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Propelling Pedagogical Paradigms: A Study of Advanced Human-computer Interaction Learning in Data Structure and Algorithms Based on Virtual Reality technology 18th

Propelling Pedagogical Paradigms: A Study of Advanced Human-...

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18th International Conference on computer Science and Education, ICCSE 2023

作者： Yahui, Yang Yanxia, Gao Ling, Peng Yingxi, Xiang Huan, He Yiwen, Liu School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China School of Fine Arts and Design Huaihua University Hunan Huaihua418000 China

ISBN: (纸本)9789819707362

Data Structure and Algorithms (DSA) is a course with strong practice and abstract theoretical knowledge. In view of the complexity of the knowledge points of the course and the difficulty of understanding, we design and implement a virtual simulation experiment teaching system of the course--Virtual simulation experiment platform. In this platform, DSA through simulation practice into animation effects, and combined with the unity engine and C# code to achieve user interaction, to provide users with a way to intuitively understand the principles of DSA, while increasing the user’s subjective initiative to learn, and to achieve the high level, Innovative and challenging teaching classroom, so that students in colleges and universities can feel relaxed and happy in the process of learning this course. What is more, they can better accept and understand DSA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data structures

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Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net

Semantic-Preserving Abstractive Text Summarization with Siam...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Sheng, Xin Xu, Linli Xu, Yinlong Jiang, Deqiang Ren, Bo School of Computer Science and Technology University of Science and Technology of China. China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China IFLYTEK Co. Ltd. China Tencent Youtu Lab China

ISBN: (纸本)9781955917766

We propose a novel siamese generative adversarial net for abstractive text summarization (SSPGAN), which can preserve the main semantics of the source text. Different from previous generative adversarial net based methods, SSPGAN is equipped with a siamese semanticpreserving discriminator, which can not only be trained to discriminate the machine-generated summaries from the human-summarized ones, but also ensure the semantic consistency between the source text and target summary. As a consequence of the min-max game between the generator and the siamese semantic-preserving discriminator, the generator can generate a summary that conveys the key content of the source text more accurately. Extensive experiments on several text summarization benchmarks in different languages demonstrate the effectiveness of the proposed method. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Semantics

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A 7nm-Based Decodable Self-Resetting Regfile Circuit 15

A 7nm-Based Decodable Self-Resetting Regfile Circuit

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Zhao, Wanlong Zhang, Yuejun Ren, Mingze Wen, Liang Wang, Pengjun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China China Coast Guard Academy Department of Electronic Technology Ningbo China Wenzhou University College of Electrical and Electronic Engineering Wenzhou325035 China

ISBN: (纸本)9798350312980

This paper presents a novel design scheme for a decodable self-resetting Regfile circuit that aims to reduce the power consumption caused by massive data and high computing power. The scheme consists of three steps: first, it analyzes the working principle and the energy consumption process of the decoding circuit;second, it proposes a self-resetting decoding circuit structure that uses the clock to control the decoding state and automatically resets when the clock is low, thus avoiding the extra power consumption due to the inversion of the memory cell state during the non-working period of the decoding circuit;third, it implements a two-read, two-write, decodable self-resetting Regfile circuit using a full-custom design method under the TSMC 7nm FinFET process. The experimental results demonstrate that the circuit has an area of 5593μm2 and an energy-delay product of 0.41pJ•nS, and it can operate stably at 4GHz under various temperatures and process corner conditions. © 2023 IEEE.

关键词： 7nm process high speed low power consumption Regfile circuit

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Computing Flow Experience Based on Physiological Signals: A Systematic Literature Review

Computing Flow Experience Based on Physiological Signals: A ...

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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： Deep learning Flow experience Flow model Machine learning Physiological signals

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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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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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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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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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