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International Conference on computer Supported Cooperative Work in Design

作者： Qingzhi Zou Jing Zhao Ming Li Ling Chen Lin Yuan Ronghuan Zhang Yushuai Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The primary objective of medical image segmentation is the isolation of pathological tissues and distinct organs from medical images, thereby assisting in medical diagnosis. The methods employed for medical image segmentation encompass convolutional neural networks and transformer-based methods. Although the self-attention mechanism in Transformers improves its capability to capture long-range dependencies, it have limitations in learning local (contextual) relationships between pixels. Some previous research has tried to solve this problem by incorporating convolutional layers into the encoder or decoder of transformers, but sometimes feature inconsistencies arise. To better extract local features from images using convolutional neural networks and to fuse low-resolution and high-resolution features from higher-level and lower features, we propose the Convolutional Attention Augmentation TransUNet (CAA-TransUNet) model. In our model, firstly, we propose a convolutional attention augmentation module that enhances both local and global features by suppressing irrelevant background information. Secondly, we have integrated attention gates into the skip connections to aggregate feature information from various stages of the encoder during the upsampling process. Finally, we employ the technique of aggregating the loss of multi-stage features to expedite convergence speed and enhance performance. The experimental results on three public datasets demonstrate that our proposed model significantly outperforms the baseline methods.

关键词： Image segmentation Pathology Computational modeling Aggregates Logic gates Feature extraction Transformers

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COMBINE EARLY AND LATE FUSION TOGETHER: A HYBRID FUSION FRAMEWORK FOR IMAGE-TEXT MATCHING

COMBINE EARLY AND LATE FUSION TOGETHER: A HYBRID FUSION FRAM...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wang, Yifan Xu, Xing Yu, Wei Xu, Ruicong Cao, Zuo Shen, Heng Tao Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China China Glasgow College University of Electronic Science and Technology of China China Meituan

ISBN: (纸本)9781665438643

Image-text matching is a challenging task in cross-modal learning due to the discrepancy of data representation between different modalities of images and texts. The mainstream methods adopt the late fusion to generate image-text similarity on encoded cross-modal features, and put effort to capture intra-modality associations with considerably high training cost. In this work, we propose to Combine Early and Late Fusion Together (CELFT), which is a universal hybrid fusion framework that can effectively overcome the above shortcomings of the late fusion scheme. In the proposed CELFT framework, the hybrid structure with early fusion and late fusion could facilitate the interaction between image and text modalities at early stage. Moreover, these two kinds of fusion strategies complement each other in capturing the inter-modal and intra-modal information, which ensure to learn more accurate image-text similarity. In the experiments, we choose four latest approaches based on the late fusion scheme as the base models, and integrate them with our CELFT framework. The results on two widely used image-text datasets MSCOCO and Flickr30K show that the matching performance of all base models is significantly improved with remarkably reduced training time. © 2021 IEEE

关键词： Image fusion

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Towards Ordinal Data science

arXiv

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arXiv 2023年

作者： Stumme, Gerd Dürrschnabel, Dominik Hanika, Tom Knowledge & Data Engineering Group Research Center for Information System Design Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Order is one of the main instruments to measure data. In this paper, we will therefore discuss difthe relationship between objects in (empirical) data. ferent means for measuring and ‘calculating’ with However, compared to methods that use numerical ordinal structures — a specific class of directed properties of objects, the amount of ordinal meth- graphs — and show how to infer knowledge from ods developed is rather small. One reason for this is them. Our aim is to establish Ordinal Data science the limited availability of computational resources as a fundamentally new research agenda. Besides in the last century that would have been required cross-fertilization with other cornerstone machine for ordinal computations. Another reason — partic- learning and knowledge representation methods, a ularly important for this line of research — is that broad range of disciplines will benefit from this en-order-based methods are often seen as too mathe- deavor, including, psychology, sociology, economics, matically rigorous for applying them to real-world web science, knowledge engineering, *** Codes 03G10, 06A15, 68T27, 68T30 Copyright © 2023, The Authors. All rights reserved.

关键词： Data science

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Edge AI Algorithm for FBG-Based E-Skin Touch Localization on Embedded Electronics

Edge AI Algorithm for FBG-Based E-Skin Touch Localization on...

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IEEE SENSORS

作者： Elisabetta Leogrande Francesco Dell'Olio Stefano Mazzoleni Calogero Maria Oddo Mariangela Filosa Department of Electrical and Information Engineering Polytechnic University of Bari Italy Department of Excellence in Robotics & AI The BioRobotics Institute Sant' Anna School of Advanced Studies Pisa Italy Interdisciplinary Research Center Health Science Sant’ Anna School of Advanced Studies Pisa Italy

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

The field of collaborative robotics is constantly growing given the central role that safe human-robot interactions are gaining in the current era of Industry 4.0. To act and interact, robotic systems are increasingly being either natively equipped with or adapted to house sensing devices. In this scenario, tactile sensors are fundamental to detect impacts, accurately manipulate objects and enable learning from demonstration strategies. The development of such systems requires technological efforts in terms of both design, for their seamless integration in robots, and software programming, for achieving reliable and prompt reactions to relevant events. In this study, an edge AI algorithm deployed on an embedded device for the recognition of contacts on an FBG (Fiber Bragg Grating)-based e-skin is presented. A Convolutional Neural Network has been trained offline to localize touch events occurred on the e-skin. The performance of the algorithm assessed on the test set reached a mean absolute error on the three cartesian coordinates of less than 4 mm. The algorithm has been then ported to a microcontroller board for real-time testing of both simulated and concurrent contacts. The achievements of this work pave the way to new opportunities for collaborative robotics applications through intelligent FBG-based artificial tactile skins.

关键词： Service robots Robot kinematics Software algorithms Tactile sensors Collaboration Edge AI Fiber gratings Software Software reliability Testing

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Modeling and hierarchical fuzzy control for locomotion control of the quadruped robot

Modeling and hierarchical fuzzy control for locomotion contr...

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IEEE International Conference on robotics and Biomimetics

作者： Jingyu Sun Lelai Zhou Yibin Li Honglin Xu Binghou Geng Center for Robotics School of Control Science and Engineering Shandong University Jinan Shandong China Ministry of Education Engineering Research Center of Intelligent Unmanned System Jinan Shandong China

ISBN: (纸本)9781665481106

In this paper, a stabilised motion method is proposed to control the different motions of a quadruped robot under different commands. By simplifying the motion model of the quadruped robot to a virtual spring damping driven model and analysing the variables affecting the change of the pitch angle of the body attitude in the motion state, the stable motion capability of the quadruped robot in different states is achieved by extracting and changing the variables to be controlled according to the given rules based on the concept of hierarchical fuzzy control. Several experiments are carried out in the simulation software to verify the feasibility of the proposed control method for stable control of the robot motion.

关键词： Robot motion Fuzzy control Analytical models Attitude control Dynamics Prototypes Software

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A Deep Multi-Task Learning Network for Activity Recognition and User Identification Using Smartphone Sensors 16

A Deep Multi-Task Learning Network for Activity Recognition ...

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16th International Conference on ENTERprise Information Systems, centerIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-Than-ideal performance and restricted applicability. This research presents a novel deep multi-Task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-Term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-Task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-The-Art methods. This demonstrates the effectiveness of the multi-Task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks. © 2025 The Author(s).

关键词： Convolutional neural networks

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Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images

arXiv

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arXiv 2025年

作者： Shui, Zhongyi Guo, Ruizhe Li, Honglin Sun, Yuxuan Zhang, Yunlong Zhu, Chenglu Cai, Jiatong Chen, Pingyi Su, Yanzhou Yang, Lin College of Computer Science and Technology Zhejiang University China School of Engineering Westlake University China DAMO Academy Alibaba Group China Center for Interdisciplinary Research and Innovation Muyuan Lab China

Nucleus detection in histopathology whole slide images (WSIs) is crucial for a broad spectrum of clinical applications. Current approaches for nucleus detection in gigapixel WSIs utilize a sliding window methodology, which overlooks boarder contextual information (e.g., tissue structure) and easily leads to inaccurate predictions. To address this problem, recent studies additionally crops a large Filed-of-View (FoV) region around each sliding window to extract contextual features. However, such methods substantially increases the inference latency. In this paper, we propose an effective and efficient context-aware nucleus detection algorithm. Specifically, instead of leveraging large FoV regions, we aggregate contextual clues from off-the-shelf features of historically visited sliding windows. This design greatly reduces computational overhead. Moreover, compared to large FoV regions at a low magnification, the sliding window patches have higher magnification and provide finer-grained tissue details, thereby enhancing the detection accuracy. To further improve the efficiency, we propose a grid pooling technique to compress dense feature maps of each patch into a few contextual tokens. Finally, we craft OCELOT-seg, the first benchmark dedicated to context-aware nucleus instance segmentation. Code, dataset, and model checkpoints will be available at https://***/windygoo/PathContext. © 2025, CC BY-NC-SA.

关键词： Medical imaging

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Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

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(θ,θ^∗)-Operator-Driven Intuitionistic Fuzzy Matrix Composite Operation and Its Optimal Application in Medical Diagnosis 11th

(θ,θ<sup>∗</sup>)-Operator-Driven Intuitionistic Fuzzy Ma...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Kang, Yuqi Feng, Huarong Zhang, Xianyong Yang, Jilin Liang, Qian School of Mathematical Sciences Sichuan Normal University Chengdu 610066 China National-Local Joint Engineering Laboratory of System Credibility Automatic Verification Research Center of Sichuan Normal University Chengdu 610066 China College of Science Xihua University Chengdu 610039 China College of Computer Science Sichuan Normal University Chengdu 610066 China

ISBN: (纸本)9783031927430

Intuitionistic fuzzy sets (IFSs) extend fuzzy sets, and they provide a more accurate representation of uncertainty in realistic scenarios. Fuzzy implication operators play a crucial role in fuzzy reasoning. Accordingly, this paper introduces fuzzy implication operators θ and θ^∗ to IFSs, and it correspondingly proposes two types of intuitionistic fuzzy matrix composite operations, where the (θ, θ^∗) induction is comparatively accompanied with a direct formulation from (∧,∨). Firstly, we investigate concrete properties of fuzzy implication operators θ,θ^∗. Then, two IFSs matrix composite operations are proposed, respectively from (∧,∨) and (θ, θ^∗). Finally, the two composite operations of IFSs matrices are applied to medical diagnosis, and their application feasibility is verified through a practical medical example;by comparative analyses, the (θ,θ^∗)-driven operation better provides right diagnosis results in contrast to previous studies. This study resorts to specific fuzzy implication operators to enrich theoretical research and practical applications of IFSs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fuzzy implication operators Intuitionistic fuzzy sets Medical diagnosis Synthetic composite operation Three-way decision

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Multi-agent Reinforcement Learning Based Delay and Power Optimization for UAV-WMN Substation Inspection

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IEEE Transactions on Network and Service Management 2025年

作者： Tang, Qingwei Sun, Wei Liu, Zhi Xiao, Yang Li, Qiyue Yuan, Xiaohui Zhang, Qian Hefei University of Technology School of Electrical and Automation Engineering Anhui Hefei230009 China Anhui Engineering Technology Research Center of Industrial Automation 193 Tunxi Rd Hefei230009 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan The University of Alabama Department of Computer Science TuscaloosaAL35487 United States University of North Texas Department of Science and Engineering DentonTX76207 United States

Unmanned aerial vehicles (UAV), due to their flexibility and extensive coverage, have gradually become essential for substation inspections. Wireless mesh networks (WMN) provide a scalable and resilient network environment for UAVs, where each node can serve as either an access point or a relay point, thereby enhancing the network’s fault tolerance and overall resilience. However, the UAV-WMN combined system is complex and dynamic, facing the challenge of dynamically adjusting node transmission power to minimize end-to-end (E2E) delay while ensuring channel utilization efficiency. Real-time topology changes, high-dimensional state spaces, and large solution spaces make it difficult for traditional algorithms to guarantee convergence and stability. Generic reinforcement learning (RL) methods also struggle with stable convergence. This paper introduces a new Lyapunov function-based proof to address these issues and provide a stable condition for dynamic control strategies. Then, we developed a specialized neural network power controller and combined it with the MATD3 algorithm, effectively enhancing the system’s convergence and E2E performance. Simulation experiments validate the effectiveness of this method and demonstrate its superior performance in complex scenarios compared to other algorithms. © 2004-2012 IEEE.

关键词： Lyapunov functions

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