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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Xuan Guo Chenchen Gao Weifan Niu Xinzong Wei Chengqi Hua Junnan Liu Mingliang Xu School of Computer and Arifcial intelligence Zhengzhou University Zhengzhou China Engineering Research Center of intelligent Swarm Systems Ministry of Education Zhengzhou China National Supercomputing Center in Zhengzhou Zhengzhou China School of Earth Science and Technology Zhengzhou University Zhengzhou China

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Geospatial tasks often require the coordination of various spatial algorithms and operations, which are usually performed through tool calling guided by natural language prompts. Crafting effective prompts is challenging due to the inherent complexity and ambiguity of natural language. In this paper, we present GeoPet, a visual analytics system designed to simplify the process of prompt engineering to improve the performance of geospatial tool callings on large language models (LLMs). At its core, GeoPet is a sophisticated tool recommendation method that accepts geospatial tasks as input, decomposes them into atomic tasks, identifies relevant tools, and extracts the most relevant tool descriptions for these atomic tasks. The system is designed to support interactive prompt engineering of geospatial tool invocations, enabling users to explore the connections between geospatial tasks and established tools and to evaluate their performance. This enables crafting and refining prompts that coordinate LLMs with human expertise. GeoPet's satisfaction, practicality, usability, and visual design are validated through two case studies and a user study. These demonstrate that the system significantly eases the burden of rapid engineering and skillfully guides LLMs in geospatial tool calling capabilities. By providing a visual and interactive system for prompt engineering, GeoPet helps users navigate complex geospatial tasks and improves the overall efficiency and accuracy of tool calling for LLMs.

关键词： Navigation Large language models Visual analytics Interactive systems Natural languages Refining Geospatial analysis Complexity theory Prompt engineering Usability

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Efficient FPGA-based graph processing with hybrid pull-push computational model

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Frontiers of computer Science 2020年第4期14卷 13-28页

作者： Chengbo YANG Long ZHENG Chuangyi GUI Hai JIN National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

Hybrid pull-push computational model can provide compelling results over either of single one for processing real-world *** and pipeline parallelism of FPGAs make it potential to process different stages of graph ***,considering the limited on-chip resources and streamline pipeline computation,the efficiency of hybrid model on FPGAs often suffers due to well-known random access feature of graph *** this paper,we present a hybrid graph processing system on FPGAs,which can achieve the best of both *** approach on FPGAs is unique and novel as ***,we propose to use edge block(consisting of edges with the same destination vertex set),which allows to sequentially access edges at block granularity for locality while still preserving the *** to the independence of blocks in the sense that all edges in an inactive block are associated with inactive vertices,this also enables to skip invalid blocks for reducing redundant ***,we consider a large number of vertices and their associated edge-blocks to maintain a predictable execution *** also present to switch models in advance with few stalls using their state *** evaluation on a wide variety of graph algorithms for many real-world graphs shows that our approach achieves up to 3.69x speedup over state-of-the-art FPGA-based graph processing systems.

关键词： graph processing efficiency computational model FPGAs

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Almond defect and freshness inspection system using hyperspectral imaging and deep learning techniques

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Postharvest Biology and technology 2024年 211卷

作者： Chen, Shih-Yu Wang, Mei-Yun Kuo, Yung-Ming Chan, Yu-Chia Chen, Yi-Cheng Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Electronic Engineering National Formosa University Yunlin 632301 Taiwan

Almonds, recognized as a nutritious and low-calorie snack rich in unsaturated fats and essential nutrients, are primarily imported from California. Despite undergoing grading control by the United States Department of Agriculture (USDA), concerns persist regarding the presence of defective kernels. Manual hand-picking is the conventional method for almond processing;however, prolonged picking can reduce accuracy due to visual fatigue. Consumers prioritize freshness due to fungi's potential for aflatoxin production, emphasizing the need for vigilant almond consumption. In response to these challenges, this study presents an innovative approach that employs a lightweight VGG16 and an enhanced MobileNet model for identifying defects based on RGB images of almonds. This method effectively classifies broken, scratched, and malformed kernels. Additionally, hyperspectral imaging (HSI) data in the 400–1000 nm range were collected to distinguish insect-damaged from undamaged kernels and assess almond freshness. For comparative analysis, various band selection algorithms were applied to extract distinct spectral bands. For insect damage identification, we propose a merged CNN model created by simultaneously combining 1D- and 2D-CNNs, allowing for the concurrent extraction of spectral and spatial characteristics. This approach achieved a remarkable ACC of 0.9905 and a Kappa value of 0.9521. Meanwhile, in evaluating freshness, PCA was employed to select the first 30 principal components as vital bands. Our proposed lightweight 1D-CNN delivered an ACC of 0.9768 and a Kappa value of 0.9527 in freshness assessment. © 2024 Elsevier B.V.

关键词： Band selection Convolutional neural network Defect Detection hyperspectral imaginig

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Large-Scale Satellite Optical Network Simulation Architecture and Performance Evaluation

Large-Scale Satellite Optical Network Simulation Architectur...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Xinjun Wang Zhenrong Zhang Kangqi Zhu Nan Hua Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China

We evaluate the accuracy and performance of different simulation modes in MegaStarSim, analyze end-to-end routing strategies in large-scale satellite optical networks, and assess link quality across various inter-sate... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Performance evaluation Satellites Accuracy Simulation computer architecture Optical fiber networks Routing Software Photonics Graphical user interfaces

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Measuring the social influence for retweet behavior prediction in microblogging network

Measuring the social influence for retweet behavior predicti...

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International Symposium on Telecommunications, IST

作者： Rahebeh Mojtahedi Safari Amir Masoud Rahmani Sasan H. Alizaeh Department of Computer Engineering Lahijan Branch Islamic Azad University Lahijan Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section3 Douliou Yunlin Taiwan Faculty of Information Technology ICT Research Institute (ITRC) Tehran Iran

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

Nowadays, retweeting behavior is considered a substantial behavior to analyze information diffusion in the microblogging network. To understand the underlying mechanism of information diffusion and analyze the evolution of the diffusion corresponding to a given original microblog, user retweeting behavior prediction has recently gained considerable attention. In this paper firstly, we introduce the most influential factors which affect user retweeting behavior and propose an effective measurement based on the random walk model to measure the social influence. Secondly, by incorporating our defined influential factors into a logistic regression classifier model, we predict the future activated user set concerning the diffusion of a given original microblog at the next time point. Unlike other comparative models, our prediction model can illustrate the evolution of diffusion and predict the retweeting behavior of all potential users through the diffusion of any original microblog in the microblogging network. Finally, based on the real Twitter microblogging dataset, the experimental results show by the contribution of our defined influential factors and presenting an effective measure of the social influence factor, we can obtain more accurate predictions in comparison to other baseline methods.

关键词： Logistic regression Accuracy Blogs Predictive models Telecommunications Information diffusion

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Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

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Neuroscience Bulletin 2023年第6期39卷 893-910页

作者： Feng Su Yangzhen Wang Mengping Wei Chong Wang Shaoli Wang Lei Yang Jianmin Li Peijiang Yuan Dong-Gen Luo Chen Zhang Department of Neurobiology School of Basic Medical SciencesBeijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing100069China Chinese Institute for Brain Research Beijing102206China State Key Laboratory of Translational Medicine and Innovative Drug Development Nanjing210000China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing100871China School of Life Sciences Tsinghua UniversityBeijing100084China School of Biological Science and Medical Engineering Beihang UniversityBeijing100191China The Key Laboratory of Developmental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing210096JiangsuChina Institute for Artificial Intelligence the State Key Laboratory of Intelligence Technology and SystemsBeijing National Research Center for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua UniversityBeijing100084China School of Mechanical Engineering and Automation Beihang UniversityBeijing100191China

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social *** tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being *** overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been ***,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction *** this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion *** system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing *** proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

关键词： Noninvasive tracking Deep learning Multi-camera Mouse group Social interaction

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FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices

arXiv

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arXiv 2025年

作者： Yao, Dezhong Shi, Yuexin Liu, Tongtong Xu, Zhiqiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The iterative training process in conventional FL introduces significant computation and communication overhead, which is unfriendly for resource-constrained edge devices. One-shot FL has emerged as a promising approach to mitigate communication overhead, and model-heterogeneous FL solves the problem of diverse computing resources across clients. However, existing methods face challenges in effectively managing model-heterogeneous one-shot FL, often leading to unsatisfactory global model performance or reliance on auxiliary datasets. To address these challenges, we propose a novel FL framework named FedMHO, which leverages deep classification models on resource-sufficient clients and lightweight generative models on resource-constrained devices. On the server side, FedMHO involves a two-stage process that includes data generation and knowledge fusion. Furthermore, we introduce FedMHO-MD and FedMHO-SD to mitigate the knowledge-forgetting problem during the knowledge fusion stage, and an unsupervised data optimization solution to improve the quality of synthetic samples. Comprehensive experiments demonstrate the effectiveness of our methods, as they outperform state-of-the-art baselines in various experimental setups. Our code is available at https://***/YXShi2000/FedMHO. © 2025, CC BY.

关键词： Federated learning

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Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation

arXiv

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arXiv 2024年

作者： Chu, Zhaoyang Wan, Yao Li, Qian Wu, Yang Zhang, Hongyu Sui, Yulei Xu, Guandong Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia School of Big Data and Software Engineering Chongqing University China School of Computer Science and Engineering University of New South Wales Australia School of Computer Science University of Technology Sydney Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding approach for vulnerability detection, owing to their ability to capture the underlying semantic structure of source code. However, GNNs face significant challenges in explainability due to their inherently black-box nature. To this end, several factual reasoning-based explainers have been proposed. These explainers provide explanations for the predictions made by GNNs by analyzing the key features that contribute to the outcomes. We argue that these factual reasoning-based explanations cannot answer critical what-if questions: "What would happen to the GNN's decision if we were to alter the code graph into alternative structures?" Inspired by advancements of counterfactual reasoning in artificial intelligence, we propose CFExplainer, a novel counterfactual explainer for GNN-based vulnerability detection. Unlike factual reasoning-based explainers, CFExplainer seeks the minimal perturbation to the input code graph that leads to a change in the prediction, thereby addressing the what-if questions for vulnerability detection. We term this perturbation a counterfactual explanation, which can pinpoint the root causes of the detected vulnerability and furnish valuable insights for developers to undertake appropriate actions for fixing the vulnerability. Extensive experiments on four GNN-based vulnerability detection models demonstrate the effectiveness of CFExplainer over existing state-of-the-art factual reasoning-based explainers. © 2024, CC BY.

关键词： Graph neural networks

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Inductively coupled Josephson junctions: a platform for rich neuromorphic dynamics

arXiv

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arXiv 2025年

作者： Baxevanis, G. Hizanidis, J. School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Institute of Electronic Structure and Laser Foundation for Research and Technology-Hellas Heraklion70013 Greece Institute of Nanoscience and Nanotechnology National Center for Scientific Research "Demokritos" Athens15341 Greece

Josephson junctions (JJs) are by nature neuromorphic hardware devices capable of mimicking excitability and spiking dynamics. When coupled together or combined with other superconducting elements, they can emulate additional behaviors found in biological neurons. From a technological point of view, JJ-based neuromorphic systems are particularly appealing since they present THz-speed processing and they operate with near-zero power dissipation. In this work we study a system of inductively coupled JJs and focus on the nonlinear dynamical aspects of its neurocomputational properties. In particular, we report on spiking behavior related to a saddle-node off invariant cycle bifurcation and excitability type II, synchronization, first spike latency effects, and multistability. Special emphasis is placed on the bursting dynamics the system is capable of reproducing, and a new underlying mechanism is proposed beyond the approach followed in prior works. Copyright © 2025, The Authors. All rights reserved.

关键词： Tunnel junctions

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A Priority-Aware AI-Generated Content Resource Allocation Method for Multi-UAV Aided Metaverse

A Priority-Aware AI-Generated Content Resource Allocation Me...

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IEEE Conference on Wireless Communications and Networking

作者： Zheng Zhang Jingjing Wang Jianrui Chen Zhengru Fang Chunxiao Jiang Zhu Han School of Cyber Science and Technology Beihang University Beijing China Department of Computer Science City University of Hong Kong Hong Kong China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the advancement of large model technologies, AI -generated content is gradually emerging as a mainstream method for content creation. The metaverse, as a key application scenario for the next-generation communication technologies, heavily depends on advanced content generation technologies. Nevertheless, the diverse types of metaverse applications and their stringent real-time requirements constrain the full potential of AIGC technologies within this environment. In order to tackle with this problem, we construct a priority-aware multi-UAV aided metaverse system and formulate it as a Markov decision process (MDP). We propose a diffusion-based reinforcement learning algorithm to solve the resource allocation problem and demonstrate its superiority through enough comparison and ablation experiments.

关键词： Metaverse Simulation Markov decision processes Reinforcement learning Autonomous aerial vehicles Diffusion models Real-time systems Communications technology Resource management Next generation networking

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