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9th International Conference on computer Engineering and Networks, CENet2019

作者： Chen, Jie Liang, Xiaoman Zhang, Jian College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811537523

With the development of the social economy, the amount of currency circulation has increased and become more frequent, but the customer gathering often occurs in many bank’s business. In this paper, with some framework technology, Spring Boot and MyBatis, a WeChat public platform software is developed for customers who are available to book branch business, such as branch service reservation, bank business reservation, reservation information inquiry and so on. Combined with WeChat public number and small program, the software offers humanized service and simple operation. Therefore, it can meet the needs of users who can choose a reasonable time to go to the ideal branch to handle their business quickly, which not only saves users’ time, but also improves the efficiency of the bank’s system. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Learning Guided Implicit Depth Function with Scale-aware Feature Fusion

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IEEE Transactions on Image processing 2025年 34卷 3309-3322页

作者： Zuo, Yifan Hu, Yuqi Xu, Yaping Wang, Zhi Fang, Yuming Yan, Jiebin Jiang, Wenhui Peng, Yuxin Huang, Yan Jiangxi University of Finance and Economics School of Information Management Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing (No. 2024SSY03141) Jiangxi Nanchang China Peking University Wangxuan Institute of Computer Technology Beijing China University of Technology Sydney Australian Artificial Intelligence Institute Sydney NSW Australia

Recently, the single image super-resolution based on implicit image function is a hot topic, which learns a universal model for arbitrary upsampling scales. By contrast, color-guided depth map super-resolution is less explored based on implicit function learning. The related research faces three questions. First, is it also necessary and applicable to fuse the depth feature and the color feature in the encoder with continuous upsampling scales? Second, is the scale information in the encoder as important as that in the decoder? Third, how to efficiently and effectively model the affinity of location distance and content similarity within cross domains in the decoder? This paper proposes a transformer-based network to answer the above questions, which includes a depth super-resolution branch and a guidance extraction branch. Specifically, in the encoder, the effective implicit cross transformer is designed to fuse the guidance from the color feature with continuous coordinate mapping. In addition, the unrelated guidance is filtered out by correlation evaluation in the high-dimension feature space. Unlike the scale only introduced in the decoder, this paper additionally embeds the scale into the position encoding and the feed-forward network in the encoder to learn the scale-aware feature representation. In the decoder, the high-resolution depth feature is reconstructed by using the internal prior and the external guidance. The internal prior is implemented by implicit self-attention in the depth super-resolution branch, and the external guidance is exploited via implicit cross-attention between both branches. Finally, the above decoded features are complementary to generate the high-resolution depth map. The sufficient experiments on the synthetic and real datasets for in-distribution and out-of-distribution upsampling scales validate the improved performance. © 1992-2012 IEEE.

关键词： Guided Depth Map Super-resolution Implicit Image Function Local Implicit Transformer Vision Transformer

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Design of a Real-Time and Reliable Multi-machine System 10th

Design of a Real-Time and Reliable Multi-machine System

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Zhang, Jian Li, Lang Li, Qiuping Zhao, Junxia Liang, Xiaoman College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

An IoT (Internet of Things) system is a multi-machine system composed of a master controller and multiple slave controllers. In some automatic control systems such as petroleum and electric power, the main control uses UART (Universal Asynchronous Receiver/Transmitter) to communicate with the slave controller. However, the communication rate of the UART interface is limited and the delay time is long, which affects the systems response to external events. In order to improve the response speed of the system to external events, this paper designs a multi-machine system based on SPI (Serial Peripheral Interface) interface. First, we use the SPI interface of the microcontroller to build a communication network. The circuit structure of the system is simple, and the hardware resource consumption is low. Then, for the problem that the SPI interface cannot detect communication errors and there is no response mechanism, we designed a communication protocol to make up for these defects from the software level. Finally, we use STM32F429IGT6 as the master controller and STM32F103C8T6 as the slave controller to build the experimental platform. The experimental results show that the system is safe and reliable, and can meet the requirements of real-time applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Controllers

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A Convolutional Knowledge Tracking Model with Forgetting Behavior and Graph Embedding

A Convolutional Knowledge Tracking Model with Forgetting Beh...

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International Conference on Electronics and information technology (EIT)

作者： Guimin Huang Zhiqing Huang School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

With the prevalence of various intelligent educational systems, it is imperative to uncover learners' proficiency in mastering knowledge points through their prior practice circumstances. Knowledge tracking is an extremely beneficial instrument in this regard. Among them, Convolutional Knowledge Tracking (CKT) has demonstrated excellent performance in numerous KT tasks, while it does not take into account the phenomenon of forgetting and the interconnection between knowledge points. In order to tackle these problems, we propose a model called FGE-CKT, which integrates forgetting behavior and graph embedding with CKT. Our model addresses the drawbacks of forgetting characteristics by modelling three key factors affecting students' forgetting behaviors through a fully connected neural net: repeated knowledge learning times, adjacent learning interval and interval between visits to repeat knowledge. At the same time, convolutional neural networks are used to simultaneously extract and incorporate connections between knowledge points into the model's input portion. It was confirmed by experiments on two well-established datasets that FGE-CKT achieved significant improvements in AUC performance.

关键词： Knowledge engineering Instruments Neural networks Predictive models Vectors Complexity theory Convolutional neural networks information technology

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Power Analysis Attack on a Lightweight Block Cipher GIFT 9th

Power Analysis Attack on a Lightweight Block Cipher GIFT

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9th International Conference on computer Engineering and Networks, CENet2019

ISBN: (纸本)9789811537523

GIFT is a new lightweight block cipher with smaller area and higher efficiency, which is very suitable for the Internet of Things (IoT) devices with constrained resources. The power analysis attack is an efficient method to extract the key from the cryptographic equipment. However, it is not easy to reveal the key by means of power analysis attack, when the cipher is implemented by hardware. In this article, we present the method of power analysis attack against GIFT. Firstly, we implemented GIFT on FPGA using the SAKURA-G board. Then, we explored the impact of point of interest (POI) on power analysis attack. We proposed the method of power analysis attack against the diffusion layer of GIFT. The experimental results show that the result of power analysis attack is affected by POI, and the key can be recovered when POI is registered. We can reveal the key using correlation power analysis, when targeting the diffusion layer of GIFT. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Side channel attack

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Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Ya Zhou Chen Chen Guimin Huang School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

In the research methods of sentence similarity, sentence similarity is often calculated from the semantic aspect, while the influence of syntactic structure is ignored. We propose an enhanced knowledge language representation model (ExtKBRCNN) based on CNN and Bi-GRU, which effectively uses the fine-grained word relations in the knowledge base to evaluate semantic similarity and models the relationship between knowledge structure and text structure. In order to make full use of the syntactic information of the sentence, we also propose a dependency tree kernel-based method (Dep-SIF), which combines syntactic information and semantic features to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates semantic and syntactic information to comprehensively evaluate sentence similarity. Experimental results show that the accuracy of the model on the MRPC dataset is 77.63% and the F1 value is 83.90%.

关键词： Knowledge engineering Accuracy Semantics Knowledge based systems Syntactics Natural language processing Software engineering

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An Optimized Scheme of information Hiding-Based Visual Secret Sharing 9th

An Optimized Scheme of Information Hiding-Based Visual Secre...

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9th International Conference on computer Engineering and Networks, CENet2019

作者： Zou, Yi Li, Lang Jiao, Ge College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811537523

information hiding-based visual secret sharing (IH-VSS) uses the information hiding techniques to embed secret data into images. In the IH-VSS scheme, hidden images are divided into n shadows under the guidance and constraints of some predetermined methods, and only a certain number or all of the credible shadows work together to recover hidden information. Based on (2, 2)-threshold IH-VSS scheme, this paper proposes an optimized IH-VSS scheme, it can randomly locate the hidden position of the secret data. We set a random number and use it as the initial hiding position, then combined with the length of the secret data, assigns the address, and completes the hiding of the secret data. The experimental results show that under the same embedding capacity, the peak signal-to-noise ratio (PSNR) of the optimized scheme is higher than other similar schemes, which is high to 55.41 dB. Moreover, the difference between the two shadows is very small. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Signal to noise ratio

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Gender Identification for Coloring Black and White Portrait with cGan 10th

Gender Identification for Coloring Black and White Portrait ...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Liu, Qingyun Lin, Mugang Sun, Yaqi Zhang, Feng College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

This paper proposes a method to color black & white portrait images with gender recognition model and condition generation network (cGan). As a cGan model essentially, a method named pix2pix interprets image style transformation as a translation process from input pixels to target pixels. Lots of input to target image pairs should be prepared for model training for different application scenarios. When pix2pix is applied to color black & white portraits, there are obvious differences in color and saturation between portraits of different genders while the ability of gender recognition isnt owned by pix2pix in essence. Therefore, a gender recognition model is added to the network to produce more realistic and gender specific color portraits. The experiments show that our work has achieved better results than some other common methods. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Color

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Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation

Continual-MAE: Adaptive Distribution Masked Autoencoders for...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jiaming Liu Ran Xu Senqiao Yang Renrui Zhang Qizhe Zhang Zehui Chen Yandong Guo Shanghang Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University AIRobotics MMLab CUHK University of Science and Technology of China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pretrained model to continually changing target distributions, addressing real-world dynamism. Existing CTTA methods mainly rely on entropy minimization or teacher-student pseudo-labeling schemes for knowledge extraction in unlabeled target domains. However, dynamic data distributions cause miscalibrated predictions and noisy pseudo-labels in existing self-supervised learning methods, hindering the effective mitigation of error accumulation and catastrophic forgetting problems during the continual adaptation process. To tackle these issues, we propose a continual self-supervised method, Adaptive Distribution Masked Autoencoders (ADMA), which enhances the extraction of target domain knowledge while mitigating the accumulation of distribution shifts. Specifically, we propose a Distribution-aware Masking (DaM) mechanism to adaptively sample masked positions, followed by establishing consistency constraints between the masked target samples and the original target samples. Additionally, for masked tokens, we utilize an efficient decoder to reconstruct a handcrafted feature descriptor (e.g., Histograms of Oriented Gradients), leveraging its invariant properties to boost task-relevant representations. Through conducting extensive experiments on four widely recognized benchmarks, our proposed method attains state-of-the-art performance in both classification and segmentation CTTA tasks.

关键词： Adaptation models Histograms Dams Prevention and mitigation Self-supervised learning Minimization Entropy

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Intelligent Test Paper Generation Algorithm Integrating Normal Distribution and Parallel Genetic Algorithm

Intelligent Test Paper Generation Algorithm Integrating Norm...

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Intelligent Computing and Human-computer Interaction (ICHCI), International Conference on

作者： Chuangye Liu Guimin Huang Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Genetic algorithms have been widely used in intelligent test paper generation systems. However, traditional genetic algorithms cannot ensure that the difficulty of test questions is normally distributed, and are prone to falling into local optimal solutions. To address the above problems, we proposed an intelligent test paper generation algorithm that combines normal distribution and parallel genetic algorithms. In the population initialization stage, we use the cumulative distribution function (CDF) of the normal distribution to initialize the population. In the subsequent genetic operations, we designed a directed mutation mechanism and added KL divergence as a penalty number in the design of the fitness function to ensure that the difficulty of the test questions is normally distributed. At the same time, in order to speed up the efficiency of the algorithm and escape from the local optimal solution, an adaptive migration strategy was introduced. Experimental results show that the difficulty of the test questions generated by this algorithm is normally distributed, and the algorithm performance is better than other algorithms.

关键词： Human computer interaction Heuristic algorithms Gaussian distribution Genetics Genetic algorithms Distribution functions Convergence

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