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学者姓名：郭红
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Abstract ：
Clustering algorithms aim at gathering similar data points from a dataset in an unsupervised manner. Although the batch clustering algorithms have relatively high accuracy, they cannot make use of the dynamic clustering results efficiently. The requirement of using the whole dataset in calculation results in the problems of resource waste and high time cost. On the contrary, incremental clustering only needs to update the varied part of a model upon the arrival of new data, which makes it unnecessary to recluster the whole data all the time. The feature is very suitable for the streaming data process, but it decreases the accuracy of the algorithms and cannot satisfy the low latency requirement of real-time data processing. In response to this problem, the paper proposes a novel unified batch and streaming clustering model (UBSCM) based on streaming computation, which includes a streaming cluster feature updating mechanism (SCFUM). The Flink framework is used to implement a new streaming KMeans algorithm based on UBSCM (KMeansUBSP). The experiments on the real-world datasets validate that the new streaming KMeans algorithm is effective in clustering the batch and streaming data in a unified manner. © 2021, Springer Nature Singapore Pte Ltd.
Keyword ：
Clustering algorithms Clustering algorithms Groupware Groupware Interactive computer systems Interactive computer systems Social networking (online) Social networking (online)
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| GB/T 7714 | Chen, Hao , Guo, Kun , Chen, Yuzhong et al. The Design and Implementation of KMeans Based on Unified Batch and Streaming Processing [C] . 2021 : 639-649 . |
| MLA | Chen, Hao et al. "The Design and Implementation of KMeans Based on Unified Batch and Streaming Processing" . (2021) : 639-649 . |
| APA | Chen, Hao , Guo, Kun , Chen, Yuzhong , Guo, Hong . The Design and Implementation of KMeans Based on Unified Batch and Streaming Processing . (2021) : 639-649 . |
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With the development of text summarization research, the methods based on RNN with the Encoder-Decoder model gradually become the mainstream. However, RNN tends to forget previous context information and leads to the lack of original information. That will reduce the accuracy of the generated summarizations. The Transformer model uses the self-attention mechanism to encode and decode historical information so that it can achieve better performance than RNN in learning context information. In this paper, a text summarization model based on transformer and switchable normalization is proposed. The accuracy of the model is improved by optimizing the normalization layer. Compared with other models, the new model has a great advantage in understanding words' semantics and associations. Experimental results in English Gigaword dataset show that the proposed model can achieve high ROUGE values and make the summarization more readable. © 2019 IEEE.
Keyword ：
Big data Big data Cloud computing Cloud computing Decoding Decoding Recurrent neural networks Recurrent neural networks Semantics Semantics Social networking (online) Social networking (online) Text processing Text processing
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| GB/T 7714 | Luo, Tao , Guo, Kun , Guo, Hong . Automatic text summarization based on transformer and switchable normalization [C] . 2019 : 1606-1611 . |
| MLA | Luo, Tao et al. "Automatic text summarization based on transformer and switchable normalization" . (2019) : 1606-1611 . |
| APA | Luo, Tao , Guo, Kun , Guo, Hong . Automatic text summarization based on transformer and switchable normalization . (2019) : 1606-1611 . |
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The prospect of applying Brain-Computer Interface(BCI) to medical treatment and rehabilitation engineering has been attracting researchers around the world. However, the patients who use Chinese always confront more difficulties when they are attempting to transmit their intentions by BCI, because the current BCIs are short of the capability of supporting Chinese input. Thus, we developed a brain-controlled Chinese pinyin input platform, intending to provide Chinese participants in research or application of BCI with more conveniences. On the platform, the procedure of inputting a Chinese character includes 4 selecting steps: consonant, vowel, tone and character. We designed 3 different 6×6 symbol matrices for selecting consonants, vowels or characters and a special line of symbol for selecting tones. All the 4 selecting steps were achieved by detecting P300 elicited target symbols. Based on this platform, we have carried out a number of BCI studies with Chinese background. © 2019 IEEE.
Keyword ：
Biomedical engineering Biomedical engineering Brain computer interface Brain computer interface Image processing Image processing Linguistics Linguistics
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| GB/T 7714 | Lv, Zhilei , Guo, Hong , Huang, Zhihua . A Brain-Controlled Chinese Pinyin Input Platform [C] . 2019 . |
| MLA | Lv, Zhilei et al. "A Brain-Controlled Chinese Pinyin Input Platform" . (2019) . |
| APA | Lv, Zhilei , Guo, Hong , Huang, Zhihua . A Brain-Controlled Chinese Pinyin Input Platform . (2019) . |
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In the era of big data, HBase has been widely used in scenarios of massive unstructured data. For the financial big data, due to the integrity and timing of it, unreasonable data storage and management usually lead to hot spots that decreases the query performance. In practice, the separation of hot and cold financial data will improve data query performance and utilization rate of cluster resources. In this paper, a hot and cold data separation scheme is designed, to store infrequently queried financial data to HBase, and frequently queried one to Redis. The cold data is reasonably planned and managed through pre-partitioning and row key design for HBase. A hot data cache based on Redis is realized to improve the query speed and reduces the pressure of HBase. In addition, due to the lack of Redis's inherent cache elimination strategy, we propose a caching strategy based on the frequencies of updating and querying operations. The experimental results show that the scheme can effectively avoid the hot storage problem, and improve the query performance, and improve the cache hit ratio of Redis. Therefore, the number of cold data access requests can be effectively reduced. © 2019 IEEE.
Keyword ：
Big data Big data Cloud computing Cloud computing Digital storage Digital storage Finance Finance Information management Information management Separation Separation Social networking (online) Social networking (online)
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| GB/T 7714 | Li, Kunhui , Guo, Kun , Guo, Hong . Financial big data hot and cold separation scheme based on hbase and redis [C] . 2019 : 1612-1617 . |
| MLA | Li, Kunhui et al. "Financial big data hot and cold separation scheme based on hbase and redis" . (2019) : 1612-1617 . |
| APA | Li, Kunhui , Guo, Kun , Guo, Hong . Financial big data hot and cold separation scheme based on hbase and redis . (2019) : 1612-1617 . |
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Abstract ：
在大数据时代,海量的非结构化数据增速远大于结构化数据,HBase被广泛用于海量非结构化数据存储中.由于HBase内置的索引是基于行键(rowkey)设计的,具有很高的查询效率.但是,在根据字段进行条件查询时需要进行全表扫描,性能较低,无法应用于实时场景.针对此问题,提出一种基于协处理器(coprocessor)的HBase二级索引方法.该方法将经常需要查询的字段通过协处理器在HBase中建立映射到行键的索引,在查询时并行扫描索引数据获取行键,并利用行键快速查询记录.同时,在创建表时,通过对Region进行预分区.在插入数据时,在行键中添加Hash值.这不仅能提高数据插入速度,也避免了热点数据现象,同时保证索引数据和主数据位于同一个Region上,查询时就能减少一次RPC请求.在模拟数据集上的实验表明:提出的二级索引方法具有较好的查询性能.不仅高于HBase自带的过滤查询,也高于基于ElasticSearch的二级索引.同时,其空间开销小于基于ElasticSearch的二级索引.
Keyword ：
ElasticSearch ElasticSearch HBase HBase 二级索引 二级索引 协处理器 协处理器
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| GB/T 7714 | 郭红 , 周健倩 , 张瑛瑛 et al. 基于协处理器的HBase二级索引方法 [J]. | 计算机工程与应用 , 2019 , 55 (21) : 86-91 . |
| MLA | 郭红 et al. "基于协处理器的HBase二级索引方法" . | 计算机工程与应用 55 . 21 (2019) : 86-91 . |
| APA | 郭红 , 周健倩 , 张瑛瑛 , 郭昆 . 基于协处理器的HBase二级索引方法 . | 计算机工程与应用 , 2019 , 55 (21) , 86-91 . |
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Protein-protein interaction plays a fundamental role in many biological processes and diseases. Characterizing protein interaction sites is crucial for the understanding of the mechanism of protein-protein interaction and their cellular functions. In this paper, we proposed a method based on integrated support vector machine (SVM) with a hybrid kernel to predict-protein interaction sites. First, a number of features of the protein interaction sites were extracted. Secondly, the technique of sliding window was used to construct a protein feature space based on the influence of the adjacent residues. Thirdly, to avoid the impact of imbalance of the data set on prediction accuracy, we employed boost-strap to re-sample the data. Finally, we built a SVM classifier, whose hybrid kernel comprised a Gaussian kernel and a Polynomial kernel. In addition, an improved particle swarm optimization (PSO) algorithm was applied to optimize the SVM parameters. Experimental results show that the PSO-optimized SVM classifier outperforms existing methods.
Keyword ：
Boost-strap Boost-strap Particle swarm optimization Particle swarm optimization Protein interaction sites Protein interaction sites Sliding window Sliding window Support vector machine Support vector machine
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| GB/T 7714 | Guo, Hong , Liu, Bingjing , Cai, Danli et al. Predicting protein-protein interaction sites using modified support vector machine [J]. | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2018 , 9 (3) : 393-398 . |
| MLA | Guo, Hong et al. "Predicting protein-protein interaction sites using modified support vector machine" . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 9 . 3 (2018) : 393-398 . |
| APA | Guo, Hong , Liu, Bingjing , Cai, Danli , Lu, Tun . Predicting protein-protein interaction sites using modified support vector machine . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2018 , 9 (3) , 393-398 . |
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The P300 Speller is a kind of Brain-Computer Interface through which a person can input characters to a computer just by adjusting his own attention. The P300 Speller can service special people, but its capability of communication is expected to be enhanced. In this paper, we propose a method to implement a system, Brain-Controlled Speech Generator (BCSG), which can say a sentence after a person briefly describes it by a P300 Speller. BCSG is composed of a sentence speller, a sentence corrector, and a sound producer. The sentence speller is based on a P300 Speller. The sentence corrector restores a sentence when a person omits some trivial characters or makes some mistakes during his spelling. The sound producer speaks out the sentence. We implemented the BCSG and our experiments showed that our BCSG could say the sentences in time after the subjects express them by paying attention to the different stimuli presented by the BCSG. The BCSG is a new communication approach for some special situations.
Keyword ：
BCSG BCSG Brain-computer interfaces (BCIs) Brain-computer interfaces (BCIs) P300 Speller P300 Speller sentence corrector sentence corrector
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| GB/T 7714 | Wang, Xiaona , Guo, Hong , Huang, Zhihua . A Brain-Controlled Speech Generator Based on the P300 Speller [C] . 2017 . |
| MLA | Wang, Xiaona et al. "A Brain-Controlled Speech Generator Based on the P300 Speller" . (2017) . |
| APA | Wang, Xiaona , Guo, Hong , Huang, Zhihua . A Brain-Controlled Speech Generator Based on the P300 Speller . (2017) . |
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Abstract ：
The P300 Speller is a kind of Brain-Computer Interface through which a person can input characters to a computer just by adjusting his own attention. The P300 Speller can service special people, but its capability of communication is expected to be enhanced. In this paper, we propose a method to implement a system, Brain-Controlled Speech Generator (BCSG), which can say a sentence after a person briefly describes it by a P300 Speller. BCSG is composed of a sentence speller, a sentence corrector, and a sound producer. The sentence speller is based on a P300 Speller. The sentence corrector restores a sentence when a person omits some trivial characters or makes some mistakes during his spelling. The sound producer speaks out the sentence. We implemented the BCSG and our experiments showed that our BCSG could say the sentences in time after the subjects express them by paying attention to the different stimuli presented by the BCSG. The BCSG is a new communication approach for some special situations. © 2017 IEEE.
Keyword ：
Biomedical engineering Biomedical engineering Brain computer interface Brain computer interface Image processing Image processing Sound reproduction Sound reproduction
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| GB/T 7714 | Wang, Xiaona , Guo, Hong , Huang, Zhihua . A brain-controlled speech generator based on the P300 speller [C] . 2017 : 1-5 . |
| MLA | Wang, Xiaona et al. "A brain-controlled speech generator based on the P300 speller" . (2017) : 1-5 . |
| APA | Wang, Xiaona , Guo, Hong , Huang, Zhihua . A brain-controlled speech generator based on the P300 speller . (2017) : 1-5 . |
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提出一种蛋白质亚细胞定位预测方法.该方法以位置特异性得分矩阵和基因本体抽取对应特征,结合支持向量机构建多标签分类模型.充分考虑了蛋白质进化信息对其亚细胞定位的影响,并基于文本分类中涉及到的卡方检验的对数变换思想,构建基因本体注释信息的加权系数对其进行加权处理,从而提高预测的准确率.采用支持向量机作为基分类器构建多标签分类模型,进一步提高预测的准确率.通过在目前该领域两个常用的真实数据集上进行的一系列测试结果表明,该方法能有效提高蛋白质亚细胞定位预测的准确率.
Keyword ：
位置特异性得分矩阵 位置特异性得分矩阵 基因本体 基因本体 多标签分类 多标签分类 定位预测 定位预测 蛋白质亚细胞 蛋白质亚细胞
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| GB/T 7714 | 刘冰静 , 郭红 . 以位置特异性得分矩阵和基因本体为特征的蛋白质亚细胞定位预测 [J]. | 福州大学学报（自然科学版） , 2017 , 45 (1) : 16-24 . |
| MLA | 刘冰静 et al. "以位置特异性得分矩阵和基因本体为特征的蛋白质亚细胞定位预测" . | 福州大学学报（自然科学版） 45 . 1 (2017) : 16-24 . |
| APA | 刘冰静 , 郭红 . 以位置特异性得分矩阵和基因本体为特征的蛋白质亚细胞定位预测 . | 福州大学学报（自然科学版） , 2017 , 45 (1) , 16-24 . |
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提出一种利用WiFi信号指纹实现对室内区域进行定位的CL-KNN(complete linkage K-nearest neighbor)算法.该算法先采用层次聚类方法对测试环境进行区域划分,再根据相应的WiFi信号指纹信息进行匹配,最后通过加权计算确定定位结果.实验结果表明,在WiFi热点数量足够多的情况下,与原始KNN算法和k-means-KNN算法相比,CL-KNN算法可以获得更高的定位精度和准确率.
Keyword ：
WiFi信号 WiFi信号 位置指纹定位 位置指纹定位 室内定位 室内定位 层次聚类算法 层次聚类算法
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| GB/T 7714 | 王怡婷 , 郭红 . 基于层次聚类的WiFi室内位置指纹定位算法 [J]. | 福州大学学报（自然科学版） , 2017 , 45 (1) : 8-15 . |
| MLA | 王怡婷 et al. "基于层次聚类的WiFi室内位置指纹定位算法" . | 福州大学学报（自然科学版） 45 . 1 (2017) : 8-15 . |
| APA | 王怡婷 , 郭红 . 基于层次聚类的WiFi室内位置指纹定位算法 . | 福州大学学报（自然科学版） , 2017 , 45 (1) , 8-15 . |
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