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Transforming 6G Mobile Edge Intelligence with Large Models

IEEE Network 2025年

作者： He, Xiaoming Jiang, Yunzhe Liu, Yinqiu Cui, Huajun Pan, Heng Mao, Yingchi Nanjing University of Posts and Telecommunications College of Internet of Things Nanjing China University of Electronic Science and Technology of China College of Communication and Information Engineering Chengdu China Nanyang Technological University College of Computing and Data Science Singapore PowerChina Beijing Engineering Corporation Limited Digital Intelligence Research Institute Beijing China Zhongyuan University of Technology Zhengzhou China Hohai University College of Computer Science and Software Engineering Nanjing China

Large models (LMs) exhibit transformative power in 6G Mobile-Edge Intelligence (MEI) scenarios, which can effectively address key challenges regarding network complexity, resource optimization, intelligent decision-making, and user experience. In this article, we first introduce the basics of LM and explain the potential applications and existing solutions for integrating LMs into 6G MEI. Afterward, we conduct a case study oriented to LM-assisted 6G MEI scenarios, focusing on how to use LMs to predict the size of traffic. Specifically, we employ two attention modules named Dartboard Spatial Multi-head Self-Attention (DS-MSA) and Causal Temporal Multi-head Self-Attention (CT-MSA) to extract spatio-temporal features. A Large Language Model (LLM) named GPT-2 is utilized as the backbone model. To fit time-series data, the original token encoding layer is replaced by the convolutional encoding layer. Additionally, to handle multi-scale temporal information, we adopt an additional temporal encoding layer. During training, the majority of the pre-trained parameters are frozen, with only the input/output layers and the fully connected layer involved in parameter updates. The simulation results demonstrate that, compared to the traditional AI-based baselines, the proposed LM-based framework acquires a lower training loss and converges faster. Moreover, it achieves better prediction accuracy with less resource consumption. © 1986-2012 IEEE.

关键词： Spatio-temporal data

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jie Chen Wenbin Liao Du Wen Jianqiang Li Fangzhong Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology Tianjin University China

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model’s robustness.

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Panicle-Cloud:An Open and Al-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

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Plant Phenomics 2023年第4期5卷 905-916页

作者： Zixuan Teng Jiawei Chen Jian Wang Shuixiu Wu Riqing Chen Yaohai Lin Liyan Shen Robert Jackson Ji Zhou Changcai Yang Digital Fujian Research Institute of Big Data for Agriculture and Forestry College of Computer and Information SciencesFujian Agriculture and Forestry UniversityFuzhou 350002China State Key Laboratory of Crop Genetics&Germplasm Enhancement academy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjing 210095China Ningxia Academy of Agriculture and Forestry Sciences Yinchuan 750002China College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China Cambridge Crop Research National Institute of Agricultural Botany(NIAB)Cambridge CB3 OLEUK Key Laboratory of Smart Agriculture and Forestry(Fujian Agriculture and Forestry University) Fujian Province UniversityFuzhou 350002China Center for Agroforestry Mega Data Science School of Future TechnologyFujian Agriculture and Forestry UniversityFuzhou 350002China

Rice(Oryza sativa)is an essential stable food for many rice consumption nations in the world and,thus,the importance to improve its yield production under global climate *** evaluate different rice varieties'yield performance,key yield-related traits such as panicle number per unit area(PNpM^(2))are key indicators,which have attracted much attention by many plant research ***,it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM^(2)trait due to complex field conditions,a large variation of rice cultivars,and their panicle morphological ***,we present Panicle-Cloud,an open and artificial intelligence(AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected *** facilitate the development of Al-powered detection models,we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists;then,we integrated several state-of-the-art deep learning models(including a preferred model called Panicle-AI)into the Panicle-Cloud platform,so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial *** trialed the Al models with images collected at different attitudes and growth stages,through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were ***,we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM^(2)trait from hundreds of rice *** correlation analysis between computational analysis and manual scoring,we found that the platform could quantify the PNpM^(2)trait reliably,based on which yield production was classified with high ***,we trust that our work demonstrates a valuable advance in phenotyping the PNpM^(2)trait in rice,which provides a useful toolkit to enable rice br

关键词： breeding cultivar hundreds

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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RSP-Hist: Approximate Histograms for big data Exploration on Hadoop Clusters

RSP-Hist: Approximate Histograms for Big Data Exploration on...

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International Conference on High Performance Computing

作者： Salman Salloum Joshua Zhexue Huang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665410175

We propose a sampling-based method, called RSP-Hist, to construct approximate equi-width histograms and help data scientists explore the probability distribution of big data on Hadoop clusters. In RSP- Hist, the Random Sample Partition (RSP) model is used to store a big data set as ready-to-use random sample data blocks, called RSP blocks, in the Hadoop Distributed File System (HDFS). An approximate histogram is computed by applying a sequential histogram algorithm in parallel to each block in a block-level sample of RSP blocks. Local histograms from individual RSP blocks are combined to produce an approximate histogram for the entire data. We tested RSP-Hist on four data sets using a small computing cluster. In this paper, we demonstrate the effect of the sampling rate and the number of buckets on the histogram accuracy and show that RSP-based approximate histograms are equivalent to the exact histograms computed from the entire data. RSP- Hist can avoid the data correlation issue in HDFS blocks and significantly reduce both computation and communication costs. It enables iterative and interactive exploration of big data sets on small computing clusters and can be used for multivariate data exploration.

关键词： Histograms Costs Correlation High performance computing Distributed databases big data data models

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Numerical Singular Integral Methods for Computational Electromagnetics 22

Numerical Singular Integral Methods for Computational Electr...

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22nd IEEE International Conference on Communication Technology, ICCT 2022

作者： Luo, Kuang Ou, Lu Zhang, Chuangfeng Zhang, Ming Li, Jinxin Liao, Shaolin Guangdong Guangzhou China School of Journalism and Communication Hunan University Hunan Province Key Laboratory of Big Data Research and Applications Hunan Changsha China College of Computer Science and Electronic Engineering Hunan University Hunan Changsha China Illinois Institute of Technology Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665470674

Efficient Numerical Singular Integral (NSI) methods have been developed for both 1D and 2D electromagnetics problems, in both the Cartesian and polar coordinates. Through a special type of integration by parts, rigorous mathematics derivation has been performed to decompose the original singular integrals into sum of non-singular integrals that can be evaluated numerically. It can be shown that it is more convenient to evaluate the line-type singularity in the Cartesian coordinate;while it is better to evaluate the point-type singularity in the polar coordinate. Also, numerical schemes to find the order of singularity has been presented. Finally, to validate the 2D numerical singular integral methods, the electromagnetic scattering problem of a 2D reflecting ellipse antenna has been simulated through the Ping-Pong algorithm developed by the authors. © 2022 IEEE.

关键词： Computational electromagnetics

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Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

arXiv

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arXiv 2023年

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Computing and Mathematical Sciences University of Leicester United Kingdom

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5 .MSC Codes 68T40 Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Stochastic Security Ephemeral Generation Protocol for 5G Enabled Internet of Things 7th

Stochastic Security Ephemeral Generation Protocol for 5G Ena...

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7th EAI International Conference on the Internet of Things as a Service, IoTaaS 2021

作者： Al Sibahee, Mustafa A. Nyangaresi, Vincent Omollo Ma, Junchao Abduljabbar, Zaid Ameen College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Computer Technology Engineering Department Iraq University College Basrah Iraq Faculty of Biological and Physical Sciences Tom Mboya University College Homabay Kenya Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen China

ISBN: (纸本)9783030959869

To ensure secure access to the data held in internet of things, many lightweight authentication schemes have been developed using approaches such as symmetric cryptography or hashing operations. Although these schemes achieve forward key secrecy and user anonymity, de-synchronization is a major problem in these protocols. As such, many other schemes have been presented to address this pertinent security challenge. However, some of these schemes are still susceptible to smart card loss attacks among others. In this paper, stochastic security ephemeral generation protocol for 5G enabled internet of things is presented. It is demonstrated to offer mutual authentication and session key agreement. It is also robust against packet replays, eavesdropping and man-in-the-middle attacks. In terms of performance, it has the lowest computation and communication overheads. © 2022, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Internet of things

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A dual-population multi-objective evolutionary algorithm driven by generative adversarial networks for benchmarking and protein-peptide docking

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computers in Biology and Medicine 2024年 168卷 107727-107727页

作者： Cheng, Honglei Wang, Gai-Ge Chen, Liyan Wang, Rui School of Computer Science and Technology Ocean University of China Qingdao China Institute of Big Data and Information Technology Wenzhou University Wenzhou China College of Systems Engineering National University of Defense Technology Changsha China Xiangjiang Laboratory Changsha China

Multi-objective optimization problems (MOPs) are characterized as optimization problems in which multiple conflicting objective functions are optimized simultaneously. To solve MOPs, some algorithms used machine learning models to drive the evolutionary algorithms, leading to the design of a variety of model-based evolutionary algorithms. However, model collapse occurs during the generation of candidate solutions, which results in local optima and poor diversity in model-based evolutionary algorithms. To address this problem, we propose a dual-population multi-objective evolutionary algorithm driven by Wasserstein generative adversarial network with gradient penalty (DGMOEA), where the dual-populations coordinate and cooperate to generate high-quality solutions, thus improving the performance of the evolutionary algorithm. We compare the proposed algorithm with the 7 state-of-the-art algorithms on 20 multi-objective benchmark functions. Experimental results indicate that DGMOEA achieves significant results in solving MOPs, where the metrics IGD and HV outperform the other comparative algorithms on 15 and 18 out of 20 benchmarks, respectively. Our algorithm is evaluated on the LEADS-PEP dataset containing 53 protein-peptide complexes, and the experimental results on solving the protein-peptide docking problem indicated that DGMOEA can effectively reduce the RMSD between the generated and the original peptide's 3D poses and achieve more competitive results. © 2023 Elsevier Ltd

关键词： Multiobjective optimization

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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