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arXiv 2025年

作者： Xu, Jiaxing Chen, Yongqiang Dong, Xia Lan, Mengcheng Huang, Tiancheng Bian, Qingtian Cheng, James Ke, Yiping College of Computing and Data Science Nanyang Technological University Singapore Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong S-Lab Nanyang Technological University Singapore

In neuroscience, identifying distinct patterns linked to neurological disorders, such as Alzheimer’s and Autism, is critical for early diagnosis and effective intervention. Graph Neural Networks (GNNs) have shown promising in analyzing brain networks, but there are two major challenges in using GNNs: (1) distribution shifts in multi-site brain network data, leading to poor Out-of-Distribution (OOD) generalization, and (2) limited interpretability in identifying key brain regions critical to neurological disorders. Existing graph OOD methods, while effective in other domains, struggle with the unique characteristics of brain networks. To bridge these gaps, we introduce BrainOOD, a novel framework tailored for brain networks that enhances GNNs’ OOD generalization and interpretability. BrainOOD framework consists of a feature selector and a structure extractor, which incorporates various auxiliary losses including an improved Graph Information Bottleneck (GIB) objective to recover causal subgraphs. By aligning structure selection across brain networks and filtering noisy features, BrainOOD offers reliable interpretations of critical brain regions. Our approach outperforms 16 existing methods and improves generalization to OOD subjects by up to 8.5%. Case studies highlight the scientific validity of the patterns extracted, which aligns with the findings in known neuroscience literature. We also propose the first OOD brain network benchmark, which provides a foundation for future research in this field. Our code is available at https://***/AngusMonroe/BrainOOD. © 2025, CC BY-NC-SA.

关键词： Graph neural networks

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Enhancing Battery State of Charge Prediction in Autonomous Systems through Experimental Observer Implementation

Enhancing Battery State of Charge Prediction in Autonomous S...

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Electrical engineering and Automatic Control (ICEEAC), International Conference on

作者： Mohammed Amine Zafrane Ahmed Said Nouri Electronic department Signal System and Data Lab University of Science and Technologies of Oran- Algeria Elecrical engineering department Sfax. the National Engineering School of Sfax –ENIS- Tunisia

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

Nowadays, ensuring optimal battery performance is indispensable, especially in autonomous navigation scenarios. Our research aims at revolutionizing battery monitoring through a cost-effective, highly reliable system applying Kalman observer and ATMega08 microcontroller technologies. We initiate with methodical sensor identification experiments to evaluate precision and linearity, followed by behavior modeling. Implementing a Kalman filter-based observer on the microcontroller enables precise state of charge (SOC) monitoring by effectively correcting errors and filtering noise. Our system's adaptability is demonstrated through successful validation across various battery types, including lithium-ion and lead-acid, with a focus on parameters like voltage, current, power, and SOC. By introducing an interruptions during discharge, the proposed system accurately tracks voltage fluctuations, like a 1-volt change from 10.3 V to 9.3 V, providing reliable SOC indications, crucial for optimal energy management in autonomous navigation. Furthermore, we enhance data acquisition quality through a user interface labView, continuously integrating with our monitoring system for optimal energy management insights during navigation.

关键词： Voltage fluctuations Phase measurement Microcontrollers Noise Linearity Observers Batteries

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Revolutionizing H2M Interaction: Telepresence System Enabling Sign Language Expansion for Individuals with Disabilities 1

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26th International Conference on Distributed Computer and Communication Networks: Control, Computation, Communications, DCCN 2023

作者： Kuznetsov, Konstantin Muthanna, Ammar Ateya, Abdelhamied A. Koucheryavy, Andrey The Bonch-Bruevich Saint-Petersburg State University of Telecommunications St. Petersburg193232 Russia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Communications Engineering Zagazig University Sharqia Zagazig44519 Egypt

ISBN: (数字)9783031504822

ISBN: (纸本)9783031504815

The modern network technology ecosystem is expanding rapidly in multiple dimensions. Due to the demands of the Tactile Internet and fifth-generation (5G) networks, conventional approaches are no longer producing significant advancements. The imminent integration of immersive technologies holds the potential to revolutionize human existence, ushering in a new era of fully realized and enhanced human-to-machine interactions (H2M). This paradigm shift is poised to offer many benefits and adopt wireless technologies to enable real-time remote control. Concurrently, introducing the Tactile Internet necessitates a fundamental reevaluation of the foundational principles governing current and prospective communication networks. This article delves into an experimental endeavor that seeks to seamlessly integrate immersive technologies within 5G networks. A microservice application was developed for this purpose, simulating the nuanced movements of a person’s hand in space and facilitating the expression of intricate gestures. The article also delves into the strategies employed to minimize latency, enhancing the overall experience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 5G mobile communication systems

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EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence 23

EdgeMove: Pipelining Device-Edge Model Training for Mobile I...

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2023 World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computing Macquarie University 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

ISBN: (纸本)9781450394161

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

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Path Pooling: Train-Free Structure Enhancement for Efficient Knowledge Graph Retrieval-Augmented Generation

arXiv

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arXiv 2025年

作者： Wang, Hairu Feng, Yuan Xie, Xike Zhou, S. Kevin School of Computer Science University of Science and Technology of China China School of Biomedical Engineering USTC China Data Darkness Lab MIRACLE Center Suzhou Institute for Advanced Reasearch USTC China

Although Large Language Models achieve strong success in many tasks, they still suffer from hallucinations and knowledge deficiencies in real-world applications. Many knowledge graph-based retrieval-augmented generation (KG-RAG) methods enhance the quality and credibility of LLMs by leveraging structure and semantic information in KGs as external knowledge bases. However, these methods struggle to effectively incorporate structure information, either incurring high computational costs or underutilizing available knowledge. Inspired by smoothing operations in graph representation learning, we propose path pooling, a simple, train-free strategy that introduces structure information through a novel path-centric pooling operation. It seamlessly integrates into existing KG-RAG methods in a plug- and-play manner, enabling richer structure information utilization. Extensive experiments demonstrate that incorporating the path pooling into the state-of-the-art KG-RAG method consistently improves performance across various settings while introducing negligible additional cost. Code is coming soon at https://***/hrwang00/path-pooling. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Spatial-Temporal Analysis of Collective Emotional Resonance in China During Global Health Crisis 25

Spatial-Temporal Analysis of Collective Emotional Resonance ...

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34th ACM Web Conference, WWW 2025

作者： Zhang, Limiao Qi, Xinyang Ma, Haiping Gao, Jie Zhang, Xingyi Hu, Yanqing Jin, Yaochu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Institutes of Physical Science and Information Technology Anhui University Hefei China School of Innovation and Entrepreneurship Shandong University Qingdao China School of Computer Science and Technology Anhui University Hefei China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen China Trustworthy and General AI Lab School of Engineering Westlake University Hangzhou China

ISBN: (纸本)9798400712746

The 21st century has already witnessed so many outbreaks with pandemic potential, including SARS (2002), H1N1 (2009), MERS (2012), Ebola (2014), Zika virus (2015), and the COVID-19 pandemic (2019). Using 60 million geotagged Sina Weibo tweets covering over 20 million active accounts, we investigate the collective emotional dynamics on social media in the most recent global pandemic, i.e., COVID-19. This research features two highlights: (1) It focuses on the Chinese population located in the initial epicenter of the pandemic. (2) It examines the initial year after the pandemic outbreak, a critical period where emotions were most intense due to the uncertainty and rapid developments related to the crisis. Using cross-disciplinary methods, we reveal a positive connection between online emotional resonance and geographic proximity, demonstrating a direct mapping between virtual network distances and physical spatial embedding. We propose a percolation-based index to measure the nationwide emotional resonance level with which we illustrate the significant economic impact of the global health issue. Finally, we identify a leader-follower pattern in emotional resonance fluctuations based on time-lag emotion correlations, revealing that less active regions play a crucial role in leading and responding to emotional changes. In the face of long COVID and emerging global health crises, our analysis elucidates how collective emotional resonance evolves, providing potential directions for online opinion interventions during global shocks. © 2025 Copyright held by the owner/author(s).

关键词： Economic and social effects

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada 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

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.

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A Novel Image Encryption and Decoding Approach Based on DNA Coding and Arnold's Transformation for IoT Applications

A Novel Image Encryption and Decoding Approach Based on DNA ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Shaltout, Nawal Abd El-Latif, Ahmed A. Al-Adrousy, Waleed Hammad, Mohamed Ateya, Abdelhamied A. Elmougy, Samir Mansoura University Faculty of Computers and Information Department of Computer Science Mansoura35516 Egypt Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science 32511 Egypt Menoufia University Faculty of Computers and Information Department of Information Technology Shibin El Kom32511 Egypt Zagazig University Department of Electronics and Communications Engineering Zagazig Egypt

ISBN: (纸本)9798350351408

Nowadays, the Internet of Things (IoT) is significantly utilized by an extensive range of applications that lead to the enhancement of different aspects of defense and military capabilities. In IoT, a wide range of smart devices that can communicate with each other are connected through it. Therefore, it is essential to protect multimedia data, especially images with an encryption approach that offers a sufficient degree of security in military applications. Hence, this paper introduces an effective scheme for image encryption and decoding relying on Arnold's transformation DNA coding. It proposes a new pseudo-random number generation PRNG using the Sine-Logistic map and Duffing map called SLDM. The encryption method involves three phases: 1- Key generation and PRNG. 2- confusion phase using the Arnold transformation method. 3- diffusion using DNA rules and operations. The results based on the USC-SIPI dataset, show that the suggested scheme is sufficiently resistant to every kind of attack. Furthermore, the suggested method yields NPCR scores of around 99.62% and UACI scores of approximately 33.48%. Due to its low complexity, it has a great deal of potential for safe and real-time picture applications. © 2024 IEEE.

关键词： Random number generation

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On Pipelined GCN with Communication-Efficient Sampling and Inclusion-Aware Caching

On Pipelined GCN with Communication-Efficient Sampling and I...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Shulin Wang Qiang Yu Xiong Wang Yuqing Li Hai Jin 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 Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Graph convolutional network (GCN) has achieved enormous success in learning structural information from unstructured data. As graphs become increasingly large, distributed training for GCNs is severely prolonged by frequent cross-worker communications. Existing efforts to improve the training efficiency often come at the expense of GCN performance, while the communication overhead persists. In this paper, we propose PSC-GCN, a holistic pipelined framework for distributed GCN training with communication-efficient sampling and inclusion-aware caching, to address the communication bottleneck while ensuring satisfactory model performance. Specifically, we devise an asynchronous pre-fetching scheme to retrieve stale statistics (features, embedding, gradient) of boundary nodes in advance, such that the embedding aggregation and model update are pipelined with statistics transmission. To alleviate communication volume and staleness effect, we introduce a variance-reduction based sampling policy, which prioritizes inner nodes over boundary ones for reducing the access frequency to remote neighbors, thus mitigating cross-worker statistics exchange. Complementing graph sampling, a feature caching module is co-designed to buffer hot nodes with high inclusion probability, ensuring that frequently sampled nodes will be available in local memory. Extensive evaluations on real-world datasets show the superiority of PSC-GCN over state-of-the-art methods, where we can reduce training time by 72%-80% without sacrificing model accuracy.

关键词： Training Estimation error Accuracy Graph convolutional networks Computational modeling Probability Benchmark testing

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