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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Li, Zihao Gao, Pan Yuan, Hui Wei, Ran Paul, Manoranjan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Control Science and Engineering Shandong University Jinan China Science and Technology on Electro-optic Control Laboratory Luoyang China Charles Sturt University Bathurst Australia

ISBN: (纸本)9798350313154

Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature extractors while ignoring global connection, or vice versa. In this paper, we design a new Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point relations, which considers both local and global attentions. Specifically, considering local spatial coherence, local feature learning is performed through Relative Position Encoding and Attentive Feature Pooling. We incorporate the learned locality into the Transformer module. The local feature affects value component in Transformer to modulate the relationship between channels of each point, which can enhance self-attention mechanism with locality based channel interaction. We demonstrate its superiority experimentally on classification and segmentation tasks. The code is available at: https://***/jiamang/IBT © 2023 IEEE.

关键词： computer vision

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Temporal Dynamics in instrumental Learning: Integrating Entropy Measures and Computational Models

Temporal Dynamics in instrumental Learning: Integrating Entr...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Zakieh Hassanzadeh Fariba Bahrami Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran School of of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

This research explores the dynamics of decision-making within an instrumental learning framework, combining analyses of response times, entropy measures, and computational modeling. We conducted a study using an RLWM task, involving 27 participants and varying set sizes to examine the relationship between task complexity and cognitive functions. Our methodology incorporates Shannon entropy, Cumulative Residual Entropy (CRE), and Instantaneous CRE to assess uncertainty and mental effort across different task conditions. We evaluate two computational models - RLWM and its enhanced version, $\mathbf{~ p i H}$ - to interpret both choice patterns and reaction times. Our findings indicate that set size significantly impacts response times and entropy measures, with increased complexity leading to higher entropy and slower responses. The temporal analysis of iCRE suggests a dynamic shift in cognitive strategies throughout the task. Model comparisons highlight distinct capabilities of RLWM and piH in capturing various aspects of cognitive processes. This comprehensive approach provides novel viewpoints into how the brain balances complex learning mechanisms with simpler rules for efficient decision-making under limited mental resources, contributing to our understanding of adaptive behavior in complex environments.

关键词： Learning systems Computational modeling Instruments Decision making Biomedical measurement Brain modeling Entropy Complexity theory Time factors Load modeling

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A Novel Facial Manipulation Detection Method Based on Contrastive Learning 5

A Novel Facial Manipulation Detection Method Based on Contra...

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5th IEEE International Conference on Electronics Technology, ICET 2022

作者： Ma, Zhiyuan Xu, Pengxiang Mei, Xue Shen, Jie Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781665485081

Nowadays, numerous synthesized face-swapping videos generated by face forgery algorithms have become an emerging problem, which promotes facial manipulation detection to be a significant topic. With the development of face forgery algorithms, some fake face images or videos generated by those strong forgery algorithms are very realistic, which have brought much difficulty to facial manipulation detection. In this paper, we present a novel facial manipulation detection method based on contrastive learning. We analyze theure features of manipulated facial images and propose to compare and learn the features of the whole face and the center face in order to get more general features. We calculate the similarity and distribution distance between the whole face and the center face. The experiments implemented on FaceForensics++ dataset demonstrate that the proposed method achieves outstanding results and can learn the general features. © 2022 IEEE.

关键词： Deep learning

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Carousel Storage and Picking Scheduling Issues: A Review

Carousel Storage and Picking Scheduling Issues: A Review

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Qin, Shujin Yang, Xinyi Wang, Jiacun Liu, Shixin Guo, Xiwang Qi, Liang Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal University Shangqiu476000 China Monmouth University Department of Computer Science and Software Engineering West Long BranchNJ07764 United States College of Information Science and Engineering Northeast University Shenyang110819 China College of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9798350337020

This paper classifies and summarises the historical literature on carousel systems in automated storage and retrieval systems in recent years. As an automated storage and retrieval system for distribution centers and production facilities, carousels facilitate the storage and dispatching of goods, significantly improving warehouse turnover efficiency. Their performance have been investigated by many scholars and experts. As carousels evolve and upgrade, more and more innovative algorithms have been used to improve the efficiency of outbound carousel storage. In this paper, we collate articles investigating how the carousel system is stored inbound versus retrieved outbound. We then discuss articles on the dual-command model of automatic storage retrieval systems as a whole. By reviewing over 50 papers, we summarise research on how to store and unload goods, focusing on the performance of automatic storage retrieval systems under dual-command conditions. On this basis, we review the current research's limitations and suggest future research directions. © 2023 IEEE.

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Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes

arXiv

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arXiv 2024年

作者： Huang, Yan Li, Xiang Shen, Yipeng He, Niao Xu, Jinming College of Control Science and Engineering Zhejiang University China Department of Computer Science ETH Zurich Switzerland

In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive stepsizes. To address this challenge, we propose D-AdaST, a Distributed Adaptive minimax method with Stepsize Tracking. The key strategy is to employ an adaptive stepsize tracking protocol involving the transmission of two extra (scalar) variables. This protocol ensures the consistency among stepsizes of nodes, eliminating the steady-state error due to the lack of coordination of stepsizes among nodes that commonly exists in vanilla distributed adaptive methods, and thus guarantees exact convergence. For nonconvex-strongly-concave distributed minimax problems, we characterize the specific transient times that ensure time-scale separation of stepsizes and quasi-independence of networks, leading to a near-optimal convergence rate of O∼ (ϵ−(4+δ)) for any small δ > 0, matching that of the centralized counterpart. To our best knowledge, D-AdaST is the first distributed adaptive method achieving near-optimal convergence without knowing any problem-dependent parameters for nonconvex minimax problems. Extensive experiments are conducted to validate our theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Multimodal Large Models are Effective Action Anticipators

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IEEE Transactions on Multimedia 2025年 27卷 2949-2960页

作者： Wang, Binglu Tian, Yao Wang, Shunzhou Yang, Le Xi'an University of Architecture and Technology College of Information and Control Engineering Xi'an710055 China Northwestern Polytechnical University School of Astronautics Xi'an710072 China Peking University School of Electronic and Computer Engineering Shenzhen Graduate School Shenzhen518055 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230022 China University of Science and Technology of China School of Information Science and Technology Hefei230022 China

The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall short in addressing these challenges. Large Language Models (LLMs), with their robust sequential modeling capabilities and extensive commonsense knowledge, present new opportunities for long-term action anticipation. In this work, we introduce the ActionLLM framework, a novel approach that treats video sequences as successive tokens, leveraging LLMs to anticipate future actions. Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer, enabling straightforward action prediction without the need for complex instructions or redundant descriptions. To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding. In addition, we introduce a Cross-Modality Interaction Block, designed to explore the specificity within each modality and capture interactions between vision and textual modalities, thereby enhancing multimodal tuning. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed ActionLLM framework, encouraging a promising direction to explore LLMs in the context of action anticipation. © 1999-2012 IEEE.

关键词： Semantics

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Analysis and Design of a New Type of Polymer-Magnet Permanent Magnet Fault-Tolerant Vernier Motor 7

Analysis and Design of a New Type of Polymer-Magnet Permanen...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Li, Dan Cao, Yuhao Wang, Bo College of Computer and Control Engineering Northeast Forestry University Harbin China China Telecom Digital Intelligence Technology Co. Ltd. Heilongjiang Branch General Manager of Digital People's Livelihood Department Harbin China

ISBN: (纸本)9798350359558

This study focuses on the Spoke Array Fault-Tolerant Permanent Magnet Vernier Machine (SAFTPMVM) which, due to its magnetic concentration effect, has a high torque density. However, an increase in air gap length significantly affects this magnetic concentration, thereby drastically reducing the motor's torque density. To address this issue, a new structure called the Fault-Tolerant Concentrated Flux Permanent Magnet Vernier Machine (FCFTPMVM) is proposed. This structure enhances the fundamental and low-order harmonic of the air gap magnetic density, thus increasing torque density without additional permanent magnet material. The principle of torque enhancement was analyzed using the equivalent magnetic circuit method, and the impact of structural parameters on torque performance was studied using finite element methods. This led to the design of optimal motor structural parameters. A comparative analysis of the electromagnetic performance of surface-mounted, spoke array, and the new concentrated flux structure motors was conducted using finite element analysis. The results demonstrate that the proposed FCFTPMVM motor exhibits high output torque, low torque ripple, high efficiency, and strong fault tolerance capabilities. © 2024 IEEE.

关键词： Permanent magnets

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Locate N’ Rotate: Two-Stage Openable Part Detection with Foundation Model Priors 17th

Locate N’ Rotate: Two-Stage Openable Part Detection with F...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Li, Siqi Chen, Xiaoxue Cheng, Haoyu Zhou, Guyue Zhao, Hao Tian, Guanzhong Ningbo innovation Center Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Institute for AI Industry Research Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789819609628

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to unseen environments and objects. In this paper, we propose a Transformer-based Openable Part Detection (OPD) framework named Multi-feature Openable Part Detection (MOPD) that incorporates perceptual grouping and geometric priors, outperforming previous methods in performance. In the first stage of the framework, we introduce a perceptual grouping feature model that provides perceptual grouping feature priors for openable part detection, enhancing detection results through a cross-attention mechanism. In the second stage, a geometric understanding feature model offers geometric feature priors for predicting motion parameters. Compared to existing methods, our proposed approach shows better performance in both detection and motion parameter prediction. Codes and models are publicly available at https://***/lisiqi-zju/MOPD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Intelligent robots

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Application of functional near-infrared spectroscopy(fNIRS) for studying the influence of capsaicin stimulation on brain perception

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Food Science and Human Wellness 2025年

作者： Ran Tao Hang Liang Huiting Qiao Xin Liu Na Xu Ling Wang Xinglian Xu Lingqi Li Yujia Yang Peng Wang State Key Laboratory of Meat Quality Control and Cultured Meat Development， Ministry of Education China， Jiangsu Collaborative Innovation Center of Meat Production and Processing， Quality and Safety Control， College of Food Science and Technology， Nanjing Agricultural University Danyang SuHuichuang Brain Scientific Research Center School of Biological Science and Medical Engineering， Beihang University College of Computer Science， Sichuan Normal University

Spicy taste have been utilized in the food industry as appetizers， stomachics， and beverages and in the health sector to alleviate high cholesterol and hypertension. However， an understanding of neural mechanisms and cognitive processes associated with spicy diets remains limited due to the lack of instruments that directly monitoring the brain’s reaction to spicy sensations. This study aimed to examine the neural responses of participants to oral capsaicin solutions through functional near-infrared spectroscopy and reveal distinct neural activation patterns associated with spiciness induction and relief phases. Results showed that the perception of spiciness is a dynamic process， and significant differences in sensory intensity and emotion were observed between the phases. The prefrontal regions of the brain were activated during spicy flavor perception and presented clear lateralization effects. Further spatial localization results indicated that the orbitofrontal， dorsolateral prefrontal， and ventral lateral prefrontal cortices were the main causes of difference in activation among the phases. In addition， correlational analyses indicated that spicy flavor perception activated cortical areas related to emotion and reward processing. Overall， this research enhances our understanding of the neural mechanisms underlying addiction to spicy food and may be useful in the development of food on the basis of consumers’ preferences and behavioral responses.

关键词： Taste Capsaicin fNIRS Prefrontal cortex Oral irritation

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HLR-Net: A Hybrid Lip-Reading Model Based on Deep Convolutional Neural Networks

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computers, Materials & Continua 2021年第8期68卷 1531-1549页

作者： Amany M.Sarhan Nada M.Elshennawy Dina M.Ibrahim Department of Computers and Control Engineering Faculty of EngineeringTanta UniversityTanta37133Egypt Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia

Lip reading is typically regarded as visually interpreting the speaker’s lip movements during the *** is a task of decoding the text from the speaker’s mouth *** paper proposes a lip-reading model that helps deaf people and persons with hearing problems to understand a speaker by capturing a video of the speaker and inputting it into the proposed model to obtain the corresponding *** deep learning technologies makes it easier for users to extract a large number of different features,which can then be converted to probabilities of letters to obtain accurate *** proposed methods for lip reading are based on sequence-to-sequence architectures that are designed for natural machine translation and audio speech ***,in this paper,a deep convolutional neural network model called the hybrid lip-reading(HLR-Net)model is developed for lip reading from a *** proposed model includes three stages,namely,preprocessing,encoder,and decoder stages,which produce the output *** inception,gradient,and bidirectional GRU layers are used to build the encoder,and the attention,fully-connected,activation function layers are used to build the decoder,which performs the connectionist temporal classification(CTC).In comparison with the three recent models,namely,the LipNet model,the lip-reading model with cascaded attention(LCANet),and attention-CTC(A-ACA)model,on the GRID corpus dataset,the proposed HLR-Net model can achieve significant improvements,achieving the CER of 4.9%,WER of 9.7%,and Bleu score of 92%in the case of unseen speakers,and the CER of 1.4%,WER of 3.3%,and Bleu score of 99%in the case of overlapped speakers.

关键词： lip-reading visual speech recognition deep neural network connectionist temporal classification

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