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A Preliminary Study on University computer Basic Curriculum Reform Aimed at Cultivating Computational Thinking from the Perspective of Smart Education 18th

A Preliminary Study on University Computer Basic Curriculum ...

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

作者： Yu, Niefang Liu, Yiwen Peng, Xiaoning Li, Xiaomei Lu, Youmin 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

ISBN: (纸本)9789819707904

With the rapid advancement of information technology such as artificial intelligence, cloud computing, big data, virtual reality, etc., in the context of the deep integration of information technology and education and teaching, smart classroom has set off a new round of revolution. Based on this background, this paper discuss how to cultivate students’ computational thinking in a smart education environment, and explore the cultivation of computational thinking in basic university computer courses from three aspects: before class, during class, and after class in smart classrooms. In the background of smart education, only by comprehensively cultivating students’ computational thinking that we can achieve more results in the future. Thereby improving students’ ability to comprehensively apply computer technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence

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Towards robust data augmentation for sequence recommendation 16

Towards robust data augmentation for sequence recommendation

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16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Chen, Yan Zhao, Sunhang Wang, Xinyuan Jin, Zhengxuan Lin, Zekai Wang, Bin The Third Research Institute of the Ministry of Public Security Shanghai China College of Letters and Science United States Zhejiang Gongshang University Computer Science and Technology China Binjiang Institute of Zhejiang University China Zhengzhou University School of Computer and Artificial Intelligence China Zhejiang Key Laboratory of Multi-Dimensional Perception Technology Application and Cybersecurity China

ISBN: (纸本)9798331506896

This paper presents RAS-Rec, a novel and robust approach to sequence recommendation that leverages the power of data augmentation techniques. RAS-Rec proposes data augmentation as a means to enhance the representations of users and items in sequence recommendation. By generating synthetic training samples through various transformations applied to the original data, RAS-Rec increases the diversity and quantity of the training set. The augmented sequence data addresses the limitations of existing methods and improves the robustness and generalization capabilities of sequence recommendation models. Additionally, a contrastive learning scheme is designed to train and further enhance the representations in RAS-Rec. Extensive experiments validate the effectiveness of the proposed approach, demonstrating its superiority over existing methods in terms of recommendation performance. The results highlight the value of data augmentation and contrastive learning in sequence recommendation tasks. The findings of this study contribute to the advancement of robust sequence recommendation techniques. © 2024 IEEE.

关键词： Contrastive Learning

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Collaborative List-and-Pairwise Filtering from Implicit Feedback : Extended Abstract 39

Collaborative List-and-Pairwise Filtering from Implicit Feed...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Yu, Runlong Liu, Qi Ye, Yuyang Cheng, Mingyue Chen, Enhong Ma, Jianhui University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology Hefei China Rutgers University Management Science and Information Systems Rutgers Business School Newark United States

ISBN: (纸本)9798350322279

Collaborative filtering (CF) from implicit datasets has attracted much attention in recent years. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proven to be helpful to exploit implicit feedback, but lead to not addressing the rank-biased scenarios where positive items are supposed to be placed on the top-k positions. Although there exist listwise methods, they have low efficiency and are not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely Collaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we first smooth a well-known rank-biased measure called Mean Average Precision (MAP) as a low-bound version to make it can be optimized. After that, we combined the objective functions of optimizing the MAP with pairwise comparisons. The CLAPF framework is a new hybrid model that provides the idea of utilizing a listwise measure in a pairwise way on implicit feedback. © 2023 IEEE.

关键词： Collaborative filtering

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Stereo Image Dense Matching Based on SGM Constrained by Feature Matching 9

Stereo Image Dense Matching Based on SGM Constrained by Feat...

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9th International Conference on computer and Communications, ICCC 2023

作者： Ma, Tao Zhu, Hangbiao Huang, Weijian An, Pei Wang, Chun Yu, Kun Institute of Computer Application China Academy of Engineering Physics Mianyang China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China National Key Laboratory of Science and Technology on Electromagnetic Energy Naval University of Engineering Wuhan China

ISBN: (纸本)9798350317251

Dense image matching plays a important role in stereo vision and remote sensing. Semi-global matching (SGM) is a widely used dense image matching framwork, which uses dynamic programming calculation strategy to optimize the global energy function. SGM has the problem of wrong disparity accumulation along the path. To solve the problem, this paper proposes a new dense image matching method based on feature matching and SGM. First, the feature matching is used to extract the reliable reference points. And then, reference points are used as additional constraints in matching cost aggregation to cut off the wrong disparity delivering along the path. In order to evaluate our method, qualitative and quantitative experiments are carried out on three stereo image pairs. The experimental results show that our method can achieve high matching accuracy by effectively reducing the error propagation. © 2023 IEEE.

关键词： Stereo vision

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Imaging Detection and Classification for Near-space Dim Targets 7

Imaging Detection and Classification for Near-space Dim Targ...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

The motion mode of near-space targets is complex due to their high threat level. The target imaging faces low SNR and susceptibility to background noise. The existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. The algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. The improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. The outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © The Institution of Engineering & technology 2023.

关键词： Extraction

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TUT4CRS: Time-aware User-preference Tracking for Conversational Recommendation System 24

TUT4CRS: Time-aware User-preference Tracking for Conversatio...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Dongxiao Zhang, Jinghan Wang, Xiaobao Ge, Meng Feng, Zhiyong Wang, Longbiao Ma, Xiaoke Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Saw Swee Hock School of Public Health National University of Singapore Singapore Singapore School of Computer Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798400706868

The Conversational Recommendation System (CRS) aims to capture user dynamic preferences and provide item recommendations based on multi-turn conversations. However, effectively modeling these dynamic preferences faces challenges due to conversational limitations, which mainly manifests as limited turns in a conversation (quantity aspect) and low compliance with queries (quality aspect). Previous studies often address these challenges in isolation, overlooking their interconnected nature. The fundamental issue underlying both problems lies in the potential abrupt changes in user preferences, to which CRS may not respond promptly. We acknowledge that user preferences are influenced by temporal factors, serving as a bridge between conversation quantity and quality. Therefore, we propose a more comprehensive CRS framework called Time-aware User-preference Tracking for Conversational Recommendation System (TUT4CRS), leveraging time dynamics to tackle both issues simultaneously. Specifically, we construct a global time interaction graph to incorporate rich external information and establish a local time-aware weight graph based on this information to adeptly select queries and effectively model user dynamic preferences. Extensive experiments on two real-world datasets validate that TUT4CRS can significantly improve recommendation performance while reducing the number of conversation turns. © 2024 ACM.

关键词： Graph neural networks

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Prediction of GDP Carbon Emission Based on Grey Model and Neural Network 4th

Prediction of GDP Carbon Emission Based on Grey Model and Ne...

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4th EAI International Conference on Robotic Sensor Networks, 2020

作者： Zhang, Feng Zhao, Huihuang Ramasamy, Manimaran College of Computer Science and Technology Hengyang Normal University Hengyang China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China

ISBN: (纸本)9783030704506

In order to improve the prediction accuracy of Gross Domestic Product (GDP) carbon emission, combining the advantages of grey prediction and neural network, a discrete grey neural network prediction model named DGMBP is established. This model not only has the advantages of less data sample and larger development coefficient range of GM 1,1 model but also combines the advantages of neural network to deal with nonlinear data sample. Taking the GDP carbon emission data of China, India, the United States and the European Union as an example, the prediction results obtained are stable, and the accuracy of the prediction data is over 95%, which is much higher than that obtained by the linear regression equation and the logical regression equation. Finally, the carbon emission of GDP in 2020–2030 is predicted by using this model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Efficient Restoration for Binary Neural Networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2025年第2期53卷 568-580页

作者： Zeng, Kai Wan, Zi-Xin Wang, Ming-Tao Shen, Tao Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Yunnan Kunming650500 China

Restoring the weight distribution, activation distribution, and gradient to the original full precision network data as much as possible can greatly improve the inference ability of the binary network. However, existing methods directly apply the restoration operation in forward propagation to binary data, and the gradient approximation functions for back-propagation are fixed or manually determined, resulting in the need for improvement in the restoration efficiency of binary networks. To address this problem, the efficient restoration method is investigated for binary neural networks. Firstly, a distribution recovery method for maximizing information entropy is proposed. By shifting the original full precision weight mean and scaling the modulus, the quantized binary weight directly has the characteristic of maximum distribution restoration. At the same time, a simple statistical translation and scaling factor is used to greatly improve the restoration efficiency of weight and activation. Furthermore, it is proposed a gradient function based on adaptive distribution approximation, which dynamically determines the update range of the current gradient in the P-percentile according to the actual distribution of the current full precision data. It adaptively changes the shape of the approximation function to efficiently update the gradient during the training process, thereby improving the convergence ability of the model. On the premise of ensuring the improvement of execution efficiency, theoretical analysis has confirmed that the method proposed in this paper can achieve maximum restoration of binary data. Compared with the existing advanced binary network models, the experimental results of our method show excellent performance, with a 60% and 67% reduction in computational time for the distribution restoration operation quantization of ResNet-18 and ResNet-20, respectively. An accuracy of 93.0% is achieved for VGG-Small binary quantization on the CIFAR-10 dataset

关键词： Restoration

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Uncovering emotion sequence patterns in different interaction groups using deep learning and sequential pattern mining

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Journal of computer Assisted Learning 2024年第4期40卷 1777-1790页

作者： Huang, Changqin Yu, Jianhui Wu, Fei Wang, Yi Chen, Nian-Shing Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China School of Education Zhejiang International Studies University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Institute for Research Excellence in Learning Sciences Program of Learning Sciences National Taiwan Normal University Taipei Taiwan

Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi-directional long short-term memory model for emotion detection yielded the highest predictive performance, with an impressive F-measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low-level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups

关键词： deep learning emotion sequence patterns learning analytics learning behaviour sequential pattern mining

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Design and Implementation of Intelligent Truck Based on Azure Kinect 1

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7th EAI International Conference on Machine Learning and Intelligent Communication, MLICOM 2022

作者： Wei, Jingfang Xu, Kaiyang Hu, Zilin Iwahori, Yuji Wu, Haibin Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin150080 China City University of Hong Kong 610200 Hong Kong Computer Science Chubu University Aichi Kasugai487-8501 Japan

ISBN: (数字)9783031302374

ISBN: (纸本)9783031302367

In recent years, due to the impact of COVID-19, the market prospect of non-contact handling has improved and the development potential is huge. This paper designs an intelligent truck based on Azure Kinect, which can save manpower and improve efficiency, and greatly reduce the infection risk of medical staff and community workers. The target object is visually recognized by Azure Kinect to obtain the center of mass of the target, and the GPS and Kalman filter are used to achieve accurate positioning. The 4-DOF robot arm is selected to grasp and transport the target object, so as to complete the non-contact handling work. In this paper, different shapes of objects are tested. The experiment shows that the system can accurately complete the positioning function, and the accuracy rate is 95.56%. The target object recognition is combined with the depth information to determine the distance, and the spatial coordinates of the object centroid are obtained in real time. The accuracy rate can reach 94.48%, and the target objects of different shapes can be recognized. When the target object is grasped by the robot arm, it can be grasped accurately according to the depth information, and the grasping rate reaches 92.67%. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Template matching

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