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2025 Australian and New Zealand Control Conference, ANZCC 2025

作者： Wu, Yue Zhong, Yuan Yu, Haitao Wang, Jiong Yan, Huaimin Wang, Jingcheng Dai, Xingyuan Zhao, Hongxia Lv, Yisheng Beijing Intelligent Transportation Development Center China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9798331532192

This paper proposes a deep learning-based hierarchical warning method designed to detect and respond to abnormal ride-hailing vehicle clustering. By integrating previously developed traffic forecasting and anomaly detection techniques, the method can differentiate varying degrees of clustering severity and trigger appropriate actions. The proposed approach fuses multi-source traffic data, utilizing a convolution-based spatio-temporal feature extraction module to predict ride-hailing vehicle arrival times, and implements a hierarchical warning system based on predefined thresholds. Validation results show that the proposed method can assess the difference between predicted and actual arrival times to evaluate the model’s confidence. On a real historical dataset from Beijing, significant deviations were used to identify abnormal ride-hailing behavior, achieving accurate graded warnings. © 2025 IEEE.

关键词： Data fusion

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Recognition and Early Warning of Ride-Hailing Vehicle Aggregation Based on Multi-Source Spatiotemporal Data Fusion

Recognition and Early Warning of Ride-Hailing Vehicle Aggreg...

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Control Conference (ANZCC), Australian and New Zealand

作者： Yue Wu Yuan Zhong Haitao Yu Jiong Wang Huaimin Yan Jingcheng Wang Xingyuan Dai Hongxia Zhao Yisheng Lv Beijing Intelligent Transportation Development Center State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Institute of Automation

ISBN: (数字)9798331532192

ISBN: (纸本)9798331532208

This paper proposes a deep learning-based hierarchical warning method designed to detect and respond to abnormal ride-hailing vehicle clustering. By integrating previously developed traffic forecasting and anomaly detection techniques, the method can differentiate varying degrees of clustering severity and trigger appropriate actions. The proposed approach fuses multi-source traffic data, utilizing a convolution-based spatio-temporal feature extraction module to predict ride-hailing vehicle arrival times, and implements a hierarchical warning system based on predefined thresholds. Validation results show that the proposed method can assess the difference between predicted and actual arrival times to evaluate the model's confidence. On a real historical dataset from Beijing, significant deviations were used to identify abnormal ride-hailing behavior, achieving accurate graded warnings.

关键词： Accuracy Fuses Data integration Predictive models Alarm systems Feature extraction Spatiotemporal phenomena Forecasting Anomaly detection

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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MindEye-OmniAssist: A Gaze-Driven LLM-Enhanced Assistive Robot System for Implicit Intention Recognition and Task Execution

arXiv

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arXiv 2025年

作者： Zhang, Zejia Yang, Bo Chen, Xinxing Shi, Weizhuang Wang, Haoyuan Luo, Wei Huang, Jian Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China State key laboratory of intelligent vehicle safety technology chongqing changan automobile co ltd Chongqing400023 China Science and technology innovation center China ship development and design centre Wuhan430060 China

A promising effective human-robot interaction in assistive robotic systems is gaze-based control. However, current gaze-based assistive systems mainly help users with basic grasping actions, offering limited support. Moreover, the restricted intent recognition capability constrains the assistive system’s ability to provide diverse assistance functions. In this paper, we propose an open implicit intention recognition framework powered by Large Language Model (LLM) and Vision Foundation Model (VFM), which can process gaze input and recognize user intents that are not confined to predefined or specific scenarios. Furthermore, we implement a gaze-driven LLM-enhanced assistive robot system (MindEye-OmniAssist) that recognizes user’s intentions through gaze and assists in completing task. To achieve this, the system utilizes open vocabulary object detector, intention recognition network and LLM to infer their full intentions. By integrating eye movement feedback and LLM, it generates action sequences to assist the user in completing tasks. Real-world experiments have been conducted for assistive tasks, and the system achieved an overall success rate of 41/55 across various undefined tasks. Preliminary results show that the proposed method holds the potential to provide a more user-friendly human-computer interaction interface and significantly enhance the versatility and effectiveness of assistive systems by supporting more complex and diverse task. Copyright © 2025, The Authors. All rights reserved.

关键词： Eye movements

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Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection

arXiv

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arXiv 2025年

作者： Li, Yong Liu, Menglin Cui, Zhen Ding, Yi Zong, Yuan Zheng, Wenming Shan, Shiguang Guan, Cuntai School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing210096 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Key Laboratory of Child Development and Learning Science of Ministry of Education School of Biological Science and Medical Engineering Nanjing210096 China

Despite the impressive performance of current vision-based facial action unit (AU) detection approaches, they are heavily susceptible to the variations across different domains and the cross-domain AU detection methods are under-explored. In response to this challenge, we propose a decoupled doubly contrastive adaptation (D2CA) approach to learn a purified AU representation that is semantically aligned for the source and target domains. Specifically, we decompose latent representations into AU-relevant and AU-irrelevant components, with the objective of exclusively facilitating adaptation within the AU-relevant subspace. To achieve the feature decoupling, D2CA is trained to disentangle AU and domain factors by assessing the quality of synthesized faces in cross-domain scenarios when either AU or domain attributes are modified. To further strengthen feature decoupling, particularly in scenarios with limited AU data diversity, D2CA employs a doubly contrastive learning mechanism comprising image and feature-level contrastive learning to ensure the quality of synthesized faces and mitigate feature ambiguities. This new framework leads to an automatically learned, dedicated separation of AU-relevant and domain-relevant factors, and it enables intuitive, scale-specific control of the cross-domain facial image synthesis. Extensive experiments demonstrate the efficacy of D2CA in successfully decoupling AU and domain factors, yielding visually pleasing cross-domain synthesized facial images. Meanwhile, D2CA consistently outperforms state-of-the-art cross-domain AU detection approaches, achieving an average F1 score improvement of 6%-14% across various cross-domain scenarios. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

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Computer Simulation of Three-Dimensional Gas Filtration Process in Porous Media

Computer Simulation of Three-Dimensional Gas Filtration Proc...

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Kurbonov, Nozim Research Institute for Development of Digital Technologies and Artificial Intelligence Laboratory 'Modeling of Complex Systems' Tashkent Uzbekistan

ISBN: (纸本)9781665432580

The paper deals with development of multidimensional model of gas filtration in porous media and numerical algorithm for solving the initial-boundary value problem. The model consists of nonlinear differential equation in partial derivatives with the appropriate initial and boundary conditions. The model makes it possible to evaluate impact of main factors affecting the filtration process such as permeability, porosity, thickness of layers, gas recovery coefficient, viscosity etc. That gives us opportunity to solve practical problems of forecasting the process of gas production as well as making decisions on exploitation of gas deposits. © 2021 IEEE.

关键词： Numerical methods

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Computer modelling of the fluid filtration process in porous media

Computer modelling of the fluid filtration process in porous...

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2022 International Conference on Information Science and Communications Technologies, ICISCT 2022

作者： Kurbonov, Nozim Research Institute for Development of Digital Technologies and Artificial Intelligence Laboratory 'Modeling of Complex Systems' Tashkent Uzbekistan

ISBN: (纸本)9781665472296

In this paper we consider a mathematical model and software tool designed for the study of the filtration process a mixture of the 'oil-gas-water' in a porous medium for concrete practical recommendations and administrative decisions. In the course of the numerical calculations were the main parameters of the process and its change ranges, allowing to determine the operation of the reservoir depending on the length and capacity, also the number of wells and production rates. The suitability of the developed algorithm and program for calculation of fields of pressure and saturation are demonstrated, as well as field development indicators in the 'Oil-Gas' systems, the 'oil-water', 'oil-gas-water'. Results of numerical experiments on computer are presented as graphics. © 2022 IEEE.

关键词： Numerical methods

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The role of GPT in promoting inclusive higher education for people with various learning disabilities: a review

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PeerJ Computer Science 2025年 11卷 e2400-e2400页

作者： Gadekallu, Thippa Reddy Yenduri, Gokul Kaluri, Rajesh Rajput, Dharmendra Singh Lakshmanna, Kuruva Fang, Kai Chen, Junxin Wang, Wei Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Computer Science Engineering and Information Systems Vellore Institute of Technology University Tamil Nadu India Zhongda Group Jiaxing China College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India School of Mathematics and Computer Science Zhejiang Agricultural and Forestry University Hangzhou China School of Software Dalian University of Technology Dalian China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China School of Medical Technology Beijing Institute of Technology Beijing China

The generative pre-trained transformer (GPT) is a notable breakthrough in the field of artificial intelligence, as it empowers machines to effectively comprehend and engage in interactions with humans. The GPT exhibits the capacity to enhance inclusivity and accessibility for students with learning disabilities in the context of higher education, hence potentially facilitating substantial advancements in the field. GPT can provide personalized and diverse solutions that successfully cater to the distinct requirements of students with learning disabilities. This motivated us to conduct an extensive review to assess the effectiveness of GPT in enhancing accessibility and inclusivity in higher education for students with learning disabilities. This review offers a comprehensive analysis of the GPT and its significance for enhancing inclusivity in the field of higher education. In this research, we also examined the possible challenges and constraints associated with the integration of GPT into inclusive higher education, along with potential solutions. Overall, this review is intended for educators, students with and without learning disabilities, policymakers, higher education institutes, researchers, and educational technology developers. This review aims to provide a comprehensive understanding of GPT in promoting inclusive higher education for people with various learning disabilities, its impacts on inclusive higher education, emerging challenges, and potential solutions. Copyright 2025 Gadekallu et al.

关键词： Contrastive Learning

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Affine Frequency Division Multiplexing: Extending OFDM for Scenario-Flexibility and Resilience

arXiv

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arXiv 2025年

作者： Yin, Haoran Tang, Yanqun Bemani, Ali Kountouris, Marios Zhou, Yu Zhang, Xingyao Liu, Yuqing Chen, Gaojie Yang, Kai Liu, Fan Masouros, Christos Li, Shuangyang Caire, Giuseppe Xiao, Pei School of Electronics and Communication Engineering Sun Yat-sen University China Mathematical and Algorithmic Sciences Laboratory Huawei France Research and Development France Communication Systems Department EU-RECOM France Department of Computer Science and Artificial Intelligence University of Granada Spain Sun Yat-sen University China School of Information and Electronics Beijing Institute of Technology China National Mobile Communications Research Laboratory Southeast University Bangladesh Department of Electrical and Electronic Engineering University College London United Kingdom Department of Electrical Engineering and Computer Science Technical University of Berlin Germany 5G/6G Innovation Centre University of Surrey United Kingdom

Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing (OFDM) waveform, leading to severe performance degradation. This calls for a new air interface design that can accommodate the severe delay-Doppler spreads in highly dynamic channels while possessing sufficient flexibility to cater to various applications. This article provides a comprehensive overview of a promising chirp-based waveform named affine frequency division multiplexing (AFDM). It is featured with two tunable parameters and achieves optimal diversity order in doubly dispersive channels (DDC). We study the fundamental principle of AFDM, illustrating its intrinsic suitability for DDC. Based on that, several potential applications of AFDM are explored. Furthermore, the major challenges and the corresponding solutions of AFDM are presented, followed by several future research directions. Finally, we draw some instructive conclusions about AFDM, hoping to provide useful inspiration for its development. Copyright © 2025, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Dynamic S-Box Generation Algorithm with Improved Strict Avalanche Criterion by Selection of Adjacency Matrix Parameters 3

Dynamic S-Box Generation Algorithm with Improved Strict Aval...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Abdurazzokov, Javokhir Digital Technologies and Artificial Intelligence Development Research Institute Doctoral Student of Intelligent Systems Laboratory Tashkent100125 Uzbekistan

ISBN: (纸本)9798350342338

This paper covers the problem of generating dynamic S-boxes with robust properties for cryptographic algorithms. Dynamic S-box calculation was performed using affine transformation. The importance of S-boxes, which are used today for modern block encryption, is studied. When calculating S-box values, it is emphasized that their design is robust to various cryptographic properties. The proposed method showed that it is possible to construct dynamic S-boxes resistant to various cryptographic requirements by selecting elements from a directed adjacency matrix. Research has shown that S-boxes generated using this method exhibit 112 nonlinearity with a differential convergence probability of 4/256 and an average strict avalanche criterion (SAC) of 0.5. These indicators show increased resistance to non-linear and other cryptographic attacks. This method contributes to the development of S-box design methodology by providing valuable insights into the design of secure and robust cryptographic systems. © 2023 IEEE.

关键词： adjacency matrix affine transformation block cipher cryptography dynamic S-boxes nonlinearity strict avalanche criterion

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