level description, their conventional isolated frameworks exhibit two critical limitations: 1) inability to establish multimodal semantic consistency, and 2) feature confusion under complex environmental noise. To address these challenges, we propose Change-UP, a unified learning framework that synergizes Multilevel Change Interpretation (MCI) with Large Language Models (LLMs), enabling comprehensive analysis through dual visualization-inference perspectives. The architecture comprises three key components: 1) Adaptive Adjustment Change-Aware (A2CA) captures multi-scale inter-temporal differences through pyramidal feature fusion, significantly enhancing signal-to-noise ratio of differential features. 2) Query Semantic Consistency (QSC) establishes cross-modal alignment through learnable semantic queries, improving feature discrimination on challenging samples. 3) Style Transform Diversity (STD) integrates text-driven style transform with style synergy optimization to enhance inter-modal diversity while preventing feature space collapse. Extensive experiments demonstrate state-of-the-art performance, achieving 87.02% MIoU and CIDEr-D 143.40, surpassing previous methods by 0.59% and 3.11%, respectively. The framework's novel integration of visual-linguistic modalities opens new possibilities for intelligent Earth observation systems.

Service Mesh has become one of the most popular microservices governance frameworks. In the service governance function of traffic management, how to design an efficient strategy for request distribution to minimize response time has attracted wide research interest. Generally, a traffic policy can be pre-set manually. However, such a static policy always performs terribly when the load changes dynamically. That is, Service Mesh cannot adaptively select the optimal policy when setting service version diversion and selecting instances on the service subset. To address this issue, in this paper, we designed an adaptive strategy based on split-flow to achieve global load balancing. Furthermore, we implement such a strategy by developing a plug-in named DTMA for Istio, a typical representative of Service Mesh. By obtaining the Service Mesh node’s topology and global instance load distribution in real-time, Istio updates the optimal split-flow weight and selects the suitable load balancing strategy. Extensive experimental results show that our algorithm can reduce the response time by nearly half and keep a load of nodes and instances stable compared with Istio’s native configuration. The optimization effect of minimizing the response time is achieved through real-time load balancing.

This paper investigates the performance of network coding (NC) in unreliable wireless networks and the integration with TCP protocol. As the wireless nodes have limited processing capacity and energy, it will be difficult for them to deal with complex problems. Coding and decoding with traditional NC will cause a large overhead for wireless nodes. It is necessary to improve the NC scheme for applying to wireless networks. Furthermore, unreliable wireless channels will result in a lot of unnecessary retransmissions in wireless networks. In this paper, we propose a joint generation network coding scheme to improve the performance of NC in wireless networks. We first analyze the impact of the probability of decoding under lossy wireless channels in the traditional NC and joint generation NC. Then, we design a scheme that integrates the proposed network coding scheme with TCP. By adopting the joint generation NC, we could avoid unnecessary retransmissions in wireless networks due to the loss of acknowledgment in TCP protocol. Our simulation results demonstrate that joint generation NC could greatly reduce retransmissions.