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Brain-inspired artificial intelligence research: A review

science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Dynamic Graph Neural Networks-Based Alert Link Prediction for Online Service Systems

Dynamic Graph Neural Networks-Based Alert Link Prediction fo...

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Yiru Chen Chenxi Zhang Zhen Dong Dingyu Yang Xin Peng Jiayu Ou Hong Yang Zheshun Wu Xiaojun Qu Wei Li School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Alibaba Group China

A fault in large online service systems often triggers numerous alerts due to the complex business and component dependencies among services, which is known as “alert storm”. In a short time, an online service system may generate a huge amount of alert data. This poses a challenge for on-call engineers to identify alerts that are associated with a system failure for root cause analysis. In this paper, we propose DyAlert, a dynamic graph neural networks-based approach for linking alerts that might be triggered by a same fault to reduce the burden of on-call engineers in the fault analysis. Our insight is that alerts are often triggered by alert propagation when a system failure occurs, e.g., alert $a$ would lead to the occurrence of alert $b$ . Whether two alerts should be linked depends on if one alert is triggered by the propagation of the other. Leveraging this insight, we design a dynamic graph (namely Alert-Metric Dynamic Graph) that describes the propagation process of alerts. Based on the dynamic graph, we train a neural networks-based model to predict alert links. We evaluate DyAlert with real-world data collected from an online service system running 85 business units and about 30,000 different services in a large enterprise. The results show that DyAlert is effective in predicting alert links and it outperforms the state-of-the-art approaches with an average increase of 0.259 in F1-score.

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Dynamic Graph Neural Networks-Based Alert Link Prediction for Online Service Systems 23

Dynamic Graph Neural Networks-Based Alert Link Prediction fo...

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Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering

作者： Yiru Chen Chenxi Zhang Zhen Dong Dingyu Yang Xin Peng Jiayu Ou Hong Yang Zheshun Wu Xiaojun Qu Wei Li School of Computer Science Fudan University China and Shanghai Key Laboratory of Data Science Fudan University China and Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Alibaba Group China

ISBN: (纸本)9798350329964

A fault in large online service systems often triggers numerous alerts due to the complex business and component dependencies among services, which is known as "alert storm". In a short time, an online service system may generate a huge amount of alert data. This poses a challenge for on-call engineers to identify alerts that are associated with a system failure for root cause analysis. In this paper, we propose DyAlert, a dynamic graph neural networks-based approach for linking alerts that might be triggered by a same fault to reduce the burden of on-call engineers in the fault analysis. Our insight is that alerts are often triggered by alert propagation when a system failure occurs, e.g., alert a would lead to the occurrence of alert b. Whether two alerts should be linked depends on if one alert is triggered by the propagation of the other. Leveraging this insight, we design a dynamic graph (namely Alert-Metric Dynamic Graph) that describes the propagation process of alerts. Based on the dynamic graph, we train a neural networks-based model to predict alert links. We evaluate DyAlert with real-world data collected from an online service system running 85 business units and about 30,000 different services in a large enterprise. The results show that DyAlert is effective in predicting alert links and it outperforms the state-of-the-art approaches with an average increase of 0.259 in F1-score.

关键词： linked alerts

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GCNs-Based Context-Aware Short Text Similarity Model

GCNs-Based Context-Aware Short Text Similarity Model

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International Conference on Pattern Recognition

作者： Xiaoqi Sun Shaochun Wu Yue Liu School of Computer Engineering and Science Shanghai University School of Computer Engineering and Science Shanghai University Shanghai Institute for Advanced Communication and Data Science Shanghai Engineering Research Center of Intelligent Computing System Shanghai China

ISBN: (纸本)9781728188089;9781728188096

Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. The essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which can not capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extends the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.

关键词： Text mining Semantics Syntactics Natural language processing Pattern recognition Task analysis Context modeling

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S2CCT: Self-Supervised Collaborative CNN-Transformer for Few-shot Medical Image Segmentation

S2CCT: Self-Supervised Collaborative CNN-Transformer for Few...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Rongzhou Shu, Ziqi Xie, Weixing Yao, Junfeng Hong, Qingqi Xiamen University National Institute for Data Science in Health and Medicine China Xiamen University Center for Digital Media Computing School of Film School of Informatics Xiamen361005 China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage Fujian and Taiwan Ministry of Culture and Tourism China Xiamen University Institute of Artificial Intelligence Xiamen361005 China

ISBN: (纸本)9798350386226

Self-supervised pre-training followed by fine-tuning is a potent paradigm for few-shot learning, leveraging extensive unlabeled data with remarkable efficacy. Current self-supervised methods often lean towards Vision Transformers (ViTs) rather than CNN-Transformer hybrid architectures, which generally demonstrate superior performance. However, this reliance on ViTs can lead to poor perception of local features by the model. The challenge lies in designing a suitable proxy task for hybrid architectures like CNN-Transformers, which have significant structural differences. Additionally, the current organization of CNN-Transformer hybrid backbones is often sequential, hindering collaboration during pre-training and the acquisition of robust representations. To address these issues, we propose Self-Supervised Collaborative CNN-Transformer (S2CCT) for few-shot medical image segmentation. This framework introduces three innovative designs: (1) a composite proxy task based on image masking and image super-resolution tailored for CNN-Transformer hybrid architectures, enabling the backbone to acquire robust representations during pre-training that can be transferred to downstream tasks;(2) a parallel CNN-Transformer architecture that better attends to multi-scale features in images, making it more suitable for dense prediction tasks like image segmentation;(3) a sparse and dense feature fusion module to enhance collaboration between the two encoders. Experiments demonstrate that S2CCT outperforms previous state-of-the-art methods on two public medical image segmentation benchmarks, i.e., ACDC and KiTs19. The code and pretrained models will be released soon. © 2024 IEEE.

关键词： Image segmentation

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Technical Function-effect Based Patent Multi-to-One Negation Game Model 25

Technical Function-effect Based Patent Multi-to-One Negation...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Du, Lu Liu, Weidong Xiao, Kaiyang Gao, Shixuan Han, Yong College of Computer Science Inner Mongolia University Hohhot China Institute of Scientific and Technical Information of China Beijing China Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China Inner Mongolia Intellectual Property Service Center Hohhot China

ISBN: (纸本)9781665405270

As the number of patent applications increases yearly, the negation relation between patents has become intertwined, which makes it difficult for constructing negation relation in patent examination manually. Therefore, how to automatically construct negation relation between patents has become the hot research. Constructing negation relation is the process of comparing technical features between patents, which depends on the technical features in the patent claims and the technical function-effect. Although there are many researches on patent one-to-one negation, few researches on patent multi-to-one negation. The research on negation relation of multi-to-one patents, has the following challenges: (1) the definition of patent features is ambiguous in comparison of negation relation;(2) the role of patent function-effect is often ignored in judgement of negation relation. To solve the above issues, we propose a technical function-effect based patent multi-to-one negation game model. In the model, we decompose patent technical features into technical utility and technical function-effect. Patent technical utility is mainly realized by using the valence theory in linguistics. After obtaining technical utility, we choose to have the same technical effect, express the technical features of different technical functions, and obtain the patent technical function-effect. Finally, we combine the technical utility and function-effect to construct a technical utility/function-effect game tree to judge the patent negation relation. According to the two-tailed test in the A/B-test, our model outperforms the baseline model in the evaluation measurement. This model can subsequently be used to calculate the portfolio value and the strategic value of the patents. © 2022 IEEE.

关键词： Patents and inventions

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Discovering Traffic Anomaly Propagation in Urban Space Using Traffic Change Peaks 6th

Discovering Traffic Anomaly Propagation in Urban Space Using...

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6th International Conference on data science, ICDS 2019

作者： Huang, Guang-Li Ji, Yimu Liu, Shangdong Zarei, Roozbeh School of Science RMIT University MelbourneVIC3000 Australia School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing Center of HPC China Nanjing210003 China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing210003 China School of Information Technology Deakin University GeelongVIC3125 Australia

ISBN: (纸本)9789811528095

Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as STOTree, are not accurate for two reasons. First, they discover the propagation pattern based on the detected anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal neighborhoods rather than considering from a global perspective, and thus cannot find a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using traffic change peaks, which can visualize the change of traffic anomalies (e.g., congestion and evacuation area) and thus accurately captures traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time period can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level (0–255) of pixels. An adaptive filter is developed to generate traffic change graphs from grid traffic images in consecutive periods, and clustering traffic change peaks along the road is to discover the propagation of traffic anomalies. The effectiveness of the proposed method has been demonstrated using a real-world GPS trajectory dataset. © Springer Nature Singapore Pte Ltd 2020.

关键词： Adaptive filters

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TraceStream: Anomalous Service Localization based on Trace Stream Clustering with Online Feedback

TraceStream: Anomalous Service Localization based on Trace S...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Tong Zhou Chenxi Zhang Xin Peng Zhenghui Yan Pairui Li Jianming Liang Haibing Zheng Wujie Zheng Yuetang Deng School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Tencent Inc. China

Modern large-scale service-based systems such as microservice systems have become increasingly complex, making it hard to localize anomalous services when various issues emerge. Traces record the workflows of requests through service instances and have been widely used in anomaly detection and root cause analysis. Existing trace-based approaches widely use statistical methods or learning-based techniques to detect trace anomalies and localize anomalous services. However, these approaches often suffer from the concept drift problem, i.e., the statistical properties of traces change over time in unforeseen ways. In this paper, we propose TraceStream, an anomalous service localization approach based on trace data stream clustering. TraceStream uses data stream clustering to discover potential anomalous trace clusters in evolving trace data and uses spectrum analysis to localize anomalous services based on the clusters. Moreover, TraceStream can effectively incorporate the online feedback of operation engineers based on the trace clusters to improve the accuracy for localizing anomalous services. Our evaluation confirms that TraceStream can effectively detect anomalies and localize anomalous services in an evolving microservice system. It can effectively incorporate human feedback to further improve the performance of anomalous service localization. Moreover, TraceStream is efficient and its efficiency can be further improved by sampling a small portion of traces by cluster.

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UJN-Land: A Large-Scale High-Resolution Parcel of Land of Multi-temporal dataset with CNN Based Semantic Segmentation Analysis 7th

UJN-Land: A Large-Scale High-Resolution Parcel of Land of Mu...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Shen, Yuan Wang, Yulin Yang, Shiyong Li, Yan Han, Shiyuan Liu, Zhen Xu, Tao School of Information Science and Engineering University of Jinan Jinan250022 China Highresolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Shandong Institutes of Industrial Technonlogy Jinan250102 China Land Spatial Data and Remote Sensing Technology Institute of Shandong Province Jinan250002 China

ISBN: (纸本)9789811672125

Semantic segmentation of land in remote sensing images plays an important role in urban management and rural planning, and can provide intelligent analysis for urban development. Convolutional neural network (CNN) based methods have achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, most of segmentation of land dataset were constructed based on various satellite images and samples that lack the number and diversity of existing datasets. These problems have brought challenges to the improvement and application of CNN based land parcel segmentation methods. This paper presented a large-scale high-resolution land dataset based on CNN analysis. More than 200,000 images of 256*256 pixels were obtained from Gaofen satellite images with a spatial resolution of 0.8 m, in which four category samples were manually annotated. In the experiment, three CNN based semantic segmentation methods were used as experimental baseline methods. The experimental results showed that the availability of the UJN-Land dataset is beneficial to the planning and management of cultivated land. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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Lane Marking Detection Algorithm Based on High-Precision Map 6th

Lane Marking Detection Algorithm Based on High-Precision Map

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6th International Conference on data science, ICDS 2019

作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Wang, Ruchuan School of Computer and Software Nanjing Institute of Industry Technology Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing210003 China Nanjing Center of HPC China Nanjing210003 China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing210003 China

ISBN: (纸本)9789811528095

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © Springer Nature Singapore Pte Ltd 2020.

关键词： Road and street markings

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