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Structure Analysis of Crowd Intelligence Systems

Yunhe Pan

Engineering 2023, Volume 25, Issue 6,   Pages 17-20 doi: 10.1016/j.eng.2021.08.016

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Heading toward Artificial Intelligence 2.0

Yunhe Pan

Engineering 2016, Volume 2, Issue 4,   Pages 409-413 doi: 10.1016/J.ENG.2016.04.018

Abstract: space, and cyberspace, the information environment related to the current development of artificial intelligence

Keywords： Artificial intelligence 2.0     Big data     Crowd intelligence     Cross-media     Human-machine     hybrid-augmented     intelligence    

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Crowd intelligence in AI 2.0 era Review

Wei LI,Wen-jun WU,Huai-min WANG,Xue-qi CHENG,Hua-jun CHEN,Zhi-hua ZHOU,Rong DING

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1,   Pages 15-43 doi: 10.1631/FITEE.1601859

Abstract: As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attractedSpecifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligenceIn this paper, we survey existing studies of crowd intelligence.Then, we introduce four categories of representative crowd intelligence platforms.Finally, we discuss promising future research directions of crowd intelligence.

Keywords： Crowd intelligence     Artificial intelligence 2.0     Crowdsourcing     Human computation    

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A platform of digital brain using crowd power Article

Dongrong XU, Fei DAI, Yue LU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 78-90 doi: 10.1631/FITEE.1700800

Abstract: A powerful platform of digital brain is proposed using crowd wisdom for brain research, based on thecomputational artificial intelligence model of synthesis reasoning and multi-source analogical generatingUsing big data, crowd wisdom, and high performance computers may significantly enhance the capability

Keywords： Artificial intelligence     Digital brain     Synthesis reasoning     Multi-source analogical generating     Crowd wisdom    

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Shadow obstacle model for realistic corner-turning behavior in crowd simulation

Gao-qi HE,Yi JIN,Qi CHEN,Zhen LIU,Wen-hui YUE,Xing-jian LU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 3,   Pages 200-211 doi: 10.1631/FITEE.1500253

Abstract: a novel model known as the shadow obstacle model to generate a realistic corner-turning behavior in crowdBy combining psychological and physical forces together, a full crowd simulation framework is establishedto provide a more realistic crowd simulation.

Keywords： Corner-turning behavior     Crowd simulation     Safety awareness     Rule-based model    

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Detecting interaction/complexitywithin crowd movements using braid entropy Research Papers

Murat AKPULAT, Murat EKİNCİ

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 849-861 doi: 10.1631/FITEE.1800313

Abstract:

The segmentation of moving and non-moving regions in an image within the field of crowd analysis isa crucial process in terms of understanding crowd behavior.The purpose of this study is to better understand crowd behavior by locally measuring the degree of interaction

Keywords： Crowd behavior     Motion segmentation     Motion entropy     Crowd scene analysis     Complexity detection     Braid entropy    

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Research on Social Risk of the Massing Crowd in Public Venues

Li Jianfeng,Liu Mao,Sui Xiaolin

Strategic Study of CAE 2007, Volume 9, Issue 6,   Pages 88-93

Abstract: development of cities becomes more quick,  the accidents happened in public venues resulted form massing crowdTo use the F - N curve,  it is able to analyse the social risk of crowd massing venues.

Keywords： crowd massing risk     social risk     F-N curve     quantitative risk analysis    

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Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd Research Article

Jiaqi GAO, Jingqi LI, Hongming SHAN, Yanyun QU, James Z. WANG, Fei-Yue WANG, Junping ZHANG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2,   Pages 187-202 doi: 10.1631/FITEE.2200380

Abstract: has important applications in public safety and pandemic control. A robust and practical system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain only. Off-the-shelf methods have some drawbacks when handling multiple domains: (1) the models will achieve limited performance (even drop dramatically) among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains, which is called catastrophic forgetting; (2) the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift; (3) it leads to linearly increasing storage overhead, either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available. To overcome these issues, we investigate a new task in incremental domain training setting called lifelong . Its goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental domains. Specifically, we propose a self-distillation learning framework as a benchmark (forget less, count better, or FLCB) for lifelong , which helps the model leverage previous meaningful knowledge in a sustainable manner for better to mitigate the forgetting when new data arrive. A new quantitative metric, normalized Backward Transfer (nBwT), is developed to evaluate the forgetting degree of the model in the process. Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

Keywords： Crowd counting     Knowledge distillation     Lifelong learning    

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Aggregated context network for crowd counting

Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 11,   Pages 1535-1670 doi: 10.1631/FITEE.1900481

Abstract: has been applied to a variety of applications such as video surveillance, traffic monitoring, assembly control, and other public safety applications. Context information, such as perspective distortion and background interference, is a crucial factor in achieving high performance for . While traditional methods focus merely on solving one specific factor, we aggregate sufficient context information into the network to tackle these problems simultaneously in this study. We build a fully convolutional network with two tasks, i.e., main density map estimation and auxiliary . The main task is to extract the multi-scale and spatial context information to learn the density map. The auxiliary task gives a comprehensive view of the background and foreground information, and the extracted information is finally incorporated into the main task by late fusion. We demonstrate that our network has better accuracy of estimation and higher robustness on three challenging datasets compared with state-of-the-art methods.

Keywords： 人群计数；卷积神经网络；密度估计；语义分割；多任务学习    

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INTERACTIVE KNOWLEDGE LEARNING BY ARTIFICIAL INTELLIGENCE FOR SMALLHOLDERS

Frontiers of Agricultural Science and Engineering 2023, Volume 10, Issue 4,   Pages 648-653 doi: 10.15302/J-FASE-2023505

Abstract: Therefore, this article proposes an interactive knowledge learning approach using artificial intelligence

Keywords： artificial intelligence     extension system     non-point source pollution control     smallholders     fertilization    

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A novel convolutional neural network method for crowd counting Research Articles

Jie-hao Huang, Xiao-guang Di, Jun-de Wu, Ai-yue Chen,18s004055@hit.edu.cn,dixiaoguang@hit.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 8,   Pages 1119-1266 doi: 10.1631/FITEE.1900282

Abstract: Crowd , in general, is a challenging task due to the large variation of head sizes in the crowds.networks, i.e., a foreground-segmentation convolutional neural network (FS-CNN) as the front end and a crowd-regression

Keywords： Crowd counting     Density estimation     Segmentation prior map     Uniform function    

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Artificial intelligence in gastroenterology: where are we heading?

Joseph JY Sung, Nicholas CH Poon

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 511-517 doi: 10.1007/s11684-020-0742-4

Abstract: Artificial intelligence (AI) is coming to medicine in a big wave.

Keywords： artificial intelligence     endoscopy     robotics     gastrointestinal diseases    

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The Tong Test: Evaluating Artificial General Intelligence Through Dynamic Embodied Physical and Social

Yujia Peng,Jiaheng Han,Zhenliang Zhang,Lifeng Fan,Tengyu Liu,Siyuan Qi,Xue Feng,Yuxi Ma,Yizhou Wang,Song-Chun Zhu,

Engineering doi: 10.1016/j.eng.2023.07.006

Abstract: The release of the generative pre-trained transformer (GPT) series has brought artificial general intelligence(AGI) to the forefront of the artificial intelligence (AI) field once again.

Keywords： Artificial general intelligence     Artificial intelligence benchmark     Artificial intelligence evaluation     Embodied artificial intelligence     Value alignment     Turing test     Causality    

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Intelligence Originating from Human Beings and Expanding in Industry— A View on the Development of ArtificialIntelligence

Jiang Changjun, Wang Junli

Strategic Study of CAE 2018, Volume 20, Issue 6,   Pages 93-100 doi: 10.15302/J-SSCAE-2018.06.015

Abstract:

Artificial Intelligence (AI) aims to simulate information storage andmechanisms and other intelligent behaviors of a human brain, so that the machine has a certain level of intelligencepaper, the historical integration and evolution of computer science, control science, brain-inspired intelligence, human brain intelligence, and other disciplines or fields closely related to AI are analyzed in depthInternet, the integration of AI calculus and computation, the model and mechanism of brain-inspired intelligence

Keywords： artificial intelligence     human brain intelligence     brain-inspired intelligence     intelligence development    

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HIGH-PERFORMANCE COMPUTATION AND ARTIFICIAL INTELLIGENCE IN PESTICIDE DISCOVERY: STATUS AND OUTLOOK

Frontiers of Agricultural Science and Engineering 2022, Volume 9, Issue 1,   Pages 150-154 doi: 10.15302/J-FASE-2021419

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Title Author Date Type Operation

Structure Analysis of Crowd Intelligence Systems

Yunhe Pan

Journal Article

Heading toward Artificial Intelligence 2.0

Yunhe Pan

Journal Article

Crowd intelligence in AI 2.0 era

Wei LI,Wen-jun WU,Huai-min WANG,Xue-qi CHENG,Hua-jun CHEN,Zhi-hua ZHOU,Rong DING

Journal Article

A platform of digital brain using crowd power

Dongrong XU, Fei DAI, Yue LU

Journal Article

Shadow obstacle model for realistic corner-turning behavior in crowd simulation

Gao-qi HE,Yi JIN,Qi CHEN,Zhen LIU,Wen-hui YUE,Xing-jian LU

Journal Article

Detecting interaction/complexitywithin crowd movements using braid entropy

Murat AKPULAT, Murat EKİNCİ

Journal Article

Research on Social Risk of the Massing Crowd in Public Venues

Li Jianfeng,Liu Mao,Sui Xiaolin

Journal Article

Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd

Jiaqi GAO, Jingqi LI, Hongming SHAN, Yanyun QU, James Z. WANG, Fei-Yue WANG, Junping ZHANG

Journal Article

Aggregated context network for crowd counting

Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn

Journal Article

INTERACTIVE KNOWLEDGE LEARNING BY ARTIFICIAL INTELLIGENCE FOR SMALLHOLDERS

Journal Article

A novel convolutional neural network method for crowd counting

Jie-hao Huang, Xiao-guang Di, Jun-de Wu, Ai-yue Chen,18s004055@hit.edu.cn,dixiaoguang@hit.edu.cn

Journal Article

Artificial intelligence in gastroenterology: where are we heading?

Joseph JY Sung, Nicholas CH Poon

Journal Article

The Tong Test: Evaluating Artificial General Intelligence Through Dynamic Embodied Physical and Social

Yujia Peng,Jiaheng Han,Zhenliang Zhang,Lifeng Fan,Tengyu Liu,Siyuan Qi,Xue Feng,Yuxi Ma,Yizhou Wang,Song-Chun Zhu,

Journal Article

Intelligence Originating from Human Beings and Expanding in Industry— A View on the Development of ArtificialIntelligence

Jiang Changjun, Wang Junli

Journal Article

HIGH-PERFORMANCE COMPUTATION AND ARTIFICIAL INTELLIGENCE IN PESTICIDE DISCOVERY: STATUS AND OUTLOOK

Journal Article