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Conference on artificial intelligence and robotics and Asia-Pacific International Symposium

作者： Ali Zareshahi MohammadAli Javadzade Esmaeel Bastami Department of Artificial Intelligence and Robotics Faculty and Research Institute of Artificial Intelligence and Cognitive Sciences Imam Husain (AS) University Tehran Iran Imam Husain (AS) University Tehran Iran

Semantic similarity is a subset of natural language processing in artificial intelligence that examines the similarity between two texts in terms of meaning. It has various applications, such as plagiarism detection, document similarity checks, encyclopedias, question-answering systems, search engines, and chatbots. To address the need for updated semantic similarity models, we used the ParsBERT language model as a base model. It is trained on Persian data and converts tokenized sentences into meaningful vectors. Through pooling layers, optimized vectors are extracted, resulting in 768-dimensional vectors for each sentence. Cosine similarity is then used to calculate the similarity between the two vectors. The results show that the proposed model achieved a Pearson correlation coefficient of approximately 0.82, surpassing previous models. The best coefficient achieved by previous implementations was around 0.77, using a combination of FastText text embeddings and a CNN model. The proposed model’s higher coefficient indicates its superior performance compared to previous models.

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DynamicWorkload-Aware Bike Rebalancing for Bike-Sharing Systems 26

DynamicWorkload-Aware Bike Rebalancing for Bike-Sharing Syst...

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26th European Conference on artificial intelligence, ECAI 2023

作者： Luo, Yuan School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of HongKong Shenzhen518172 China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology China

ISBN: (纸本)9781643684369

Bike Sharing Systems (BSSs) offer a flexible and sustainable transport option that has gained popularity in urban areas globally. However, as users move bikes according to their own needs, imbalanced bike distribution becomes a significant challenge for BSS operators. To address this problem, we propose a Workload Awareness (WA) approach that considers the rebalancing workload of BSS sub-networks and congestion issues when repositioning bikes dynamically. Our algorithm, WA, identifies sub-networks in a BSS and ensures a similar rebalancing load for each sub-network. Our mixed integer nonlinear programming (MINLP) model then finds a repositioning policy for each sub-network, taking into account operator capacity, bike and dock information, and minimizing total losses due to bike shortages and dock congestion. Our experiments on the Ningbo City Bike system demonstrate that our approach outperforms state-of-the-art methods by reducing the loss of the system by up to 60% and significantly reducing computational time by up to 36%. © 2023 The Authors.

关键词： Docks

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Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1716-1729页

作者： Weiliang Zuo Jingmin Xin Changnong Liu Nanning Zheng Akira Sano IEEE the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence the National Engineering Research Center for Visual Information and Applicationsand the Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’an 710049China the Data Center China Construction BankBeijing 100033China the Department of System Design Engineering Keio UniversityYokohama 223-8522Japan

Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still *** paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost *** establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is *** the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.

关键词： Capon beamformer direction-of-arrival(DOA)estimation large-sample mean-squared-error(MSE) subspace-based methods uniform linear array

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Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images

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Data intelligence 2023年第4期5卷 1008-1032页

作者： Abdullahi Umar Ibrahim Ayse Gunnay Kibarer Fadi Al-Turjman Department of Biomedical Engineering Near East UniversityNicosiaMersin 10Turkey Research Center for Al and loT Faculty of EngineeringUniversity of KyreniaMersin 10Turkey Artificial Intelligence Engineering Dept. Al and Robotics InstituteNear East UniversityMersin 1Turkey

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis *** can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis,this method can be tedious for both Microbiologists and Radiologists and can lead to *** challenges can be solved by employing Computer-Aided Detection(CAD)via Al-driven models which learn features based on convolution and result in an output with high *** this paper,we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet *** study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle *** classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 *** classification of tuberculosis using X-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 *** result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.

关键词： Tuberculosis Deep Learning Pretrained AlexNet Chest X-ray Microscopic slide

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Tackling Privacy Heterogeneity in Federated Learning 21

Tackling Privacy Heterogeneity in Federated Learning

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Xu, Ruichen Zhang, Ying-Jun Angela Huang, Jianwei The Department of Information Engineering The Chinese University of Hong Kong Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of HongKong Shenzhen518172 China

ISBN: (纸本)9783903176553

Differentially private federated learning enables clients with privacy concerns to collaboratively train a model while preserving their privacy. Clients’ locally available data and maximum tolerable privacy budgets will affect their contributions to the training performance. To date, existing studies focus on homogeneous privacy budgets and thus there lack systematic studies regarding the impact of clients’ diverse privacy budgets (privacy heterogeneity). This paper represents the first step toward filling this gap. Through rigorous convergence analysis, we illustrate that the influence of privacy protection on training loss is affected by client selection probabilities. In addition, client selection and privacy protection together induce a non-vanishing training error in federated learning. Our analysis then shows that the non-vanishing training error is a convex function of client selection probabilities and thus allows us to formulate privacy-aware client selection as a convex optimization problem. Numerical results demonstrate that the privacy-aware client selection strategy can significantly improve learning performance. For example, compared with unbiased selection, the privacy-aware client selection strategy decreases the test loss by up to 63% on MNIST convolutional neural network classifier. © 2023 IFIP.

关键词： Budget control

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Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors

Person Segmentation and Action Classification for Multi-Chan...

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IEEE/SICE International Symposium on System Integration

作者： Svetlana Seliunina Artem Otelepko Raphael Memmesheimer Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different actions and annotated it. We propose a method based on a MaskDINO model to detect and segment persons and to recognize their actions from combined spherical projected multi-channel representations of the LiDAR data with an additional positional encoding. Our approach demonstrates good performance for the person segmentation task and further performs well for the estimation of the person action states walking, waving, and sitting. An ablation study provides insights about the individual channel contributions for the person segmentation task. The trained models, code and dataset are made publicly available.

关键词： Reflectivity Adaptation models Laser radar Three-dimensional displays System integration Robot sensing systems Encoding Sensor systems Sensors Safety

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Collaborative Inference for MEC Services Based on Multimodal Deep Neural Network

Collaborative Inference for MEC Services Based on Multimodal...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Liu, Yu Wang, Chuxuan Xiao, Yilin Lv, Zefang Xiao, Liang Ji, Xiangyang Xiamen University School of Informatics Xiamen China Xiamen University Dept. of Institute of Artificial Intelligence Xiamen China Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Tsinghua University Department of Automation Beijing China

ISBN: (纸本)9798350345384

Collaborative inference can effectively ease the computational burden imposed on mobile devices by deep neural networks (DNNs), such as employing multimodal DNNs to achieve a comprehensive understanding of the environment. Although reinforcement learning (RL)-based collaborative inference adapts to dynamic environments and enhances inference performance for DNNs, especially those with chain-structured architectures, the policy selection for multimodal DNNs must account for multiple computation-intensive feature encoders. This paper proposes a hierarchical reinforcement learning (RL)-based collaborative inference scheme for multimodal DNNs that concurrently determines the partition point and identifies the appropriate DNN-based feature encoder for each modality within the mobile edge computing system. The hierarchical structure designed for policy selection has alleviated the exponential growth of the modalities. Moreover, the mobile device maintains a confidence score based on probabilities of predefined classes to assess the risk of inference policies that results in low inference accuracy and thus avoids exploring the risk policy. Simulation results demonstrate that our proposed scheme surpasses the Neurosurgeon benchmark in reducing inference latency and energy consumption, all while maintaining a high level of confidence score without significant degradation. © 2023 IEEE.

关键词： Deep neural networks

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HybriMap: Hybrid Clues Utilization for Effective Vectorized HD Map Construction

arXiv

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arXiv 2024年

作者： Zhang, Chi Song, Qi Li, Feifei Chen, Yongquan Huang, Rui The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Constructing vectorized high-definition maps from surround-view cameras has garnered significant attention in recent years. However, the commonly employed multi-stage sequential workflow in prevailing approaches often leads to the loss of early-stage information, particularly in perspective-view features. Usually, such loss is observed as an instance missing or shape mismatching in the final birds-eye-view predictions. To address this concern, we propose a novel approach, namely HybriMap, which effectively exploits clues from hybrid features to ensure the delivery of valuable information. Specifically, we design the Dual Enhancement Module, to enable both explicit integration and implicit modification under the guidance of hybrid features. Additionally, the perspective keypoints are utilized as supervision, further directing the feature enhancement process. Extensive experiments conducted on existing benchmarks have demonstrated the state-of-the-art performance of our proposed approach. © 2024, CC BY.

关键词： Cameras

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SRM-Hair: Single Image Head Mesh Reconstruction via 3D Morphable Hair

arXiv

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arXiv 2025年

作者： Wang, Zidu Zhao, Jiankuo Xu, Miao Zhu, Xiangyu Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China

3D Morphable Models (3DMMs) have played a pivotal role as a fundamental representation or initialization for 3D avatar animation and reconstruction. However, extending 3DMMs to hair remains challenging due to the difficulty of enforcing vertex-level consistent semantic meaning across hair shapes. This paper introduces a novel method, Semantic-consistent Ray Modeling of Hair (SRM-Hair), for making 3D hair morphable and controlled by coefficients. The key contribution lies in semantic-consistent ray modeling, which extracts ordered hair surface vertices and exhibits notable properties such as additivity for hairstyle fusion, adaptability, flipping, and thickness modification. We collect a dataset of over 250 high-fidelity real hair scans paired with 3D face data to serve as a prior for the 3D morphable hair. Based on this, SRM-Hair can reconstruct a hair mesh combined with a 3D head from a single image. Note that SRM-Hair produces an independent hair mesh, facilitating applications in virtual avatar creation, realistic animation, and high-fidelity hair rendering. Both quantitative and qualitative experiments demonstrate that SRM-Hair achieves state-of-the-art performance in 3D mesh reconstruction. Our project is available at https://***/wang-zidu/SRM-Hair. © 2025, CC BY-NC-ND.

关键词： Mesh generation

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A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction

A Self-supervised Contrastive Learning Method for Grasp Outc...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Liu, Chengliang Huang, Binhua Liu, Yiwen Su, Yuanzhe Mai, Ke Zhang, Yupo Yi, Zhengkun Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Siat Branch Shenzhen518055 China

ISBN: (纸本)9798350327182

In this paper, we probe the proficiency of contrastive learning techniques in forecast grasp outcomes, without supervision. Employing a dataset that's open to the public, we establish the effectiveness of contrastive learning techniques in accurately predicting grasp outcomes. More precisely, an impressive accuracy of 81.83% is achieved by the dynamic-dictionary-based method combined with the momentum updating technique, using data from just one tactile sensor, surpassing other unsupervised techniques. Our findings underscore the promise held by contrastive learning techniques in the domain of robotic grasping, while emphasizing the critical role played by precise grasp prediction in securing stable grasps. © 2023 IEEE.

关键词： Forecasting

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