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2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)最新文献

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Research on End-to-End Programmable Transmission Technology for Ship Manufacturing 船舶制造端到端可编程传输技术研究
2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828883
Dongyao Wang, Y. Liu, Fan Yang, Fangmin Xu
{"title":"Research on End-to-End Programmable Transmission Technology for Ship Manufacturing","authors":"Dongyao Wang, Y. Liu, Fan Yang, Fangmin Xu","doi":"10.1109/icicse55337.2022.9828883","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828883","url":null,"abstract":"With the exponential growth of the number of automation networking equipment and data transmission volume in the shipyard, the diversity of business and flow data for ship construction is rapidly increased. To meet different QoS (Quality of Service) requirements of different traffics, this paper carried out the research on the end-to-end transmission technology in 5G converged network which will be the popular network infrastructure within the shipyard workshop. Under the framework of SDN (Software Defined network)-based end-to-end programmable transmission, three technologies, including identification/mapping of multi-source heterogeneous data, programmable transmission scheme of end-to-end service data flow, congestion control and load balance, are intensively studied. The above technologies are combined to realize the on-demand transmission of ship-construction data in the environment of multi-network coexistence.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123792560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Transformer-Based Deep Learning Method for the Prediction of Ventilator Pressure 基于变压器的呼吸机压力预测深度学习方法
2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828926
Rui Fan
{"title":"Transformer-Based Deep Learning Method for the Prediction of Ventilator Pressure","authors":"Rui Fan","doi":"10.1109/icicse55337.2022.9828926","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828926","url":null,"abstract":"As patients infected with SARS-CoV-2 are more likely to have trouble breathing, a great demand for ventilators has been generated since the COVID-19 is continuing to spread around the world. However, the research and development of ventilator control suffer from high cost, slow efficiency, and lack of automation, especially regarding the estimation of ventilator pressure. In this paper, to address this challenge and help control the mechanical ventilators better, we develop a Transformer-based deep learning method for the prediction of ventilator pressure. Based on the dataset provided by Google Brain in a Kaggle competition, we connect the Transformer encoders by residual connections to extract features from the time-series ventilator data, and successfully achieve the goal to predict the ventilator pressure with excellent performance. After applying the K-Fold cross validation technique, our Transformer-based model reaches a mean absolute error 0.1311 on the private test set. This result ranks 67/2605 (top 2.6%) in the leaderboard of Google Brain - Ventilator Pressure Prediction competition, and can get a silver medal in this Kaggle competition. This work could accelerate the development of new methods to overcome the cost barrier of ventilator control.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122842367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Rudimentary Proof on Goldbach Conjectures 哥德巴赫猜想的初步证明
2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828990
Talal Al-Ameen, Imad Muhi
{"title":"A Rudimentary Proof on Goldbach Conjectures","authors":"Talal Al-Ameen, Imad Muhi","doi":"10.1109/icicse55337.2022.9828990","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828990","url":null,"abstract":"Goldbach’s conjecture is important because it suggests a causal relationship between all the positive even numbers ( ≥ 4) and the sums of the primes. In this paper, we prove that the sum of any two odd prime numbers is an even number and thereby the sequence of all even numbers > 4 can be expressed as the sum of two odd primes ad infinitum (Goldbach’s strong conjecture). And that every even number > 6 can be written as the sum of two odd primes plus 2, which in turn implies that every odd number ≥ 9 can be expressed as the sum of exactly two odd primes plus 3 (i.e. three odd primes) (Goldbach’s weak conjecture).","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115796768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Design and Implementation of a Perioperative Medical Data Quality Management Platform 围手术期医疗数据质量管理平台的设计与实现
2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828956
Jie Cao, Ju Zhang, Xiaoguang Lin, An Long Sun
{"title":"Design and Implementation of a Perioperative Medical Data Quality Management Platform","authors":"Jie Cao, Ju Zhang, Xiaoguang Lin, An Long Sun","doi":"10.1109/icicse55337.2022.9828956","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828956","url":null,"abstract":"At present, there are more than 60 million hospitalized surgeries each year in China, and hundreds of millions of medical data records have been accumulated. The diversity, speed and other characteristics make it confounding for perioperative medical data to comply with consistent standards, resulting in widespread quality problems. Many issues escape simple inspections because the data generated for surgeries are from multiple data streams. Hence perioperative medical data quality management platform is designed in this paper to unite data from multiple sources and address issues discovered from cross-referencing. By representing cross-referencing data rules with temporal logic, it implements a comprehensive work platform for data quality inspection, data quality control and data annotation of perioperative medical data.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133980533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Knowledge Graph Based Adversarial Radar Threat Assessment 基于知识图谱的对抗雷达威胁评估
2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828881
Chenyu Zhu, Yue Li, Xinyue Hou, Peng Wang, Xiaoyan Peng
{"title":"Knowledge Graph Based Adversarial Radar Threat Assessment","authors":"Chenyu Zhu, Yue Li, Xinyue Hou, Peng Wang, Xiaoyan Peng","doi":"10.1109/icicse55337.2022.9828881","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828881","url":null,"abstract":"In the field of military equipment knowledge, there are a large number of equipment models, weapon types, working parameters, and other time-frequency-space data information, among which there is a lot of valuable information. At present, when combat-related personnel face this massive knowledge, they cannot efficiently obtain the key knowledge, which means that they cannot provide effective guidance based on the potential key knowledge. To solve this problem, based on the investigation and analysis of the existing knowledge graph construction method, we excavate and extract military equipment knowledge, instantiate and correlate different weapon equipment, and construct the knowledge graph of military equipment. Its construction can not only deeply study the key technical difficulties of the graph in this field, but also has strong strategic support for the future development of this field. In the end, we propose a threat assessment for target radar with a TransE inference model based on the knowledge graph.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121945508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Image Matching Based on Fast PCA-SIFT Descriptors with Automatic Determination of Dimensionality for PCA 基于快速PCA- sift描述子的PCA维数自动确定图像匹配
2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828915
Yi Zheng, Ping Zheng
{"title":"Image Matching Based on Fast PCA-SIFT Descriptors with Automatic Determination of Dimensionality for PCA","authors":"Yi Zheng, Ping Zheng","doi":"10.1109/icicse55337.2022.9828915","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828915","url":null,"abstract":"Image matching is a key technology in the field of image processing. An effective image matching method based on fast PCA-SIFT descriptors is proposed and studied deeply. Firstly, the matrix left multiplication method is used to reduce the operation load of PCA and improve its operation speed. Secondly, we utilize the total interpretation proportion of the data variance of the top several principal components to determine the optimal dimension of the descriptor for PCA, thus the number of retained principal components can be determined automatically. Some intuitive and persuasive simulation experiments are carried out by using the proposed method. Experimental results demonstrate that the proposed method can automatically determine the number of retained principal components, and can reduce the operation load of PCA. The proposed image matching method can be used in the fields of three-dimensional reconstruction, cooperative augmented reality and teleoperation robots.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123772278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Text Analysis Method Based on Multi-channel Parallel Classifier 基于多通道并行分类器的文本分析方法
2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828968
Bingliang Lu, Zhihao Lin, Xindong Zhang
{"title":"Text Analysis Method Based on Multi-channel Parallel Classifier","authors":"Bingliang Lu, Zhihao Lin, Xindong Zhang","doi":"10.1109/icicse55337.2022.9828968","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828968","url":null,"abstract":"In recent years, with the continuous recognition of the value of data, text sentiment analysis in natural language processing has gradually become a research hotspot in the field of artificial intelligence. In this article, we propose a multi-channel parallel algorithm. First, train the entire network by constructing a word embedding layer, map the vocabulary to a higher-dimensional space through word2vec. Then the generated embedding matrix is integrated with the parallel classifier model based on TextCNN, LSTM and Transformer. We use web crawler technology to extract sentiment classification data set from various industries and multiple fields, and conduct comparative experiments on this data set. Experimental results show that the effect of this model is better than that of a single-kernel classifier model.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131215945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Advanced NMF-Based Approach for Single Cell Data Clustering 一种基于神经网络的单细胞数据聚类方法
2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828919
Peng Zhao, Yongpan Sheng, Xiaohui Zhan
{"title":"An Advanced NMF-Based Approach for Single Cell Data Clustering","authors":"Peng Zhao, Yongpan Sheng, Xiaohui Zhan","doi":"10.1109/icicse55337.2022.9828919","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828919","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) provides transcriptomic profiling for individual cells, allowing researchers to study the heterogeneity of tissues, recognize rare cell identities and discover new cellular subtypes. Clustering analysis is usually used to predict cell class assignments and infer cell identities. However, The performance of existing single-cell clustering methods is extremely sensitive to the presence of noise data and outliers. Nevertheless, there is still no consensus on the best performing method. To address this issue, we utilize an advanced NMF for scRNA-seq data clustering based on soft self-paced learning (S3NMF). We will gradually add cells from simple to complex to our model until the model converges. In this way, the influence of noisy data and outliers can be significantly reduced. The proposed method achieves the best performance on both simulation data and real scRNA-seq data.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131704290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-view User Preference Learning with Knowledge Graph for Recommendation 基于知识图谱的多视图用户偏好学习推荐
2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828877
Yiming Zhang, Yitong Pang, Zhihua Wei
{"title":"Multi-view User Preference Learning with Knowledge Graph for Recommendation","authors":"Yiming Zhang, Yitong Pang, Zhihua Wei","doi":"10.1109/icicse55337.2022.9828877","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828877","url":null,"abstract":"To learn more comprehensive user preference, existing works in recommendation propose to utilize side information, like Knowledge Graph (KG). However, the user representation can come from many views, including ID attributes of the user, collaborative signals of interaction history, and fine-grained preferences in KG, which has not been well studied in previous works. To address the limitation, in this work, we propose the Multi-view User Preference Learning with knowledge graph (MUPL) for recommendation to address the limitation. Specifically, we propose to employ Gate Recurrent Unit (GRU) to learn the user latent collaborative feature from interaction sequence. Besides, we design a Knowledge Graph Attention Network (KGANet) to capture user fine-grained preference for the entities related with the items. Then we fusion user ID attributes, the collaborative feature and fine-grained preference for entities into the user final representation. Similarly, we employ an item encoder to get the item final representation. Finally, a predictor is proposed for recommendation. Extensive experiments on three public datasets show that our model outperforms the state-of-the-art (SOTA) methods on effectiveness.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"311 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123459041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Survey: Complex Knowledge Base Question Answering 一项调查:复杂知识库问答
2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE) Pub Date : 2022-03-18 DOI: 10.1109/icicse55337.2022.9828967
Yuxin Luo, Bailong Yang, Donghui Xu, Luogeng Tian
{"title":"A Survey: Complex Knowledge Base Question Answering","authors":"Yuxin Luo, Bailong Yang, Donghui Xu, Luogeng Tian","doi":"10.1109/icicse55337.2022.9828967","DOIUrl":"https://doi.org/10.1109/icicse55337.2022.9828967","url":null,"abstract":"Knowledge base question answering(KBQA) is a technique that utilizes the rich semantic information in the knowledge base and fully understands the question to obtain the answer. At present, scholars put more energy into solving complex relationship problems. This paper first outlines the background and core challenges of complex KBQA. Second, two mainstream complex KBQA methods are introduced, namely, semantic parsing (SP-based) and information retrieval (IR-based) methods. Finally, future research trends are analyzed.","PeriodicalId":177985,"journal":{"name":"2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE)","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128218241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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