Vehicle-to-everything (V2X) communication plays an essential role in the Internet-of-Vehicles (IoV) systems. However, in practice the diverse decision objectives of different users and the highly dynamic nature of IoV cause great challenges in realizing efficient data transmission. This paper investigates a sequential power control problem in a typical V2X communication network formed by multiple data delivery links, and proposes a novel decision framework from the perspective of dynamic multi-objective optimization (DMO). The framework is developed based on the dynamic multi-objective optimization evolutionary algorithm (DMOEA). When the variation of channel fading state is detected, an innovative environment change response mechanism is implemented to predict the centroid change of the Pareto optimal set (PS) in the new environment. This allows to properly generate the initial population for the subsequent evolutionary search process, and rapidly track the time-varying Pareto optimal front (PF). The effectiveness and advantages of the proposed framework over conventional solutions are validated by simulation results.

Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCE) cooperating with Priority Flow Control (PFC) has been widely deployed in production datacenters to enable low latency, lossless transmission. At the same time, modern datacenters typically offer parallel transmission paths between any pair of end-hosts, underscoring the importance of load balancing. However, the well-studied load balancing mechanisms designed for lossy datacenter networks (DCNs) are ill-suited for such lossless environments. Through extensive experiments, we are among the first to comprehensively inspect the interactions between PFC and load balancing, and uncover that existing fine-grained rerouting schemes can be counterproductive to spread the congested flows among more paths, further aggravating PFC’s head-of-line (HoL) blocking. Motivated by this, we present FLB, a Fine-grained Load Balancing scheme for lossless DCNs. At its core, FLB employs threshold-free rerouting to effectively balance traffic load and improve link utilization during normal conditions and leverages timely congested flow isolation to eliminate HoL blocking on non-congested flows when congestion occurs. To handle complex multi-bottleneck scenarios, we further introduce FLB*, which incorporates an enhanced congestion-point-aware isolation mechanism using Congestion Point Identifiers (CPI) to eliminate HoL blocking among different congested flows.We have fully implemented a FLB prototype, and our evaluation results show that FLB reduces PFC PAUSE rate by up to 96% and avoids HoL blocking, translating to up to 45% improvement in goodput over CONGA+DCQCN and 40%, 36%, 29% and 18% reduction in average flow completion time (FCT) over LetFlow+Swift, MP-RDMA, Proteus+DCQCN and LetFlow+PCN, respectively.

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.

Resource allocation in vehicle-to-everything (V2X) communication is an important yet challenging problem. Most existing works target optimizing a specific performance metric in a relatively stable condition, which may not be able to adaptively satisfy the dynamic quality of service (QoS) requirements prevalent in practical vehicular networks. In this paper, we investigate a typical V2X resource allocation problem requiring optimization of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication performance. Instead of setting one single system goal, we formulate it as a multi-objective optimization problem and aim to seek the complete trade-off between the objectives, i.e., the Pareto frontier (PF). To this end, the problem is first transformed to a multi-objective Markov decision process (MOMDP) and solved by a multi-objective reinforcement learning resource allocation (MORLRA) algorithm. Simulation results demonstrate the effectiveness and advantages of the proposed method.

Large Language Models (LLMs) demonstrate various capabilities previously considered unique to humans. However, current evidence is insufficient to determine whether LLMs have developed spatial cognition, a fundamental aspect of human cognition underpinning logical-mathematical reasoning and various other skills. Previous studies on this topic have primarily concentrated on small-scale perceptions, leaving the spatial cognition within the context of GIScience largely unexamined. We introduce a benchmark that evaluates spatial cognition abilities across seven categories to systematically assess how well LLMs process and generate three types of spatial knowledge: landmark, route, and survey knowledge. Furthermore, we propose a tool-augmented approach named Hybrid Mind, which integrates LLMs with deterministic GIS algorithms to enhance their performance in spatial cognitive tasks. The core idea involves the implementation of a mental map builder that generates a quantitative map based on segmented qualitative constraints, overcoming LLMs’ fallacies in synthesizing spatial information. Our experimental results revealed that although LLMs exhibited potential for spatial cognition, their performance was poor across most spatial cognitive tasks, particularly in constructing route and survey knowledge. The leading model, GPT-4-turbo, correctly answered fewer than one-fourth of the questions. In contrast, the Hybrid Mind approach significantly improved performance, correctly solving 70.48% of the questions.

Cellular Signal Strength (CSS), defined as the signal power received by mobile phones, is an important aspect of geographic information flow analysis, because the density of such information can reflect the urbanization variables such as population, gross domestic product, built-up area, electric power consumption, etc. Despite the importance, the real-time analysis of global CSS distribution remains a challenging problem due to the large data scale. In this article, a Display-driven Computing (DisDC) technique is designed and applied to provide efficient large scale interactive CSS visualization, generating results by calculating the value of each pixel that directly for display. Specifically, we present an efficient CSS measurement algorithm, which introduces spatial indexes and a corresponding query strategy; besides, an optimized parallel computing architecture is proposed to ensure the ability of real-time visualization. Experiments show that our approach obviously outperforms traditional methods and is capable of handling more than 40 million base stations in real-time. Moreover, an online demonstration is provided athttps://github.com/MemoryMmy/CSSMap.

We present DiSA, a Display-driven Spatial Analysis framework for interactive analysis of large-scale geographical vector data. DiSA calculates visualization of analysis results directly using a parallel per-pixel approach with efficient fine-grained spatial indexes. Compared with conventional object-based methods, DiSA can greatly reduce the computational complexity (from O(n) to O(log(n)) in some cases), making it less sensitive to data volumes. Experimental results verify that DiSA can provide analysis of billion-scale spatial objects in milliseconds. We demonstrate DiSA with various application scenarios including raw data exploration, spatial buffer and overlay analysis, and global cellular signal strength analysis. Users can explore 10 millions of spatial objects, adjust algorithm parameters, and always see the results in real-time on a personal computer.

The widespread deployment of unsafe programming languages such as C and C++, leaves many programs vulnerable to memory corruption attacks. With the continuous improvement of control-flow hijacking defense methods, recent works on data-oriented attacks including Data-oriented Exploits (DOE), Data-oriented Programming (DOP), and Block-oriented Programming (BOP) have been showed that these attacks can cause significant threat even in the presence of control-flow defense mechanism. Moreover, DFI (Date Flow Integrity) is a software-only approach for mitigating data-oriented attacks, while it incurs a 104% performance overhead. There are no suitable defense methods for such attacks as yet. In this paper, we propose Dam, a practical scheme to mitigate data-oriented attacks with tagged memory based on hardware. Dam is a novel approach using the idea of tagged memory to break data-flow stitching and gadgets dispatcher of generating data-oriented attacks rather than complete DFI. By enforcing security checking on memory access, Dam eliminates two requirements in constructing a valid data-oriented attack. We have implemented Dam by extending lowRISC, a RISC-V based SoC (System of a Chip) that implements tagged memory. And our evaluation results show that our scheme has an average performance cost of 6.48%, while Dam provides source compatibility and strong security.