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Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021:A survey

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Brain Science Advances 2022年第2期8卷 127-141页

作者： Jing Luo Qi Mao Yaojie Wang Zhenghao Shi Xinhong Hei Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and EngineeringXi’an University of TechnologyXi’an 710054ShaanxiChina

Objective:From September 10 to 13,2021,the finals of the BCI Controlled Robot Contest in World Robot Contest 2021 were held in Beijing,*** teams participated in the Algorithm Contest of Calibration-free Motor Imagery *** participants employed both traditional electroencephalograph(EEG)analysis methods and deep learning-based methods in the *** this paper,we reviewed the algorithms utilized by the participants,extracted the trends and highlighted interesting approaches from these methods to inform future contests and research ***:First,we analyzed the algorithms in separate steps,including EEG channel and signal segment setup,prepossessing technology,and classification ***,we emphasized the highlights of each ***,we compared the competition algorithm with the SOTA ***:The algorithm employed in the finals performed better than that of the SOTA *** the final stage of the contest,four of the top five teams used convolutional neural network models,suggesting that with the rapid development of deep learning,convolutional neural network-based models have been the most popular methods in the field of motor imagery BCI.

关键词： brain-computer interface motor imagery con-volutional neural network World Robot Contest

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key Management and Authentication Scheme Forward Trusted Pervasive Container Computing

Key Management and Authentication Scheme Forward Trusted Per...

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Big Data and Privacy Computing (BDPC), International Conference on

作者： XinHong Hei YuZhan Xue YiChuan Wang YiFan Liu Chen Peng Xiang Sun School of computer science and engineering Xi'an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology School of computer science and engineering Xi'an University of Technology Xi’an China

ISBN: (数字)9798350307597

ISBN: (纸本)9798350307603

Cloud computing technology is an internet-based computing model that provides high reliability and security measures, including data backup, secure access control, etc. As Docker containers play a increasingly important role in traditional cloud computing architectures, the communication needs between containers become prominent. However, existing container to container communication mainly relies on network protocols for file transfer, making it difficult to achieve the expected results in cloud cluster security. Therefore, this paper primarily focuses on container based keys and proposes a container identity trusted key management method based on Pervasive container computing architecture. The experimental results demonstrate that the container identity trusted key management method not only meets the security transmission characteristics required in the container cloud architecture, but also adopts a strategy combined with trusted hardware, providing security guarantees. In addition, the method proposed in this article that combines trusted hardware for signature verification can defend against multiple network attacks compared to traditional container architectures without using security policies.

关键词： computers Cloud computing Handwriting recognition Data privacy Protocols computer architecture Containers

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HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Technology Wuhan University of Science and Technology School of Future Technology South China University of Technology Shenzhen Research Institute of Hunan University School of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

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Variational Zero-shot Multispectral Pansharpening

arXiv

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arXiv 2024年

作者： Rui, Xiangyu Cao, Xiangyong Li, Yining Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Macao Institute of Systems Engineering Macau University of Science and Technology Taipa China

Pansharpening aims to generate a high spatial resolution multispectral image (HRMS) by fusing a low spatial resolution multispectral image (LRMS) and a panchromatic image (PAN). The most challenging issue for this task is that only the to-be-fused LRMS and PAN are available, and the existing deep learning-based methods are unsuitable since they rely on many training pairs. Traditional variational optimization (VO) based methods are well-suited for addressing such a problem. They focus on carefully designing explicit fusion rules as well as regularizations for an optimization problem, which are based on the researcher’s discovery of the image relationships and image structures. Unlike previous VO-based methods, in this work, we explore such complex relationships by a parameterized term rather than a manually designed one. Specifically, we propose a zero-shot pansharpening method by introducing a neural network into the optimization objective. This network estimates a representation component of HRMS, which mainly describes the relationship between HRMS and PAN. In this way, the network achieves a similar goal to the so-called deep image prior because it implicitly regulates the relationship between the HRMS and PAN images through its inherent structure. We directly minimize this optimization objective via network parameters and the expected HRMS image through alternating minimization. Extensive experiments on various benchmark datasets demonstrate that our proposed method can achieve better performance compared with other state-of-the-art methods. The codes are available at https://***/xyrui/PSDip. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Prompt and Contrastive Learning for Few-shot Sentiment Classification 5

Prompt and Contrastive Learning for Few-shot Sentiment Class...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Wang, Fei Chen, Long Huang, Xiaohua Xu, Cai Zhao, Wei Guan, Ziyu Lu, Guangyue Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Sentiment classification is a hot topic in the field of natural language processing. Currently, state-of-the-art classification models follow two steps: pre-training a large language model on upstream tasks, and then using human-labeled data to fine-tune a task-related model. However, there is a large gap between the upstream tasks of the pre-trained model and the downstream tasks being performed, resulting in the need for more labeled data to achieve excellent performance. Manually annotating data is expensive. In this paper, we propose a few-shot sentiment classification method based on Prompt and Contrastive Learning (PCL), which can significantly improve the performance of large-scale pre-trained language models in low-data and high-data regimes. Prompt learning aims to alleviate the gap between upstream and downstream tasks, and the contrastive learning is designed to capture the inter-class and intra-class distribution patterns of labeled data. Thanks to the integration of the two strategies, PCL markedly exceeds baselines with low resources. Extensive experiments on three datasets show that our method has outstanding performance in the few-shot settings. © 2022 ACM.

关键词： Computational linguistics

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Elevating Predictions: Sculpting Flight Delay Predictions with Recursive Feature Elimination

Elevating Predictions: Sculpting Flight Delay Predictions wi...

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computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Wei Wei Chenchen Yu Zhao Yang Fu Bo School of Computer Science and Engineering and the Shaanxi Key Laboratory for Network Computing and Security Xi'an University Of Technology Xi’an China School of Computer Science and Engineering Xi'an University Of Technology Xi’an China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

With the rapid development of the aviation industry and the significant growth of aviation data, how to use big data technology to accurately predict flight delays has become a problem that needs to be solved in the civil aviation transportation industry, and the application of big data models in the aviation field has also become crucial. To address this challenge, a new combinatorial forecasting method is proposed in this study. This method integrates random forest algorithm and recursive feature elimination technology to construct a comprehensive prediction model, aiming at improving the accuracy and efficiency of flight delay prediction. Experiments show that the proposed method performs better in the prediction accuracy and prediction error of flight delay data after the data is normalized to eliminate the influence of different dimensions and magnitudes on the model performance, which proves the effectiveness and practicability of the proposed method.

关键词： computers Accuracy Transportation industry Predictive models Big Data Prediction algorithms Data models Delays Forecasting Random forests

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News-driven financial warning based on label information attention 5

News-driven financial warning based on label information att...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Zhilin, Ren Long, Chen Xiaohua, Huang Wenjing, Wang Cai, Xu Wei, Zhao Ziyu, Guan Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Most existing news-driven stock market prediction methods ignore the potential relationship between financial news and stocks. The complex relationship can help us to improve the accuracy of algorithmic trading systems. Therefore, we propose a deep learning method for financial warning by fusing Stock label Information (SLI). We extract events from news texts and fuse stock information together as feature vectors, using neural networks to model the underlying relationship between news and stocks. Experimental results show that our method outperforms other baseline methods in experiments on TPX500 and TPX100 datasets. CCS CONCEPTS •Computing methodologies •Artificial intelligence •Natural language processing © 2022 ACM.

关键词： Deep learning

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Innovations in Machine Learning for Flight Delay Prediction: A Targeted Overview

Innovations in Machine Learning for Flight Delay Prediction:...

引用

computers and Artificial Intelligence Technologies (CAIT), International Conference on

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

Artificial intelligence technology is increasingly being applied to air traffic flow forecasting. Based on the core data of Web of Science, this study uses bibliometrics and content review methods to investigate the current situation, methodology evaluation and future research direction of flight delay prediction. Initially, a basic statistical analysis was performed on all the papers. This was followed by topic co-occurrence and keyword co-occurrence analysis, which revealed that most of the research focused on three main categories: machine learning methods, deep learning techniques, and algorithmic optimization strategies. Then reviewing the content in these categories, further content analysis showed that machine learning holds significant potential for handling large and diverse datasets.

关键词： Adaptation models Analytical models Computational modeling Machine learning Predictive models Prediction algorithms Data models Real-time systems Delays Optimization

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Petri Net Model of MITM Attack Based on NDP Protocol

Petri Net Model of MITM Attack Based on NDP Protocol

引用

2022 International Conference on networking and network Applications, NaNA 2022

作者： Zhang, Liumei Han, Yu Wang, Yichuan Quan, Ruiqin School of Computer Science Xi’an Shiyou University Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China Xinjiang Institute of Engineering China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Neighbor Discovery Protocol (NDP) is one of the core protocols of IPv6 networks. Since NDP messages are an unauthenticated stateless protocol, they are vulnerable to various types of attacks, and Man-In-The-Middle (MITM) attacks are one of the most well-known attacks in the computer field. During NDP address resolution, attackers change their own IP-MAC mapping relationships by sniffing NA (Neighbor Advertisement) messages, thus spoofing the source host's neighbor cache table and compromising the confidentiality, integrity and availability of the IPv6 network. Therefore, this paper focuses on the MITM attack in the NDP address resolution process, and performs a fine-grained analysis and Petri Net modelling of the attack process. © 2022 IEEE.

关键词： Petri nets

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Reversible Data Hiding Simultaneously Using Substitution of MSB and Compression of LSB of Encrypted Image 14

Reversible Data Hiding Simultaneously Using Substitution of ...

引用

14th International Conference on Digital Image Processing, ICDIP 2022

作者： Sui, Liansheng Liu, Jie Cheng, Ying Xiao, Zhaolin Tian, Ailing School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China Student’s Affairs Division Xi’an University of Technology Xi’an710048 China Shaanxi Province Key Lab of Thin Film Technology and Optical Test Xi’an Technological University Xi’an710048 China

ISBN: (纸本)9781510657564

With the development of privacy protection, reversible data hiding methods in encrypted image have drawn extensive research interest. Among them, a new method is proposed based on embedding prediction errors, i.e., EPE-based method, where secret information is embedded in the encrypted most significant bit plane. Not only the original image can be recovered with high quality but also the payload can reach close to 1 bit per pixel (bpp). However, there are potential errors in the process of extracting secret data, because most significant bits of a part of pixels are used as flags to mark prediction error location. In this paper, a reversible data hiding method in encrypted image with high capacity is proposed by combining most significant bit prediction with least significant bit compression. At first, most significant bit of each pixel is predicted and a location map of prediction errors in the original image is generated. In the same time, the original image is encrypted using a stream cipher method. Then, the location map is embedded into the vacated space generated with compressing least significant bits and the secret data is embedded into most significant bits of a part of pixels without prediction errors. In this way, the marked encrypted image is obtained. Finally, the original image can be recovered without any error and the secret information can be extracted correctly from the marked encrypted image. Experimental results show that the proposed method has better performance than EPE-based and other methods. © 2022 SPIE.

关键词： Pixels

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