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arXiv 2024年

作者： Wang, Xiao Wang, Yutong Yang, Jing Jia, Xiaofeng Li, Lijun Ding, Weiping Wang, Fei-Yue School of Artificial Intelligence Anhui University Hefei266114 China Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Big Data Collaborative Security Technology Beijing100015 China School of Automation Southeast University Nanjing210018 China School of Information Science and Technology Nantong University Nantong226019 China Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer "Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness "6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion

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STUDENTS RATHER THAN EXPERTS: A NEW AI FOR EDUCATION PIPELINE TO MODEL MORE HUMAN-LIKE AND PERSONALISED EARLY ADOLESCENCES

arXiv

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arXiv 2024年

作者： Ma, Yiping Hu, Shiyu Li, Xuchen Wang, Yipei Liu, Shiqing Cheong, Kang Hao Lab of Artificial Intelligence for Education East China Normal University China School of Computer Science and Technology East China Normal University China School of Physical and Mathematical Sciences Nanyang Technological University Singapore Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Institute of Automation Southeast University China Department of Education East China Normal University China College of Computing and Data Science Nanyang Technological University Singapore

The capabilities of large language models (LLMs) have been applied in expert systems across various domains, providing new opportunities for AI in Education (AI4Education). Educational interactions involve a cyclical exchange between teachers and students. Current research predominantly focuses on using LLMs to simulate teachers, leveraging their expertise to enhance student learning outcomes. However, the simulation of students, which could improve teachers’ instructional skills, has received insufficient attention due to the challenges of modeling and evaluating virtual students. This research poses the question: "Can LLMs be utilized to develop virtual student agents that mimic human-like performance and individual variability?" Unlike expert systems focusing on knowledge delivery, virtual students must replicate learning difficulties, emotional responses, and linguistic uncertainties. These traits present significant challenges in both modeling and evaluation. To address these issues, this study focuses on language learning as a context for modeling virtual student agents. We propose a novel AI4Education framework, termed SOE (Scene - Object - Evaluation), to systematically construct LVSA (LLM-based Virtual Student Agents). By curating a dataset of personalized teacher-student interactions with various personality traits, question types, and learning stages, and fine-tuning LLMs using LoRA, we conduct multi-dimensional evaluation experiments. Specifically, we: (1) develop a theoretical framework for generating LVSA;(2) integrate human subjective evaluation metrics into GPT-4 assessments, demonstrating a strong correlation between human evaluators and GPT-4 in judging LVSA authenticity;and (3) validate that LLMs can generate human-like, personalized virtual student agents in educational contexts, laying a foundation for future applications in pre-service teacher training and multi-agent simulation environments. More related resources will be released at: https://

关键词： Expert systems

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Can LVLMs Describe Videos like Humans? A Five-in-One Video Annotations Benchmark for Better Human-Machine Comparison

arXiv

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arXiv 2024年

作者： Hu, Shiyu Li, Xuchen Li, Xuzhao Zhang, Jing Wang, Yipei Zhao, Xin Cheong, Kang Hao School of Physical and Mathematical Sciences Nanyang Technological University Singapore Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Automation Beijing Institute of Technology China Institute of Automation Southeast University Bangladesh School of Computer and Communication Engineering University of Science and Technology Beijing China College of Computing and Data Science Nanyang Technological University Singapore

Large vision-language models (LVLMs) have made significant strides in addressing complex video tasks, sparking researchers’ interest in their human-like multimodal understanding capabilities. Video description serves as a fundamental task for evaluating video comprehension, necessitating a deep understanding of spatial and temporal dynamics, which presents challenges for both humans and machines. Thus, investigating whether LVLMs can describe videos as comprehensively as humans—through reasonable human-machine comparisons using video captioning as a proxy task—will enhance our understanding and application of these models. However, current benchmarks for video comprehension have notable limitations, including short video durations, brief annotations, and reliance on a single annotator’s perspective. These factors hinder a comprehensive assessment of LVLMs’ ability to understand complex, lengthy videos and prevent the establishment of a robust human baseline that accurately reflects human video comprehension capabilities. To address these issues, we propose a novel benchmark, FIOVA (Five In One Video Annotations), designed to evaluate the differences between LVLMs and human understanding more comprehensively. FIOVA includes 3,002 long video sequences (averaging 33.6 seconds) that cover diverse scenarios with complex spatiotemporal relationships. Each video is annotated by five distinct annotators, capturing a wide range of perspectives and resulting in captions that are 4 ∼ 15 times longer than existing benchmarks, thereby establishing a robust baseline that represents human understanding comprehensively for the first time in video description tasks. Using the FIOVA benchmark, we conducted an in-depth evaluation of six state-of-the-art LVLMs (VideoLLaMA2, LLaVA-NEXT-Video, Video-LLaVA, VideoChat2, Tarsier, and ShareGPT4Video), comparing their performance with humans. Results show that while current LVLMs demonstrate some perception and reasoning capabilities, they st

关键词： Visual languages

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Three-dimensional ATUM-SEM reconstruction and analysis of hepatic endoplasmic reticulum-organelle interactions

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Journal of Molecular Cell Biology 2021年第9期13卷 636-645页

作者： Yi Jiang Linlin Li Xi Chen Jiazheng Liu Jingbin Yuan Qiwei Xie Hua Han National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 101408China School of Future Technology University of Chinese Academy of SciencesBeijing 101408China Data Mining Lab Beijing University of TechnologyBeijing 100124China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China

The endoplasmic reticulum(ER)is a contiguous and complicated membrane network in eukaryotic cells,and membrane contact sites(MCSs)between the ER and other organelles perform vital cellular functions,including lipid homeostasis,metabolite exchange,calcium level regulation,and organelle ***,we establish a whole pipeline to reconstruct all ER,mitochondria,lipid droplets,lysosomes,peroxisomes,and nuclei by automated tape-collecting ultramicrotome scanning electron microscopy and deep learning techniques,which generates an unprecedented 3D model for mapping liver ***,the morphology of various organelles and the MCSs between the ER and other organelles are systematically *** found that the ER presents with predominantly flat cisternae and is knitted tightly all throughout the intracellular space and around other *** addition,the ER has a smaller volume-to-membrane surface area ratio than other organelles,which suggests that the ER could be more suited for functions that require a large membrane surface *** data also indicate that ER-mitochondria contacts are particularly abundant,especially for branched *** study provides 3D reconstructions of various organelles in liver samples together with important fundamental information for biochemical and functional studies in the liver.

关键词： 3D reconstruction ATUM-SEM deep learning liver ER MCSs

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Special issue: Innovative applications of big data and artificial intelligence

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Frontiers of Engineering Management 2022年第4期9卷 517-519页

作者： W.Art CHAOVALITWONGSE Ye YUAN Qingpeng ZHANG Jianguo LIU Department of Industrial Engineering University of ArkansasFayettevilleAR 72701USA School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Data Science City University of Hong KongHong KongChina Institute of Accounting and Finance Shanghai University of Finance and EconomicsShanghai 200433China

Big data refers to complex data sets that cannot be processed using traditional data processing *** could also be defined as a large amount of unstructured or structured data from various *** intelligence(AI)refers to the science of expressing and acquiring knowledge using data,algorithms,and computing *** far,the big data industry chain is gradually mature,leading to a combination of big data and AI techniques developed in comprehensive *** cutting-edge technologies of big data and AI have played an increasingly important role in the innovative development of various industries such as epidemic prevention and control,emergency management,social governance,manufacturing,medical services,intelligent transportation,Internet of Things/supply chain,Internet industry,new media,and so *** addition,there are many typical innovative projects,which are being produced.

关键词： artificial services prevention

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Distance-Forward Learning: Enhancing the Forward-Forward Algorithm Towards High-Performance On-Chip Learning

arXiv

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arXiv 2024年

作者： Wu, Yujie Xu, Siyuan Wu, Jibin Deng, Lei Xu, Mingkun Wen, Qinghao Li, Guoqi Department of Computing The Hong Kong Polytechnic University Hong Kong Institute of Automation Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Center for Brain Inspired Computing Research Department of Precision Instrument Tsinghua University Beijing China

The Forward-Forward (FF) algorithm was recently proposed as a local learning method to address the limitations of backpropagation (BP), offering biological plausibility along with memory-efficient and highly parallelized computational benefits. However, it suffers from suboptimal performance and poor generalization, largely due to inadequate theoretical support and a lack of effective learning strategies. In this work, we reformulate FF using distance metric learning and propose a distance-forward algorithm (DF) to improve FF performance in supervised vision tasks while preserving its local computational properties, making it competitive for efficient on-chip learning. To achieve this, we reinterpret FF through the lens of centroid-based metric learning and develop a goodness-based N-pair margin loss to facilitate the learning of discriminative features. Furthermore, we integrate layer-collaboration local update strategies to reduce information loss caused by greedy local parameter updates. Our method surpasses existing FF models and other advanced local learning approaches, with accuracies of 99.7% on MNIST, 88.2% on CIFAR-10, 59% on CIFAR-100, 95.9% on SVHN, and 82.5% on ImageNette, respectively. Moreover, it achieves comparable performance with less than 40% memory cost compared to BP training, while exhibiting stronger robustness to multiple types of hardware-related noise, demonstrating its potential for online learning and energy-efficient computation on neuromorphic chips. © 2024, CC BY-NC-ND.

关键词： Contrastive Learning

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Sustainable Self-evolution Adversarial Training

arXiv

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arXiv 2024年

作者： Wang, Wenxuan Wang, Chenglei Qi, Huihui Ye, Menghao Qian, Xuelin Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an China School of Automation Northwestern Polytechnical University The BRain and Artificial INtelligence Lab Shaanxi Xi’an China

With the wide application of deep neural network models in various computer vision tasks, there has been a proliferation of adversarial example generation strategies aimed at deeply exploring model security. However, existing adversarial training defense models, which rely on single or limited types of attacks under a one-time learning process, struggle to adapt to the dynamic and evolving nature of attack methods. Therefore, to achieve defense performance improvements for models in long-term applications, we propose a novel Sustainable Self-Evolution Adversarial Training (SSEAT) framework. Specifically, we introduce a continual adversarial defense pipeline to realize learning from various kinds of adversarial examples across multiple stages. Additionally, to address the issue of model catastrophic forgetting caused by continual learning from ongoing novel attacks, we propose an adversarial data replay module to better select more diverse and key relearning data. Furthermore, we design a consistency regularization strategy to encourage current defense models to learn more from previously trained ones, guiding them to retain more past knowledge and maintain accuracy on clean samples. Extensive experiments have been conducted to verify the efficacy of the proposed SSEAT defense method, which demonstrates superior defense performance and classification accuracy compared to competitors. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Research on Human Features Semantic Segmentation Based on Laser Point Cloud 12

Research on Human Features Semantic Segmentation Based on La...

引用

12th International Conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2022

作者： Ma, Tianyi Xuan, Bokai Li, Jian Xu, Yuexuan Liu, Minghe Ding, Qingsong Wang, Jianwen Sun, Hao School of Artificial Intelligence and Data Science Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of Education Hebei University of Technology Tianjin300130 China School of Automation Beijing University of Posts and Telecommunications Beijing100876 China Shun Kangda Medical Technology Company Limited Foshan528200 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In view of the problems of health care for the semi-disabled elderly, this paper studies the semantic segmentation of human features in a bathing environment with a scrubbing device. Firstly three-dimensional point cloud data of different types to construct a human model is collected by the lidar. Secondly, overcome the influence of the water fog environment on the modeling by the hybrid filtering algorithm, and the human point cloud area is extracted. Finally, the human semantic segmentation model fusing the spatial feature extraction module and the channel attention module is proposed based on PointNet improvement. After training and testing on the target data set, the results show that the algorithm can accurately identify feature information for 3D human models of different types. The segmentation rate reaches 95.7%, which is 4.5% higher than the PointNet network, significantly improves the segmentation of human features, and has high engineering application value. © 2022 IEEE.

关键词： Optical radar

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Unsupervised Spatio-Temporal State Estimation for Fine-Grained Adaptive Anomaly Diagnosis of Industrial Cyber-Physical Systems

SSRN

引用

SSRN 2024年

作者： Sun, Haili Huang, Yan Han, Lansheng Fu, Cai Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Accurate detection and diagnosis of abnormal behaviors such as network attacks from multivariate time series (MTS) are crucial for ensuring the stable and effective operation of industrial cyber-physical systems (CPS). However, existing researches pay little attention to the logical dependencies among system working states, and have difficulties in explaining the evolution mechanisms of abnormal signals. To reveal the spatio-temporal association relationships and evolution mechanisms of the working states of industrial CPS, this paper proposes a fine-grained adaptive anomaly diagnosis method (i.e. MAD-Transformer) to identify and assess anomalies in MTS. A temporal and spatial state matrix are constructed to characterize state changes and inter-sensor correlations for precise anomaly localization. Subsequently, a three-branch structure of series-temporal-spatial attention module is designed to simultaneously capture the series, temporal, and spatial dependencies among MTS. Three associated alignment loss and a reconstruction loss are constructed to jointly optimize the model. Finally, anomalies are determined and diagnosed by comparing the residual matrices with the original matrices. Comparative experiments on five publicly datasets along with a petroleum refining simulation dataset demonstrate that MAD-Transformer can adaptively detect fine-grained anomalies with short duration, and outperforms the state-of-the-art baselines in terms of noise robustness and localization performance. © 2024, The Authors. All rights reserved.

关键词： State estimation

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