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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yunming Wang, Jianming Ma, Haoran Sun, Yukuan Tiangong University School of Computer Science and Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin China Tiangong University School of Software Tianjin China Tiangong University Center for Engineering Intership and Training Tianjin China

ISBN: (纸本)9798350346558

Recently, deep learning on 3D point cloud data has driven rapid development in the field of autonomous driving. However, processing 3D point clouds is a challenging task. Transformers are widely used in various fields of deep learning. In the community of 3D point clouds, Point Cloud Transformer(PCT, a transformer-based framework) also achieves excellent performance on 3D point cloud tasks. However, the well-known attention mechanism benefits performance improvement while it also leads to expensive computational costs. To alleviate the problem, we propose a novel structure named efficient Point Cloud Classification Network with Position Fusion(EPCP), which uses the strategy of fusing absolute coordinates-based features and relative position information instead of the attention mechanism. By fusing relative position information, our method ensures that the output features maintain different sensitivity to specific areas, which plays a similar role as the attention mechanism. To verify the effectiveness of our method, we conduct experiments on ModelNet40 and real-world ScanObjectNN datasets. Compared with PCT, our method maintains the same accuracy on ModelNet40 and improves by 5.5% on ScanObjectNN. At the same time, our method reduces the number of parameters by 91% and FLOPs by 50% compared to PCT on the ModelNet40 dataset. © 2022 IEEE.

关键词： Classification (of information)

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Adaptive Climbing Strategy of Snake Robot Based on Compliance Control

Adaptive Climbing Strategy of Snake Robot Based on Complianc...

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Intelligent Robotics and Systems (ISoIRS), International Symposium on

作者： Huizhuo Yang Jiyuan Dong Meng Wang Shiyong Meng Lei Yang Jiashuo Xue School of Electronic and Information Engineering Tiangong University Tianjin China Department of Computer Science and Technology Tiangong University Tianjin China Department of Software Engineering Tiangong University Tianjin China

ISBN: (数字)9798350364798

ISBN: (纸本)9798350364804

Snake robots, exhibiting a high degree of redundancy, are a class of biomimetic robots capable of forming a helical rolling gait for climbing trees and pipes. The surface structure of naturally grown trees is irregular, which may hinder the snake robots from achieving optimal surface conformation. Additionally, the presence of numerous branching structures in pipes introduces unpredictability in pipe climbing. To address these challenges, we propose an adaptive climbing strategy for snake robots based on compliant control. By adjusting a pre-set elasticity coefficient, the snake robot modulates the output torque of each joint, thereby altering the target angle of the joint. This adjustment enables the snake robot to adapt to unstructured environments. We conducted an experiment on climbing curved pipes to verify the effectiveness of this method. The result demonstrate that under unknown conditions, the robustness of the snake robot’s climbing motion is enhanced by modifying the pre-set elasticity coefficient based on compliant control. This finding underscores the potential of compliant control in improving the adaptability and performance of Snake robots in complex environments.

关键词： Torque Shape Snake robots Pipelines Redundancy Adaptive algorithms Elasticity

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Transforming Diabetes Care: A Review of IoT-Based Mobile Health Systems 10th

Transforming Diabetes Care: A Review of IoT-Based Mobile He...

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10th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2023, and 4th EAI International Conference on Wearables in Healthcare, HealthWear 2023

作者： Ashfaq, Fiza Ahad, Abdul Hussain, Mudassar Madeira, Filipe Department of Computer Science University of Management and Technology Sialkot51040 Pakistan School of Software Northwestern Polytechnical University Shaanxi Xian China Istanbul34467 Turkey /Pole of Digital Literacy and Social Inclusion Polytechnic Institute of Santarem Santarem2001-904 Portugal Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

ISBN: (纸本)9783031719103

Diabetes, a chronic ailment requiring ongoing care and monitoring, has undergone a healthcare shift thanks to the Internet of Things (IoT). Real-time monitoring, individualized treatment plans, and improved patient-provider communication are all features of IoT-based mobile health solutions. This study explores the components, functionalities, benefits, and disadvantages of IoT-based mobile health systems for the management of diabetes. It investigates wearable sensors, smartphone apps, and linked devices like glucose meter and monitors, placing a focus on their precision, dependability, and usability. The study explores data analytics techniques that allow for customized advice and actions. Patients can effectively control their diabetes with the use of individualized treatments and real-time monitoring. The research also looks into the data analytical methods used in IoT-based mobile health systems for the treatment of diabetes. IoT-based mobile health system issues and implications are also covered. These include issues with data security and privacy, platform and device compatibility, standardization of data formats, and ensuring that all people with diabetes have equal access to technology. Healthcare workers, academics, and policymakers may learn more about the potential of IoT-based mobile health systems to revolutionize diabetes treatment through this thorough assessment. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Electronic health record

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State/Action-based Fairness Verification for Non-determinism

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Chinese Journal of Electronics 2023年第4期20卷 603-606页

作者： XU Guangquan PANG Shanchen GAO Honghao XIAO Yingyuan LI Xiaohong FENG Zhiyong School of Computer Science and Technology Tianjin University Tianjin China Department of Computer Science and Technology Tsinghua University Beijing China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China School of Computer Engineering and Science Shanghai University Shanghai China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

Most of the existing work about fairness verification was carried out by checking the fairness based on state, action, or process respectively. It is hard to resolve the fairness issue for complex software systems, especially in life-critical systems and safety-critical systems. In this paper, we verify the fairness based on both state and action, which is realized mainly by introducing an extended Kripke structure and four kinds of fairness (weak global/local fairness and strong global/local fairness). The experimental results show the superiority of our method by the savings in memory (2.08%) and time consumption (26.45%).

关键词： Memory management software systems

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‘Skimming-Perusal’ Detection: A Simple Object Detection Baseline in GigaPixel-level Images

‘Skimming-Perusal’ Detection: A Simple Object Detection Ba...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhibin Zhang Wanli Xue Kaihua Zhang Shengyong Chen School of Computer Science and Engineering Tianjin University of Technology China School of Computer and Software Nanjing University of Information Science and Technology China

Object detection has achieved amazing performance in regular-sized images, but with the emergence of gigapixel-level images, even the most advanced object detection methods cannot be directly used to process them quickly and efficiently. Therefore, this paper proposes a simple baseline for gigapixel-level images object detection called Skimming-Perusal Detection (SPDet). The SPDet consists mainly of two parts, a skimming model and a perusal model. The skimming model is based on an efficient global-to-local search strategy to detect possible regions containing objects. Non-object regions are merged through a skimming iterative merging strategy to generate skimming patch candidates. The perusal model adaptive scales the skimming patch candidates guided by the coarse detection of the skimming model. Extensive evaluations on the PANDA dataset demonstrate that the SPDet boosts detection speed on gigapixel-level images by 6× while achieving better performance than a variety of state-of-the-art methods. The source code is released at https://***/TJUT-CV/SPDet.

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A Concept Drift Tolerant Abnormal Detection Method for Network Traffic

A Concept Drift Tolerant Abnormal Detection Method for Netwo...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Xu Wang Li Han Shimin Sun Guowei Xu School of computer science and engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China School of software Tiangong University Tianjin China

we propose a cutting-edge solution that leverages passive adaptive methods based on ensemble learning to effectively detect anomalous traffic in data streams. Our approach tackles the issue of concept drift by integrating various deep learning models, each offering a unique perspective to learn data features. By doing so, our solution can accurately capture the distribution of data concepts and propose weight layers to make the model adaptable to changes caused by concept drift. In a series of experiments conducted on public datasets, our model demonstrated remarkable effectiveness in detecting anomalies, achieving accuracy rates of over 97% after adapting to concept drift.

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Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation

Incremental Nuclei Segmentation from Histopathological Image...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Huyong Wang Huisi Wu Jing Qin College of Computer Science and Software Engineering Shenzhen University Centre for Smart Health School of Nursing The Hong Kong Polytechnic University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present a novel semantic segmentation approach for incremental nuclei segmentation from histopathological images, which is a very challenging task as we have to in-crementally optimize existing models to make them perform well in both old and new classes without using training samples of old classes. Yet, it is an indispensable component of computer-aided diagnosis systems. The proposed approach has two key techniques. First, we propose a new future-class awareness mechanism by separating some potential regions for future classes from background based on their similari-ties to both old and new classes in the representation space. With this mechanism, we can not only reserve more parameter space for future updates but also enhance the repre-sentation capability of learned features. We further propose an innovative compatibility-inspired distillation scheme to make our model take full advantage of the knowledge learned by the old model. We conducted extensive experiments on two famous histopathological datasets and the results demonstrate the proposed approach achieves much better performance than state-of-the-art approaches. The code is available at https://***/why199911/nSeg.

关键词： Training computer vision Codes Computational modeling Semantic segmentation software Pattern recognition

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ExtRe: Extended Temporal-Spatial Network for Consumer-Electronic WiFi-Based Human Activity Recognition

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Wang, Peiliang Huang, Huakun Zhao, Lingjun Zhu, Beibei Huang, Huawei Wu, Huijun School of Computer Science and Cyber Engineering Guangzhou University China School of Electronics and Information Guangdong Polytechnic Normal University China School of Software Engineering Sun Yat-sen University China School of Civil Engineering Guangzhou University China

With consumer electronics (CE) development, consumer-electronic WiFi-based human activity recognition (HAR) has been acknowledged as an essential non-intrusive technology in several crucial human-oriented fields, such as metaverse accessing, sign language communication, and healthcare *** learning from the experience from the CV field, i.e., treating wireless signals as images and extracting the feature based on a 2-dimensional convolutional neural network (2D CNN), existing studies have achieved considerable ***, there is a critical difference in feature compositions between WiFi signals and images. Whether existing methods apply to coarse-grained or fine-grained activity recognition, their performance is limited due to the loss of important dynamic temporal features hidden among multiple *** overcome these problems, we propose an extended temporal-spatial approach for WiFi-based HAR, named ExtRe, in which sufficient attention is paid to both temporal and spatial-channel characteristics. We also considered a challenging case, i.e., coarse-grained or fine-grained *** results based on six datasets show that, compared with the state-of-the-art methods, our ExtRe achieves superior performance. The proposed ExtRe achieves 100% accuracy on a fine-grained dataset. On the coarse-grained dataset, ExtRe improves the accuracy by 0.6% with high *** addition, ExtRe has 20.67% less floating point operations (FLOPs). IEEE

关键词： E-learning

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Addressing asynchronicity in clinical multimodal fusion via individualized chest X-ray generation 24

Addressing asynchronicity in clinical multimodal fusion via ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Wenfang Yao Chen Liu Kejing Yin William K. Cheung Jing Qin School of Nursing The Hong Kong Polytechnic University School of Nursing The Hong Kong Polytechnic University and School of Software Engineering South China University of Technology Department of Computer Science Hong Kong Baptist University

ISBN: (纸本)9798331314385

Integrating multi-modal clinical data, such as electronic health records (EHR) and chest X-ray images (CXR), is particularly beneficial for clinical prediction tasks. However, in a temporal setting, multi-modal data are often inherently asynchronous. EHR can be continuously collected but CXR is generally taken with a much longer interval due to its high cost and radiation dose. When clinical prediction is needed, the last available CXR image might have been outdated, leading to suboptimal predictions. To address this challenge, we propose DDL-CXR, a method that dynamically generates an up-to-date latent representation of the individualized CXR images. Our approach leverages latent diffusion models for patient-specific generation strategically conditioned on a previous CXR image and EHR time series, providing information regarding anatomical structures and disease progressions, respectively. In this way, the interaction across modalities could be better captured by the latent CXR generation process, ultimately improving the prediction performance. Experiments using MIMIC datasets show that the proposed model could effectively address asynchronicity in multimodal fusion and consistently outperform existing methods.

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PH-Net: Semi-Supervised Breast Lesion Segmentation via Patch-Wise Hardness

PH-Net: Semi-Supervised Breast Lesion Segmentation via Patch...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Siyao Jiang Huisi Wu Junyang Chen Qin Zhang Jing Qin College of Computer Science and Software Engineering Shenzhen University Centre for Smart Health School of Nursing The Hong Kong Polytechnic University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present a novel semi-supervised framework for breast ultrasound (BUS) image segmentation, which is a very challenging task owing to (1) large scale and shape variations of breast lesions and (2) extremely ambiguous boundaries caused by massive speckle noise and artifacts in BUS images. While existing models achieved certain progress in this task, we believe the main bottleneck nowadays for further improvement is that we still cannot deal with hard cases well. Our framework aims to break through this bottleneck, which includes two innovative components: an adaptive patch augmentation scheme and a hard-patch contrastive learning module. We first identify hard patches by computing the average entropy of each patch and then shield hard patches to prevent them from being cropped out while performing random patch cutmix. Such a scheme is able to prevent hard regions from being inadequately trained under strong augmentation. We further develop a new hard-patch contrastive learning algorithm to direct model attention to hard regions by applying extra contrast to pixels in hard patches, further improving segmentation performance on hard cases. We demonstrate the superior-ity of our framework to state-of-the-art approaches on two famous BUS datasets, achieving better performance under different labeling conditions. The code is available at https://***/jjjsyyy/PH-Net.

关键词： Training Image segmentation Adaptation models Ultrasonic imaging Shape Perturbation methods Contrastive learning

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