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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Hong, Yichen Zhou, Chao Liu, Zelu Zifeng, Ni Liu, Juan Li, Xiangxian Yang, Chenglei Bian, Yulong Shandong University Weihai China Human Computer Interaction Technology and Intelligent Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China Shandong University School of Mechanical Electrical & Information Engineering Weihai China Shandong University Jinan China School of Software Shandong University Jinan China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Shandong University Shandong Weihai China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application China

ISBN: (纸本)9798400713958

Flow experience is a key user experience in human-computer interaction (HCI). Compared with traditional subjective measurements, physiological signal-based methods (i.e. physiological computation of flow experience) can measure flow experience in a more objective and real-time way without interrupting the ongoing activity of the user. Despite scattered studies, a systematic review of research in this field is absent. Synthesizing key aspects of this field, including the conceptualization of flow experience, prevalent theoretical models, foundational research steps for experiment, computerized tasks for inducing flow, commonly used physiological signals, methods for analyzing flow state, and application in HCI. Finally, we discuss the potential of this field to propel HCI and user experience evaluation technologies, while projecting future research avenues. This paper is expected to help researchers grasp existing work in physiological signal-based flow computing and inspire subsequent explorations. © 2025 Copyright held by the owner/author(s).

关键词： Flow simulation

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A Novel Approach for Concrete Crack and Spall Detection Based on Improved YOLOv8 23

A Novel Approach for Concrete Crack and Spall Detection Base...

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Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Md Abdur Rouf Yuji Iwahori Haisong Chen Aili Wang Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology China Department of Computer Science Chubu University Japan Shenzhen Polytechnic University China

ISBN: (纸本)9798400709272

The detection and prevention of concrete cracks and spalls is crucial to ensure the structural integrity and longevity of civil infrastructure. In this research paper, we propose a novel method for concrete crack and spall detection based on YOLOv8 with ByteTrack and supervision. The proposed method exploits the advantages of the YOLOv8 object detection framework, which provides real-time and accurate detection, tracking and counting of various objects. By integrating ByteTrack, a state-of-the-art tracking algorithm, we increase system performance and robustness in tracking cracks and spalls over time. Furthermore, supervision is employed to improve detection accuracy through iterative training and fine-tuning. To train the model, an extensive dataset of concrete crack and spall images is collected, annotated, and enhanced. The dataset includes a variety of scenarios, lighting conditions, and crack/spill sizes, which ensures the model's ability to generalize to real-world situations. Transfer learning is used to support the YOLOv8 backbone with pre-trained weights, to accelerate the convergence of the training process. Experimental evaluation is conducted on a benchmark dataset, and the proposed method outperforms existing techniques in terms of accuracy, precision, and recall. YOLOv8 with ByteTrack and Supervision achieves an overall accuracy of 94% detection rate for concrete cracks and spalls, even under challenging conditions. Real-time deployment is achieved on a high-performance computing platform, allowing efficient and timely monitoring of concrete structures. The proposed method demonstrates its potential as a valuable tool for infrastructure management and maintenance, enabling early detection of concrete cracks and spalls. By promptly identifying such defects, necessary repair and reinforcement measures can be implemented, preventing further deterioration and ensuring the safety and longevity of civil infrastructure. The high detection performance of the

关键词： ByteTrack & Supervision Concrete crack and spall detection Tracking and counting YOLOv8

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Emsd-detr:efficient small object detection for UAV aerial images based on enhanced RT-DETR model

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The Journal of Supercomputing 2025年第9期81卷 1-33页

作者： Zhang, Chaofeng Yang, Jingmin School of Computer Science Minnan Normal University Zhangzhou China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou China Department of Electronic Engineering National Taipei University of Technology Taipei China

UAV aerial image object detection studies are affected by imbalances in large fields of view, changes in scale and viewing angle, object aggregation occlusion, and changes in lighting and weather conditions, resulting in poor performance of small object detection algorithms. In this paper, we propose an Efficient Multi-scale Drone DETR (EMSD-DETR), an object detection model for UAV aerial images that aims to overcome specific challenges. To mitigate the loss of information about small objects due to the increase in the number of layers of the convolutional neural networks, the FRE-Block is proposed. Furthermore, we propose a small object sensitive pyramid for cross-scale feature fusion that effectively fuses local spatial coordinate information with global contextual information to avoid missing details of different feature layers. Finally, the Focaler-Shape-IoU is introduced that incorporates consideration of the shape and scale of the bounding box to expedite convergence and enhance detection precision for difficult samples. Experiments on the VisDrone dataset demonstrate that the EMSD-DETR model improves 1.8%, 2.9%, 2.7%, and 1.2% with regard to precision, recall, mAP $$_{50}$$ , and mAP $$_{50:95}$$ , respectively, while decreasing the number of parameters by 8.2%, as compared to RT-DETR. In addition, generalizability experiments on the TinyPerson and NWPU VHR-10 dataset prove the validity of the EMSD-DETR model.

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Multi-Modal Instrument Recognition Method Based on Improved YOLOv5s and ESPNet

Multi-Modal Instrument Recognition Method Based on Improved ...

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Shixian Shen Jianguo Wang Yong Feng Yingna Li Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China China Copper Industry Co. Ltd Kunming China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Addressing the challenges of high cost, low accuracy, and poor real-time performance in anomaly detection within industrial production processes, this study proposes a multi-modal instrument recognition method based on improved YOLOv5s and ESPNet. Specifically, a dynamic non-monotonic focusing mechanism is introduced into the YOLOv5s object detection model, enhancing its ability to accurately detect the position of pointer-type instruments. Simultaneously, affine and perspective transformations are employed to rectify skewed and rotated images. Secondly, variable convolutions are integrated into the ESPNet segmentation network to adaptively capture nonlinear deformation elements of pointers and dials in images, thereby extracting comprehensive key information from both. Subsequently, a Hough transform operation is applied to fit the straight line representing the pointer, while contour tracking methods are utilized to extract the valid range of dial data. Based on linear proportional relationships, the reading of pointer-type instruments is computed. Finally, a comprehensive model is built by integrating image-acquired instrument data with corresponding sensor data, enabling anomaly detection from a multimodal data analysis perspective. Experimental results demonstrate an identification accuracy of 96.21% for pointer-type instruments, with an average detection speed of 0.216 seconds and a data anomaly detection rate of 99.43%. This method accurately and rapidly identifies data anomalies, meeting the demands for key indicator monitoring in industrial production.

关键词： YOLO Industries Analytical models Image segmentation Accuracy Instruments Focusing Data models Anomaly detection Monitoring

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Classification of Thoracic Bone Scintigraphic Images Using ResNet with Attention Modules 2

Classification of Thoracic Bone Scintigraphic Images Using R...

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2nd International Conference on Signal Image Processing and Communication, ICSIPC 2022

作者： Cao, Xu Cao, Yongchun Lin, Qiang Man, Zhengxing Wang, Yubo Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University China School of Mathematics and Computer Science Northwest Minzu University China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University China

ISBN: (纸本)9781510657694

Whole-body bone scan is an effective tool for diagnosing bone metastasis from malignant tumors, including lung cancer. However, due to the low spatial resolution of Scintigraphic images, manual analysis of the images is very difficult. In order to accurately diagnose bone metastases, a self-defined convolutional neural network is studied and constructed in this paper for detecting lung cancer-Caused skeletal metastasis in bone scan images Firstly, since thoracic regions such as ribs and spine are the most frequent areas of bone metastases, we preprocessed the collected SPECT whole-body bone imaging data to extract the thoracic regions. Then based on the feature that ResNet can alleviate the vanishing gradient problem caused by increasing depth in deep neural networks and the feature that attention can focus on important information to improve network expression. A deep SPECT image classification network Att-ResNet24 with residual module and hybrid attention mechanism is developed for classification of these images, including bone metastases and non-bone metastases. Experiments on a set of real SPETC whole-body bone images show that self-defined classification networks can be effectively used for the detection of bone metastases, obtaining the best scores 0.737, 0.744, 0.736, and 0.735 for accuracy, precision, recall, and F-1 score, respectively. © 2022 SPIE.

关键词： Image classification

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Interactive Intelligent 3D Surveying Vehicle Based on Deep Learning 23

Interactive Intelligent 3D Surveying Vehicle Based on Deep L...

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Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Zeyu Hu Haisong Chen Jingfang Wei Yunyi Zuo Kaiyang Xu Yuji Iwahori Haibin Wu Aili Wang Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology China Shenzhen Polytechnic University China Department of Computer Science Chubu University Japan

ISBN: (纸本)9798400709272

With the rapid development of artificial intelligence, the emergence of intelligent vehicles has brought disruptive changes to traditional survey technology. In response to the limitations of geological environmental factors on the scope and search progress of field exploration, this article utilizes a modular design concept to develop a system structure framework and specific solutions for each functional module of a multi-functional intelligent survey vehicle. An intelligent control platform based on the Nvidia Jetson TX2 and ROS systems has been built, and software programs for eye movement control and 3D dense reconstruction have been written. A system that can assist motion control through line of sight has been designed, A small, low-cost intelligent survey vehicle that can simultaneously perform real-time scene 3D reconstruction. This survey vehicle combines IoT technology, and on the basis of traditional survey vehicle functions, it has functions such as eye tracking, scene 3D reconstruction, remote object detection, etc. The obtained information is transmitted back to the PC through remote communication for users to view the survey results in real-time. At the same time, the operation of the survey vehicle can be controlled according to the direction of the line of sight, providing intelligent interaction ability and multi-dimensional exploration results for exploration work.

关键词： 3D dense reconstruction Eye tracking control motion Intelligent survey vehicle SLAM YOLOv5

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Multi-disease classification of whole-body scintigraphy images based on deep learning 7

Multi-disease classification of whole-body scintigraphy imag...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Liu, Liangxia Cao, Yongchun Lin, Qiang Man, Zhengxing Wang, Weiqiong Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Northwest Minzu University China School of Mathematics and Computer Science Northwest Minzu University China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University China

ISBN: (纸本)9781665478571

Bone scan images has important clinical application value for the early diagnosis and treatment of skeletal lesions in the whole-body. In order to realize classification of multiple diseases (i.e., normal, bone metastases, arthritis, and thyroid cancer) in whole-body bone scintigraphy images, In this paper, the multi-disease classification method based on convolutional neural network was studied on bone scan images, and a self-defined classification model was proposed, which was named SPNT9. The model consists of nine convolutional layers, five pooling layers, two fully connected layers and one softmax layer. Finally, the model was validated with real SPECT images collected. By comparing with VGGNet, ResNet and DenseNet models, The SPNT9 model obtained the best classification performance in the test samples, with Accuracy, Precision, Recall and F-1 of 0.8814, 0.7806, 0.7900 and 0.7899, respectively. The results show that the proposed model can be used to accurately classify multiple diseases in SPECT images. © 2022 IEEE.

关键词： Convolutional neural networks

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Link Prediction with Mixed Structure Attribute of Network 8th

Link Prediction with Mixed Structure Attribute of Network

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Tang, Minghu Key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission Qinghai Minzu University Xining810007 China School of Computer Science Qinghai Minzu University Xining810007 China

ISBN: (纸本)9783031067600

Link prediction aim is to use known information of network to infer missing edges, identify spurious interactions, evaluate network evolving mechanisms, and so on. Currently, with the development of deep learning technology, many neural network-based link prediction algorithms have emerged. However, these existing algorithms, due to the introduction of many parameters, are too computationally expensive to efficiently process large-scale network data. In fact, based on the belief that network nodes with a great number of common neighbors are more likely to be connected, many similarity indices have achieved considerable accuracy and efficiency. Moreover, the method based on structural similarity, simple and applicable, low computing cost, and high prediction accuracy, can quickly process large-scale data. Inspired by the idea, in this paper through the analysis of the network structure index shows that the clustering and assortative coefficients can reflect the similarity between nodes. To this end, this paper integrates the two to form a new link prediction algorithm based on the overall characteristics of the network structure, which realizes the fast and efficient prediction of the missing links in the network. Experiments on 10 real-world networks show that the method is highly accurate and robust compared with baseline. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Speech Enhancement with Fractional Fourier Transform 21

Speech Enhancement with Fractional Fourier Transform

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21st International Symposium on Communications and Information Technologies, ISCIT 2022

作者： Zhu, Cun Sun, Yan Pan, Chunhua Key Laboratory of Artificial Intelligence Qinghai Minzu University Application Technology State Ethnic Affairs Commission Xining810007 China School of Computer of Qinghai Normal University Qinghai Xining810003 China

ISBN: (数字)9781665498517

ISBN: (纸本)9781665498517

The Fast Discrete Fourier Transform is commonly analysis method for speech signal processing in frequency domain. It has the problem of adjusting window size for desired resolution. But the Fractional Fourier Transform can have both time domain and frequency domain processing capabilities. This paper performs global processing by combining Fractional Fourier Transform and wavelet multi-scale local elimination on the speech signal and enhancement experiment with RNN neural network. The experimental results show that the speech signal is processed both locally by wavelet and globally by Fractional Fourier Transform. The combination of the two allows the speech signal to take into account both local and global information in the time and frequency domain. © 2022 IEEE.

关键词： Speech enhancement

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A new spaceborne/airborne bistatic synthetic aperture radar time synchronization method based on fractional Fourier transform 5

A new spaceborne/airborne bistatic synthetic aperture radar ...

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2023 5th International Conference on Information Science, Electrical, and Automation Engineering, ISEAE 2023

作者： Zhou, Fang Gui, Changchun Hu, Xinxiao Xing, Mengdao Zhang, Jiajia School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Electronic Engineering Xidian University Xi'an710071 China Key Laboratory of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei230088 China

ISBN: (纸本)9781510667440

With the change of observation demands, traditional monostatic synthetic aperture radar(SAR) can no longer meet the requirements such as passive reception and cooperative multimode imaging. Spaceborne/Airborne hybrid bistatic SAR(SA-BSAR) emerges as the times require, and has been widely concerned with its flexibility, concealment. However, as the receiver and transmitter do not share the same frequency oscillation source, the impact of time error on imaging cannot be ignored as the satellite operates for a long time. At the same time, SA-BSAR is in the form of double root sign in the course of slant range. How to obtain two-dimensional(2-D) spectrum is also the key to imaging. This paper proposes a new method to estimate time error based on fractional Fourier transform (FRFT), which simplifies the traditional time error estimation steps. Finally, the Air-Phase method is used to solve the 2-D spectrum to achieve the target imaging. The algorithms proposed in this paper are implemented by FFT without interpolation, which ensures the imaging efficiency. The final experimental results show the effectiveness of the algorithm. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synthetic aperture radar

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