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1. End-to-End No-Reference Video Semantic Communication Quality Assessment via Deep Neural Networks | |||
Zhang Baiquan,Que Xirong | |||
Computer Science and Technology 12 January 2024 | |||
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Abstract:Video semantic communication is developing rapidly nowadays, but traditional video quality assessment methods are not fully compatible with it. There is a lack of a no-reference video quality assessment method specifically designed for video semantic communication. In this paper, we propose a new end-to-end no-reference video semantic communication quality assessment using deep neural networks. Our model, which is implemented based on video semantic communication, it creatively uses both common and individual features extracted from videos through semantic communication for video quality assessment. Our model adopts a multi-task DNN framework, which assesses the quality of both common and individual features, and finally combines both to obtain the final video quality prediction score. Experimental results show that our assessment model outperforms other no-reference video quality assessment methods and is more suitable for semantic communication. | |||
TO cite this article:Zhang Baiquan,Que Xirong. End-to-End No-Reference Video Semantic Communication Quality Assessment via Deep Neural Networks[OL].[12 January 2024] http://en.paper.edu.cn/en_releasepaper/content/4761748 |
2. Research on entity extraction of demand for industry-university-research projects based on BERT-BiLSTM-CRF | |||
ZHANG Zhiqing,TAO Zekui | |||
Computer Science and Technology 04 January 2024 | |||
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Abstract:In order to realize the effective docking between schools and enterprises in industry-university-research cooperation projects and accurately obtain the technical needs of enterprises, based on the characteristics of concise and diversified texts of industry-university-research projects, a Chinese named entity extraction method based on BERT+BiLSTM+CRF model was proposed. Firstly, the BERT model is used to encode the input text, then the BiLSTM model is used to model the context to capture more comprehensive context information, and finally the label decoding is carried out through the CRF layer to obtain the optimal entity annotation results. Experimental results show that the proposed method is effective and feasible, and the extraction effect is better than that of the traditional method, which provides the possibility to solve the difficulties of technical demand information extraction, such as polysemy and language variants. | |||
TO cite this article:ZHANG Zhiqing,TAO Zekui. Research on entity extraction of demand for industry-university-research projects based on BERT-BiLSTM-CRF[OL].[ 4 January 2024] http://en.paper.edu.cn/en_releasepaper/content/4761868 |
3. Calculation of Lightning Electromagnetic Pulse Based on Neural Network Boundary | |||
Di Wang,Jianghai Wang | |||
Computer Science and Technology 02 January 2024 | |||
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Abstract:In this paper, a LSTM network based boundary model is investigated to compute the propagation of lightning electromagnetic pulsed fields (LEMP) in space. A LSTM neural network is introduced in place of the traditional Perfectly Matched Layer (PML) absorbing boundary conditions in solving the value of LEMP using the FDTD algorithm.The data on the PML boundary is utilized for model training. Compared with the traditional PML boundary model, the computational complexity and computation are greatly reduced because the neural network needs only one cell layer as the boundary. Meanwhile, in order to enhance the generalization ability of the model, the Random Forest (RF) algorithm is used to screen the features of the data on the PML boundary. The experimental results show that the new model calculates the EMF values at each position in the space with good accuracy. | |||
TO cite this article:Di Wang,Jianghai Wang. Calculation of Lightning Electromagnetic Pulse Based on Neural Network Boundary[OL].[ 2 January 2024] http://en.paper.edu.cn/en_releasepaper/content/4761839 |
4. An SDN-Based Flow Table Encoding Approach for Resource and Efficiency Optimization in Topic-based Pub/Sub Systems | |||
Zhou Yu,Zhang Yang | |||
Computer Science and Technology 03 December 2023 | |||
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Abstract:With the rapid development of software-defined networking (SDN), SDN-Based multi-level flow table architectures are always employed to address issues such as QoS, security policies, and matching efficiency. The rise of semantic communication has also sparked researchers\' interest in semantic information and its utilization. Many studies on semantic representation and semantic summarization have emerged. This study takes a new perspective to reduce the number of table entries by utilizing the semantic relationships implied in the topic tree in the topic-based pub/sub systems and introduces the concept of semantic aggregation. Semantic aggregation of table entries can work with multi-level flow table architecture to reduce the number of table entries while ensuring the correct delivery of streams. We propose a semantic-based table entry encoding algorithm to implement our idea and conduct several experiments to examine its performance. The experiment results demonstrate that our algorithm can achieve high space and efficiency optimization rate in a short encoding time. | |||
TO cite this article:Zhou Yu,Zhang Yang. An SDN-Based Flow Table Encoding Approach for Resource and Efficiency Optimization in Topic-based Pub/Sub Systems[OL].[ 3 December 2023] http://en.paper.edu.cn/en_releasepaper/content/4761615 |
5. Light field Stitching via 4D Homography | |||
DAI Yi-chen,CAI Min-jie | |||
Computer Science and Technology 14 May 2023 | |||
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Abstract:The problem of the light field (LF) stitching aims to align two 4D LFs seamlessly. However, the prior works use conventional $3\times3$ homography to draw the 2D relation and ignore the depth information, leading to two main disadvantages, namely, significant stitching artifacts in the general scene and failure to produce stitched depth map. This paper tackles these challenges by proposing a $4\times4$ homography that analytically and globally describes the relationship between two LFs under pure rotation. Besides, we also present a novel linear solver called 4ry, which can estimate the 4D homography by giving four 4D LF feature correspondences. Extensive synthetic and real data experiments demonstrate that the proposed method outperforms state-of-the-art approaches in LF stitching qualitatively and quantitatively. More importantly, the output of our method is still an LF that retains the nature of LF, such as refocusing, viewpoint shifting, and depth estimation. | |||
TO cite this article:DAI Yi-chen,CAI Min-jie. Light field Stitching via 4D Homography[OL].[14 May 2023] http://en.paper.edu.cn/en_releasepaper/content/4760779 |
6. Moving Object Detection Algorithm Based on Dynamic Vision Sensor | |||
SUN Xue,SUN Xue,LIU Dengfeng,LIU Dengfeng | |||
Computer Science and Technology 11 April 2023 | |||
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Abstract:Target detection and tracking mainly use RGB camera and deep learning algorithm. The information in the image is complex and redundant so the computing consumes a lot of resources. To solve the above problems, this paper proposes an improved spectral clustering algorithm to detect moving object. The algorithm is based on event data generated by dynamic vision sensors. In this paper, the cosine - Manhattan fusion distance is used to obtain a more accurate similarity matrix. The clustering results of some data are used to guide other data to speed up operation. The number of clusters is set adaptively to avoid the subjective influence of human beings. The results show that the accuracy of the improved algorithm on multiple data sets is more than 80%, and the time is significantly shortened. Spectral clustering algorithm based on dynamic vision sensor has great application potential in dealing with multi-target motion problems. | |||
TO cite this article:SUN Xue,SUN Xue,LIU Dengfeng, et al. Moving Object Detection Algorithm Based on Dynamic Vision Sensor[OL].[11 April 2023] http://en.paper.edu.cn/en_releasepaper/content/4760291 |
7. Design and Implementation of Web-based Code Breaker Crossword Game | |||
Zhao Yifan | |||
Computer Science and Technology 17 February 2023 | |||
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Abstract:Code breaker crosswords is a kind puzzle game for active thinking. Players need to break the code and complete all the words in a given grid. This kind of game often appears in the paper material of the journal. As the paper material decreases, the online code breaker crossword game becomes feasible. The paper designs a crossword generation algorithm, the generation algorithm creates a scoring matrix for each letter of each word, compares and calculates the optimal position of each possible word, and sorts the word length and creates randomness to increase the diversity of results. Ultimately solves the problem of computing an optimal crossword puzzle given a list of words. The experimental results show that the algorithm can freely set the word list, grid size, and calculation time, and get the optimal result within the specified time. Based on the algorithm, this paper implements an online crossword puzzle website to provide users with a multi-faceted gaming experience. | |||
TO cite this article:Zhao Yifan. Design and Implementation of Web-based Code Breaker Crossword Game[OL].[17 February 2023] http://en.paper.edu.cn/en_releasepaper/content/4759175 |
8. WFLNNet: Weighted Fusion of Linear and Nonlinear Predictions for Multivariate Time Series | |||
Dan Liu,Yuke Wang,Kun Xie,Ruotian Xie,Wei Liang,Dafang Zhang,Jigang Wen | |||
Computer Science and Technology 19 May 2022 | |||
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Abstract:Multivariate time series forecasting has been widely used in finance, environment, transportation and other fields. However, traditional statistical prediction models usually assume that the time series conforms to a certain distribution or functional form, and cannot capture the complex nonlinear relationships. Although neural network based algorithms have powerful learning abilities, they usually ignore the linear features in time series. By weighted and fused both Linear and Nonlinear Predictions, this paper proposes a novel WFLNNet, where the linear prediction module is designed based on an autoregressive model while the nonlinear prediction module is designed based on the neural network and consists of a feature extraction encoder, an interactive attention network, and a fully connected layer to capture the most effective features in temporal and spatial correlations, as well as a mutual influence among multivariate time series. We have done experiments using 4 real datasets by comparing them with 6 baseline algorithms. The experimental results demonstrate that WFLNNet outperforms the 6 baseline algorithms with more accurate prediction. | |||
TO cite this article:Dan Liu,Yuke Wang,Kun Xie, et al. WFLNNet: Weighted Fusion of Linear and Nonlinear Predictions for Multivariate Time Series[OL].[19 May 2022] http://en.paper.edu.cn/en_releasepaper/content/4757819 |
9. A Holistic Timing Analysis with Low Pessimism and Low Time Complexity for Rate-Constrained Traffic in TTEthernet | |||
Ran Li,Bin Fu,Guoqi Xie,Fei Peng,Renfa Li | |||
Computer Science and Technology 18 May 2022 | |||
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Abstract:Reviews: please describe the background, status and application of the research with 150-300 words. I and we can not be used as the subject, and the abstract must not the same as the sentences of the main text. General research paper: please extracts the key points of the paper, give the main research achievements with object, methods, results and conclusion with 200-400 words. I and we can not be used as the subject, and the abstract must not the same as the sentences of the main text. | |||
TO cite this article:Ran Li,Bin Fu,Guoqi Xie, et al. A Holistic Timing Analysis with Low Pessimism and Low Time Complexity for Rate-Constrained Traffic in TTEthernet[OL].[18 May 2022] http://en.paper.edu.cn/en_releasepaper/content/4757804 |
10. In-Vehicle Network Intrusion Detection using Deep Transfer Learning and SVM | |||
YANG Jun-Fang,FU Bin,LI Ren-Fa | |||
Computer Science and Technology 18 May 2022 | |||
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Abstract:With the rapid development of intelligent driving technology and in-vehicle infotainment systems, the number of communication interfaces to the outside world has increased, and the architecture of the in-vehicle network has become highly complex. As a result, the Controller Area Network (CAN), which lacks security features, is more vulnerable to external attacks. Deep learning models are widely used in intrusion detection techniques, but it requires a large amount of data. However, accurate CAN attack data is not readily available in large quantities. To solve this problem, we propose an intrusion detection system (IDS) for in-vehicle CAN bus using deep transfer learning and support vector machines (SVM). In our IDS, the deep transfer learning model extracts the features of CAN messages; the SVM uses these features to identify if the CAN bus has been hacked. Experimental results show that with a limited amount of datasets, the proposed IDS still has a good detection performance with an accuracy of up to 99.1\%. | |||
TO cite this article:YANG Jun-Fang,FU Bin,LI Ren-Fa. In-Vehicle Network Intrusion Detection using Deep Transfer Learning and SVM[OL].[18 May 2022] http://en.paper.edu.cn/en_releasepaper/content/4757805 |
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