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29th International Conference on Computational Linguistics, COLING 2022

作者： Sheng, Xin Xu, Linli Xu, Yinlong Bao, Changcun Chen, Huang Ren, Bo Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Tencent Youtu Lab China

The task of generating texts of different categories has attracted more and more attention in the area of natural language generation recently. Meanwhile, generative adversarial net (GAN) has demonstrated its effectiveness on text generation, and is further applied to category text generation in later works. Different from existing methods, which mainly consider the pairwise relations between the text embedding and the corresponding fixed one-hot class label (data-to-class relations), this paper proposes a novel Contrastive Category Generative Adversarial Net (CoCGAN) to incorporate contrastive learning into adversarial category text generation, considering more flexible data-to-class relations as well as relations between the multiple text embeddings in the same batch (data-to-data relations). The discriminator of CoCGAN discriminates the authenticity of given samples and optimizes a contrastive learning objective to capture both more flexible data-to-class relations and data-to-data relations among training samples. Accordingly, the generator tries to produce more realistic samples which can confuse the discriminator. Experimental results on both synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generation models. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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Research on intrusion detection algorithm based on dilated convolution and bidirectional gating cyclic unit

Research on intrusion detection algorithm based on dilated c...

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2022 International Conference on Algorithms, Microchips and Network applications

作者： Liu, Xingyuan Miao, Xianghua Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Technology Application Key Laboratory of Yunnan Province Kunming650500 China

ISBN: (纸本)9781510653290

Intrusion detection based on machine learning plays an important role in the security protection of the network environment. In order to improve the accuracy of network intrusion detection, intrusion detection algorithm based on dilated convolution and a two-way gating unit is proposed. Firstly, dilated convolution (DCNN) is used to increase the receptive field of information and extract spatial features. Secondly, the bidirectional gated cyclic unit (bigru) model is used to capture the temporal features of data, and the attention mechanism is introduced to increase the calculation of feature importance. Compared with the classical machine learning classifier, this model has a high detection rate. The multi-classification experiments on the famous UNSW-NB15 data set show that the model has leading performance, and its classification accuracy can reach 97.14%. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Intrusion detection

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Learning discriminative representation with global and fine-grained features for cross-view gait recognition

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CAAI Transactions on Intelligence technology 2022年第2期7卷 187-199页

作者： Jing Xiao Huan Yang Kun Xie Jia Zhu Ji Zhang School of Computer Science South China Normal UniversityGuangzhouGuangdongChina Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhuaZhejiangChina School of Sciences University of Southern QueenslandToowoomba QldAustralia

In this study,we examine the cross-view gait recognition *** existing methods establish global feature representation based on the whole human body ***,they ignore some important details of different parts of the human *** the latest literature,positioning partial regions to learn fine-grained features has been verified to be effective in human *** they only consider coarse fine-grained fea-tures and ignore the relationship between neighboring *** the above insights together,we propose a novel model called GaitGP,which learns both important details through fine-grained features and the relationship between neighboring regions through global *** GaitGP model mainly consists of the following two ***,we propose a Channel-Attention Feature Extractor(CAFE)to extract the global features,which aggregates the channel-level attention to enhance the spatial information in a novel convolutional ***,we present the Global and Partial Feature Combiner(GPFC)to learn different fine-grained features,and combine them with the global fea-tures extracted by the CAFE to obtain the relevant information between neighboring *** results on the CASIA gait recognition dataset B(CASIA-B),The OU-ISIR gait database,multi-view large population dataset,and The OU-ISIR gait database gait datasets show that our method is superior to the state-of-the-art cross-view gait recognition methods.

关键词： gait database grain

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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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