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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Riki Numata Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka IITC School of ICT Robotics and Mechanical Engineering Hankyoung National University Anseong Kyonggi-do Republic of Korea

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Digital holographic microscopy (DHM) can provide three-dimensional information. For this reason, this technology has been applied to various fields such as malaria diagnosis using red blood cell (RBC)s and automatic cancer diagnosis using living cells. In research on automatic diagnosis using deep learning, the quality of training data is very important to improve performance. Therefore, we propose an accurate cell segmentation technology for automatic diagnostic assistance of DHM in this paper. The proposed method uses a segmentation algorithm, Segment Anything, and an image recognition algorithm, Xception. In this paper, to evaluate the proposed method, we use five images containing multiple RBCs and calculate the detection rate and false detection rate. This method can reduce the cost of data acquisition and obtain accurate data by automatically acquiring RBCs from images containing multiple RBCs, making it suitable for the automatic diagnosis of DHM using deep learning.

关键词： Deep learning Image segmentation Accuracy Image recognition Red blood cells Microscopy Training data

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semanti...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Weizhao He Yang Zhang Wei Zhuo Linlin Shen Jiaqi Yang Songhe Deng Liang Sun Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science University of Nottingham China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively.

关键词： Training Metalearning Visualization Semantic segmentation Semantics Prototypes Training data

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An Perception Enhanced Human-Robot Skill Transfer Method for Reactive Interaction 25th

An Perception Enhanced Human-Robot Skill Transfer Method for...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Si, Weiyong Dong, Jiale Wang, Ning Yang, Chenguang School of Computer Science and Electronic Engineering University of Essex Wivenhoe Park ColchesterCO4 3SQ United Kingdom South China University of Technology Guangzhou510640 China Bristol Robotics Lab University of the West of England BristolBS16 1QY United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9783031720611

In this paper, we propose a trust human-robot skill transfer framework, interpretive and reactive dynamic system (IRDS), by investigating the behaviour tree (BT) and dynamic movement primitives (DMPs) enhanced by the feedback from perceived information for dynamic and uncertain tasks and environments. Human sensorimotor control allows interactions with various environments and accomplishes complex manipulation tasks with uncertainty;however, it is still hard for robots to own this capability. In this work, we aim to transfer these reactive skills to robots, which enable robots to interact with humans and environments under varying uncertainty. The main challenges of robot skill learning are the generalization, safety and stability issues during the skills learning and execution autonomously. BT was investigated for task planning and reactive interaction during the robot execution. The dynamic system-based model generates action for the low-level compliant controller. The convergence of the proposed IRDS framework under dynamic interaction and disturbance was proved by control theory. We conducted simulations and physical experiments on the real robots to evaluate the generalization performance and the convergence capability under uncertainty. And the results show that the reactivity, convergence and interaction performance can be guaranteed, and the learned skills can be transferred among different physical robot platforms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human robot interaction

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Resilient distributed optimization under mobile malicious attacks

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IFAC-PapersOnLine 2023年第2期56卷 997-1002页

作者： Yuan Wang Changxin Liu Hideaki Ishii Karl Henrik Johansson Department of Robotics Hunan University Changsha China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Computer Science Tokyo Institute of Technology Yokohama Japan

This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnerabilities of a consensus-based secure distributed optimization protocol under mobile adversaries. Then, a modified resilient distributed optimization algorithm is proposed. We develop conditions on the network structure for both complete and non-complete directed graph cases, under which the proposed algorithm guarantees that the estimates by regular nodes converge to the convex combination of the minimizers of their local functions. Simulations are carried out to verify the effectiveness of our approach.

关键词： Fault-tolerant distributed optimization multi-agent systems network security mobile adversary agents

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhihao Hao Bob Zhang Haisheng Li School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa Macau Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa Macau

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives.

关键词： Training Adaptation models Federated learning Biological system modeling Collaboration Signal processing Regulation Blockchains Security Speech processing

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Breast Tumor Classification with Fine-Tuned Hyperparameter Training using Deep Learning Models

Breast Tumor Classification with Fine-Tuned Hyperparameter T...

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AI-Driven Smart Healthcare for Society 5.0

作者： Yash Maini Sanjay Kumar Singh Pranshu Saxena University School of Automation and Robotics Guru Gobind Singh Indraprastha University Surajmal Vihar Delhi India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

Breast tumor classification plays a crucial role in early detection and treatment planning for breast cancer. In recent years, deep learning models have shown remarkable success in image-based medical diagnosis. This study focuses on improving breast tumor classification using fine-tuned hyperparameter training of deep learning models. By optimizing key parameters such as learning rate, batch size, and network architecture, the classification accuracy of models like ResNet, AlexNet, GoogleNet and VGG is enhanced. The proposed approach leverages advanced techniques such as transfer learning, data augmentation, and hyperparameter optimization algorithms (Bayesian Optimization along with population-based training) to achieve robust performance on breast cancer datasets. The proposed method is able to achieve 98.46% accuracy while classifying breast cancer dataset. Experimental results demonstrate significant improvements in classification accuracy, precision, recall, and F1-score compared to conventional approaches, making the fine-tuned models more suitable for clinical applications. The findings suggest that hyperparameter tuning in deep learning models is critical to improving model performance and generalization in breast tumor classification tasks.

关键词： Deep learning Training Accuracy Breast tumors Ultrasonic imaging Transfer learning Breast cancer Planning Optimization Tuning

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FAS: Fast ANN-SNN Conversion for Spiking Large Language Models

arXiv

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arXiv 2025年

作者： Chen, Long Song, Xiaotian Song, Andy Chen, BaDong Lv, Jiancheng Sun, Yanan School of Computer Science Sichuan University China School of Computing Technologies Royal Melbourne Institute Technology University Australia Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

Spiking Large Language Models have been shown as a good alternative to LLMs in various scenarios. Existing methods for creating Spiking LLMs, i.e., direct training and ANN-SNN conversion, often suffer from performance degradation and relatively high computational costs. To address these issues, we propose a novel Fast ANN-SNN conversion strategy (FAS) that transforms LLMs into spiking LLMs in two stages. The first stage employs a full-parameter fine-tuning of pre-trained models, so it does not need any direct training from scratch. The second stage introduces a coarse-to-fine calibration method to reduce conversion errors and improve accuracy. Our experiments on both language and vision-language tasks across four different scales of LLMs demonstrate that FAS can achieve state-of-the-art performance yet with significantly reduced inference latency and computational costs. For example, FAS only takes 8 timesteps to achieve an accuracy of 3% higher than that of the OPT-7B model, while reducing energy consumption by 96.63%. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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SEQUEL: Semi-Supervised Preference-based RL with Query Synthesis via Latent Interpolation

SEQUEL: Semi-Supervised Preference-based RL with Query Synth...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel Marta Simon Holk Christian Pek Iolanda Leite Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures Dept. of Cognitive Robotics TU Delft Delft The Netherlands

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Preference-based reinforcement learning (RL) poses as a recent research direction in robot learning, by allowing humans to teach robots through preferences on pairs of desired behaviours. Nonetheless, to obtain realistic robot policies, an arbitrarily large number of queries is required to be answered by humans. In this work, we approach the sample-efficiency challenge by presenting a technique which synthesizes queries, in a semi-supervised learning perspective. To achieve this, we leverage latent variational autoencoder (VAE) representations of trajectory segments (sequences of state-action pairs). Our approach manages to produce queries which are closely aligned with those labeled by humans, while avoiding excessive uncertainty according to the human preference predictions as determined by reward estimations. Additionally, by introducing variation without deviating from the original human’s intents, more robust reward function representations are achieved. We compare our approach to recent state-of-the-art preference-based RL semi-supervised learning techniques. Our experimental findings reveal that we can enhance the generalization of the estimated reward function without requiring additional human intervention. Lastly, to confirm the practical applicability of our approach, we conduct experiments involving actual human users in a simulated social navigation setting. Videos of the experiments can be found at https://***/view/rl-sequel

关键词： Interpolation Uncertainty Navigation Estimation Reinforcement learning Semisupervised learning Robot learning

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Collision Avoidance for Multiple UAVs in Unknown Scenarios with Causal Representation Disentanglement

arXiv

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arXiv 2024年

作者： Zhuang, Jiafan Xia, Zihao Han, Gaofei Wang, Boxi Li, Wenji Wang, Dongliang Hao, Zhifeng Cai, Ruichu Fan, Zhun College of Engineering Shantou University China School of Computer Science Guangdong University of Technology China University of Electronic Science and Technology of China China International Cooperation Base of Evolutionary Intelligence and Robotics China

Deep reinforcement learning (DRL) has achieved remarkable progress in online path planning tasks for multi-UAV systems. However, existing DRL-based methods often suffer from performance degradation when tackling unseen scenarios, since the non-causal factors in visual representations adversely affect policy learning. To address this issue, we propose a novel representation learning approach, i.e., causal representation disentanglement, which can identify the causal and non-causal factors in representations. After that, we only pass causal factors for subsequent policy learning and thus explicitly eliminate the influence of non-causal factors, which effectively improves the generalization ability of DRL models. Experimental results show that our proposed method can achieve robust navigation performance and effective collision avoidance especially in unseen scenarios, which significantly outperforms existing SOTA algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Novel Noise Filtering Method Using Kalman Filter in Digital Holographic Microscopy (DHM)

A Novel Noise Filtering Method Using Kalman Filter in Digita...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Taishi Ono Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of Computer Science and Networks Kyushu Institute of Technology Fukuoka Japan IITC School of ICT Robotics and Mechanical Engineering Hankyong National University Kyonggi-do Republic of Korea

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Three-dimensional (3D) profile by Digital Holographic Microscopy (DHM) always contains noise. This is an issue in the phase information extraction method of DHM system. However, in the conventional phase information extraction from the frequency domain, it may not extract only phase information. To solve this problem, we propose that the extracting phase information from the frequency domain of recorded hologram using DHM system is specified multiple times and Kalman filtering is performed on the multiple 3D profiles. The purpose of this method is to eliminate randomly generated noise and generate highly accurate 3D profiles. The generated 3D profile is subjectively and quantitatively evaluated with conventional methods. As a result, 3D profile of the proposed method shows the higher accuracy than the conventional method.

关键词： Three-dimensional displays Accuracy Shape Microscopy Frequency-domain analysis Noise Noise reduction

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