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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 ...

引用

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 Generic Deepfake Detection with Dynamic Curriculum

Towards Generic Deepfake Detection with Dynamic Curriculum

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wentang Song Yuzhen Lin Bin Li Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

Most previous deepfake detection methods bent their efforts to discriminate artifacts by end-to-end training. However, the learned networks often fail to mine the generic face forgery information efficiently due to ignoring data diversity. In this work, we propose to introduce sample hardness into the training of deepfake detectors via a curriculum learning paradigm. Specifically, we present a novel simple yet effective strategy, named Dynamic Facial Forensic Curriculum (DFFC), which makes the model gradually focus on hard samples during the training. To this end, we propose Dynamic Forensic Hardness (DFH) which integrates the facial quality score and instantaneous instance loss to dynamically measure sample hardness during training. Besides, we present a pacing function to construct data subsets from easy to hard throughout the training process based on DFH. Comprehensive experiments show that DFFC can improve both within- and cross-dataset performance of various kinds of end-to-end deepfake detectors in a plug-and-play manner. It indicates that DFFC can help deepfake detectors learn generic forgery discriminative features more efficiently by exploiting the information from hard samples.

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Simulation analysis of erosion characteristics of slurry pipelines based on CFD-DDPM pipeline parameters 43

Simulation analysis of erosion characteristics of slurry pip...

引用

43rd Chinese control Conference, CCC 2024

作者： Yuan, Haoran Wu, Jiande Xiong, Xin Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Yunnan Kunming650500 China

ISBN: (纸本)9789887581581

In order to explore the impact of pipe diameter, bend-diameter ratio and bend angle on the erosion of slurry pipe, a numerical simulation method approach grounded in Computational Fluid Dynamics - Dense Discrete Phase Model (CFD-DDPM) is proposed. Firstly, the geometric models of bends with different pipe diameters, bend-diameter ratios, and bend angles are constructed;secondly, the slurry pipe erosion model based on Dense Discrete Phase Model (DDPM) is constructed;then, model validation is conducted on the slurry pipe erosion model to affirm the accuracy of the model;finally, the computational fluid dynamics method is employed to simulate the erosion and wear behavior of the pipeline, to investigate the impact of pipe diameter, bend-diameter ratio and bend angle on the erosion and wear of the slurry pipeline. At the same time, gray correlation analysis was also used to analyze the significant relationship between the three pipeline factors and erosion and wear. The results show that the main erosion area is not determined, with the change of the pipeline parameters, the most serious erosion area will move;the effect of different pipeline parameter variations on the erosion rate is different, in which the pipe diameter of the largest impact, the second largest bend-diameter ratio, the smallest impact of the bend angle. Therefore, in the design of slurry pipeline, should first consider the influence of pipe diameter factors. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Slurry pipelines

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An Enhanced Model-Free Predictive Current control for PMSMs Incorporating Discrete Space Vector Modulation 19th

An Enhanced Model-Free Predictive Current Control for PMSMs ...

引用

19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Yang, Yuhui Qin, Qing Qin, Yingmei Che, Yanqiu School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9789819613823

Model-free predictive current control (MFPCC) has excellent parameter robustness owing to unused parameters of the controller. However, the method has the current gradient update stagnation problem due to the limited availability of only 7 voltage vectors, which results in a high current/torque ripple. To address this shortcoming, an enhanced MFPCC for permanent magnet synchronous machines (PMSMs) incorporating discrete space vector modulation (DSVM) is proposed in this paper. First, the basic voltage vectors are expanded from 7 to 37 by DSVM. Then, a novel current gradient updating method is developed to effectively reduce the current/torque ripple caused by current gradient updating stagnation. Finally, a candidate vector preselection method is designed to simplify the calculation process of current gradient updating. The effectiveness of the proposed method is verified on a 2 kW PMSM driving experimental platform. The experimental results show that the THD is reduced from 25.5% to 7.8% while the dq-axes current ripple is respectively reduced from 5.1 A and 5.8 A to 2.2A and 1.5 A. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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Vehicle Platoon control Based on information Exchanges in Internet of Vehicles 5th

Vehicle Platoon Control Based on Information Exchanges in ...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Gao, Tianqi Tian, Yuanxu Tao, Hongfeng Ni, Yuanzhi School of Internet of Things Engineering Jiangnan University Wuxi214122 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

ISBN: (纸本)9789811939976

Vehicle platooning is an emerging application in transportation system, boosting safe and efficient traffic environment. Based on the vehicular wireless communication technology, the driving states of the neighboring vehicles can be obtained for accurate vehicle control. In this work, the vehicle platoon system is modeled considering random characteristics of vehicular wireless communications. The consensus-based platoon control strategy and the autoregressive filtering algorithm are designed for efficient and effective control under topology of predecessor following (PF) and predecessor leading following (PLF), respectively. The platoon control model and algorithms are implemented by Simulink, where the results verify the effectiveness of the proposed solutions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series analysis

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A Document Presentation Attack Detection Scheme with Optical Flow under a Flashlight

A Document Presentation Attack Detection Scheme with Optical...

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Asia-Pacific Signal and information processing Association Annual Summit and Conference (APSIPA)

作者： Changsheng Chen Wenyu Chen Ximin Chen Haodong Li Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

The presentation attack of a document image can be launched by simple printing/displaying and capturing operations. It has posed a serious threat to the existing document authentication system. However, the generalization performance of existing document presentation attack detection (DPAD) methods is not satisfactory over document images with diverse contents and qualities. In this work, we propose a novel DPAD method that exploits a new data modality observable in the video recorded under a flashlight, named the optical Flow-based Authentication Network (F-AutheNet). Our F-AutheNet extracts both the spatial and optical flow information from video frames. The spatial data carries information on low-level image features, while the optical flow data shows the variations of reflective light under different materials. The features extracted from both data modalities are fused through a cross-modal focal loss. In the experiment, we collect a comprehensive student card video database (SCVD) under a flashlight, comprising more than 18K genuine, printer-captured, and screen-recaptured video frames. Experimental results show that, by incorporating features extracted from the optical flow data, the generalization performance of generic DPAD backbones has improved by a large margin under some challenging experimental protocols. Specifically, the average EER metrics have been reduced by 2.73 and 2.84 percentage points under cross-domain experiments covering samples of different capturing distances (i.e., qualities) and contents, respectively.

关键词： Optical losses Protocols Authentication Feature extraction Rendering (computer graphics) User experience Spatial databases Reflection Data mining Optical flow

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How to Train Your Neural Network for Molecular Structure Generation from Mass Spectra?

How to Train Your Neural Network for Molecular Structure Gen...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Kai Liu, Yanmin Dian, Longyang Sun, Shiwei Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Shandong University State Key Laboratory of Microbial Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9798350386226

Mass spectrometry serves as a pivotal tool for the analysis of small molecules through an examination of their mass-to-charge ratios. Recent advancements in deep learning have markedly enhanced the analysis of mass spectrometric data, facilitating the prediction of novel small molecule structures without the necessity of extensive databases. Nonetheless, the paucity of annotated datasets impedes the efficacious training of molecular generation models predicated on MS2 spectra. To mitigate this limitation, we introduce ctMSNovelist, an avant-garde method that amalgamates pre-training, fine-tuning, and co-training techniques to construct a more precise model for the generation of molecular structures from tandem mass spectrometry data. This novel approach augments both the training regimen and the predictive accuracy of the MSNovelist model, thereby surmounting the obstacle of limited data. The methodology commences with the pretraining of a Variational Autoencoder (VAE) to generate molecular fingerprints derived from SMILES strings. Subsequently, it undergoes fine-tuning to emulate noisy fingerprints originating from mass spectrometry (MS) data. Concurrently, MSNovelist is co-trained utilizing these simulated fingerprints, inclusive of the highly noisy variants produced in the early stages of VAE training. The incorporation of a substantial volume of noisy data serves to enhance model accuracy and avert overfitting. We evaluated ctMSNovelist using the GNPS dataset and attained a SMILES prediction accuracy of 48.8%, representing a 4.1% enhancement over MSNovelist. It is pertinent to note that the sole distinction between ctMSNovelist and MSNovelist in this experiment was the training process. The code and models are publicly available at https://***/xfcui/ctMSNovelist. © 2024 IEEE.

关键词： Neural networks

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Joint Optimization of Privacy Protection and Resource Allocation for Vehicular Edge Networks

Joint Optimization of Privacy Protection and Resource Alloca...

引用

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

作者： Gao, Tianqi Tao, Hongfeng Ni, Yuanzhi Shi, Jun Jiang, Fengcheng Jiangnan University School of Internet of Things Engineering Wuxi214122 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798350345384

In the last decade, the service of vehicular networks has benefited from the rapid development of advanced communication and computing technologies including edge computing. For vehicular network operators, it is valuable to study how to realize the accurate allocation of edge computing resource on the premise of protecting users' privacy. In this paper, we design a histogram publishing algorithm based on differential privacy. The algorithm first classifies tasks according to the characteristics of computing requests, then interferes categorized tasks and designs a shuffling algorithm based on Blum Blum Shub (BBS). Finally, the frequency histogram of computing resource is obtained by post-processing of the Expectation-Maximum (EM) algorithm. The simulation results verify the accuracy and superiority of the proposed histogram publishing algorithm by comparing it with the existing methods. © 2023 IEEE.

关键词： Edge computing

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Semi-Supervised Learning for Low-Light Image Enhancement by Pseudo Low-Light Image 16

Semi-Supervised Learning for Low-Light Image Enhancement by ...

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16th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

作者： Xie, Shuo Ma, Yingjie Xu, Wei Qiu, Song Sun, Yifei East China Normal University School of Communication and Electronic Engineering China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798350330755

Building paired datasets in low-light enhancement entails significant cost and time, making such datasets precious commodities. Many researchers focus on how to enable models to learn more information from limited datasets. A prevalent strategy involves employing semi-supervised learning techniques to enhance model performance through additional unpaired images. However, one of the main challenges faced is the scarcity of a vast number of unpaired images from the same domain as the original low-light images. Consequently, we introduce a semi-supervised image enhancement method using pseudo-low-light images. Initially, we generate pseudo low-light images with less noise compared with the source domain image by the Signal-to-Noise Ratio prior and diffusion models. We then employ the Mean-Teacher network and the feature constraints of the pseudo-low-light images to realize low-light image enhancement. Comprehensive experimental results validate the efficacy of our approach on real-world datasets. © 2023 IEEE.

关键词： Image enhancement

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Facial Expression Recognition In-the-Wild with Deep Pre-trained Models 17th

Facial Expression Recognition In-the-Wild with Deep Pre-tra...

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17th European Conference on Computer Vision, ECCV 2022

作者： Li, Siyang Xu, Yifan Wu, Huanyu Wu, Dongrui Yin, Yingjie Cao, Jiajiong Ding, Jingting Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Ant Group Hangzhou China

ISBN: (纸本)9783031250743

Facial expression recognition (FER) is challenging, when transiting from the laboratory to in-the-wild situations. In this paper, we present a general framework for the Learning from Synthetic Data Challenge in the 4th Affective Behavior Analysis In-The-Wild (ABAW4) competition, to learn as much knowledge as possible from synthetic faces with expressions. To cope with four problems in training robust deep FER models, including uncertain labels, class imbalance, mismatch between pretraining and downstream tasks, and incapability of a single model structure, our framework consists of four respective modules, which can be utilized for FER in-the-wild. Experimental results on the official validation set from the competition demonstrated that our proposed approach outperformed the baseline by a large margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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