Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416524
Neha Mittal, M. Hanmandlu, R. Vijay
This paper presents an approach for finger knuckle print (FKP's) authentication using DAISY descriptor on different knuckle combinations. Score level fusion is applied on scores of features extracted from FKPs. An improved identification rate is achieved after fusion as compared to that from the individual knuckle print. There has also been a little effort on study of feature selection. The entropy measures are employed for the feature selection. The experimental results also demonstrate the superiority of the DAISY descriptor over other features in terms of the time and accuracy.
{"title":"A finger-knuckle-print authentication system based on DAISY descriptor","authors":"Neha Mittal, M. Hanmandlu, R. Vijay","doi":"10.1109/ISDA.2012.6416524","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416524","url":null,"abstract":"This paper presents an approach for finger knuckle print (FKP's) authentication using DAISY descriptor on different knuckle combinations. Score level fusion is applied on scores of features extracted from FKPs. An improved identification rate is achieved after fusion as compared to that from the individual knuckle print. There has also been a little effort on study of feature selection. The entropy measures are employed for the feature selection. The experimental results also demonstrate the superiority of the DAISY descriptor over other features in terms of the time and accuracy.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130163504","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416578
Shrutilipi Bhattacharjee, Rendhir R. Prasad, Akash Dwivedi, A. Dasgupta, S. Ghosh
Increasing availability of geospatial data provides exceptional opportunities in knowledge creation and distribution. For the discovery of suitable data sources, keyword based search in the catalogue becomes inaccurate. The main reason behind this is the existing semantic heterogeneity in the database schema, deployed by different service providers. It necessitates the semantic management of the spatial catalogues. This paper presents an ontology based approach which is useful to create and manage catalogues semantically, hence resolving the semantic heterogeneity between geospatial repositories and incompatibility with spatial queries. Further, there is no standard available for semantic searching of the spatial catalogue till date. It will enhance the data extraction process by providing semantic meaning to the spatial catalogue.
{"title":"Ontology based framework for semantic resolution of geospatial query","authors":"Shrutilipi Bhattacharjee, Rendhir R. Prasad, Akash Dwivedi, A. Dasgupta, S. Ghosh","doi":"10.1109/ISDA.2012.6416578","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416578","url":null,"abstract":"Increasing availability of geospatial data provides exceptional opportunities in knowledge creation and distribution. For the discovery of suitable data sources, keyword based search in the catalogue becomes inaccurate. The main reason behind this is the existing semantic heterogeneity in the database schema, deployed by different service providers. It necessitates the semantic management of the spatial catalogues. This paper presents an ontology based approach which is useful to create and manage catalogues semantically, hence resolving the semantic heterogeneity between geospatial repositories and incompatibility with spatial queries. Further, there is no standard available for semantic searching of the spatial catalogue till date. It will enhance the data extraction process by providing semantic meaning to the spatial catalogue.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133984793","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416548
C. Halder, J. Paul, K. Roy
This paper presents analysis of individuality of handwritten Bangla numerals. It has a great prospect in Writer Identification, Writer Verification, Forensic Science etc. After collecting and extracting characters from filled in forms, 400 dimensional feature vectors is computed based on gradient of the images. A total of 450 documents were used for this work. In our experiment we have used LIBLINEAR classifier of WEKA environment. We have computed and analyzed the Individuality of each numeral and observed that the numeral 5 has the most individuality property than other numerals and 0 has the least. We have also done the writer identification with all the numerals and obtained 96.5% accuracy with all writers.
{"title":"Individuality of Bangla numerals","authors":"C. Halder, J. Paul, K. Roy","doi":"10.1109/ISDA.2012.6416548","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416548","url":null,"abstract":"This paper presents analysis of individuality of handwritten Bangla numerals. It has a great prospect in Writer Identification, Writer Verification, Forensic Science etc. After collecting and extracting characters from filled in forms, 400 dimensional feature vectors is computed based on gradient of the images. A total of 450 documents were used for this work. In our experiment we have used LIBLINEAR classifier of WEKA environment. We have computed and analyzed the Individuality of each numeral and observed that the numeral 5 has the most individuality property than other numerals and 0 has the least. We have also done the writer identification with all the numerals and obtained 96.5% accuracy with all writers.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132794152","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416656
A. Frolov, A. Abraham, Pavel Polyakov, D. Húsek, H. Řezanková
Studied are differences of two approaches to binary data dimension reduction. The first one is Boolean Matrix Factorization and the second one is Expectation Maximization Boolean Factor Analysis. The two BMF methods are used for comparison. First is M8 method from the BMDP statistical software package. The second is the BMF method, as suggested by Belohlavek and Vychodil (BVA2). These two are compared to Expectation Maximization Boolean Factor Analysis extended with binarization step developed here. Generated (Bars problem) and mushroom dataset are used for(experiments. In particular, under scrutiny was the reconstruction ability of the computed factors and the information gain as the measure of dimension reduction. In addition, presented are some general remarks on all the methods being compared.
{"title":"BFA and BMF: What is the difference","authors":"A. Frolov, A. Abraham, Pavel Polyakov, D. Húsek, H. Řezanková","doi":"10.1109/ISDA.2012.6416656","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416656","url":null,"abstract":"Studied are differences of two approaches to binary data dimension reduction. The first one is Boolean Matrix Factorization and the second one is Expectation Maximization Boolean Factor Analysis. The two BMF methods are used for comparison. First is M8 method from the BMDP statistical software package. The second is the BMF method, as suggested by Belohlavek and Vychodil (BVA2). These two are compared to Expectation Maximization Boolean Factor Analysis extended with binarization step developed here. Generated (Bars problem) and mushroom dataset are used for(experiments. In particular, under scrutiny was the reconstruction ability of the computed factors and the information gain as the measure of dimension reduction. In addition, presented are some general remarks on all the methods being compared.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132384186","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416616
G. JyothishChandran, Shajimon K. John
Data converters, ADCs and DACs, interface the real world of analog signals to the digital domain. They can be classified as `Nyquist rate converters' and `Over sampled converters'. Former operates at a sampling rate of twice the input signal frequency. They do not make use of the advantages of exceptional high speeds achieved in the current VLSI technology. Also the limitations in matching accuracy of the analog circuits needed in this type, limits their accuracy to an effective number of bits (ENOB) of 12 to 14 bits for various implementations. Over sampling data converters uses sampling rate much higher than Nyquist rate, typically higher by a factor between 8 and 512 or higher. They can achieve over 20 ENOB resolution at reasonably high conversion speeds. The engine behind this over sampling converter is a delta-sigma modulator. The main advantage of delta sigma modulator is that they offer a very good separation of input signal from the quantisation noise due to the over sampling process and noise shaping. The Signal to noise ratio (SNR) for a Nyquist rate converter depends on the number of bits of the converter. In this type SNR can be increased by approximately 6dB per bit. In over sampling converters the SNR depends on the depth of oversampling also, which is specified as `Oversampling ratio' (OSR). Theoretically, for each doubling in sampling rate SNR can be be improved by a factor of 3dB, which corresponds to a half bit increment in Nyquist rate converters. Thus without increasing chip area SNR is increased. In this paper a 10 bit delta sigma DAC is implemented and SNR was measured with various sinusoids at different over sampling ratios. To reduce the number of transistors in the implementation, Minimal energy dual bit adder (MEDB adder) is used.
{"title":"Implementation of an area efficient data converter with increased effective number of bits","authors":"G. JyothishChandran, Shajimon K. John","doi":"10.1109/ISDA.2012.6416616","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416616","url":null,"abstract":"Data converters, ADCs and DACs, interface the real world of analog signals to the digital domain. They can be classified as `Nyquist rate converters' and `Over sampled converters'. Former operates at a sampling rate of twice the input signal frequency. They do not make use of the advantages of exceptional high speeds achieved in the current VLSI technology. Also the limitations in matching accuracy of the analog circuits needed in this type, limits their accuracy to an effective number of bits (ENOB) of 12 to 14 bits for various implementations. Over sampling data converters uses sampling rate much higher than Nyquist rate, typically higher by a factor between 8 and 512 or higher. They can achieve over 20 ENOB resolution at reasonably high conversion speeds. The engine behind this over sampling converter is a delta-sigma modulator. The main advantage of delta sigma modulator is that they offer a very good separation of input signal from the quantisation noise due to the over sampling process and noise shaping. The Signal to noise ratio (SNR) for a Nyquist rate converter depends on the number of bits of the converter. In this type SNR can be increased by approximately 6dB per bit. In over sampling converters the SNR depends on the depth of oversampling also, which is specified as `Oversampling ratio' (OSR). Theoretically, for each doubling in sampling rate SNR can be be improved by a factor of 3dB, which corresponds to a half bit increment in Nyquist rate converters. Thus without increasing chip area SNR is increased. In this paper a 10 bit delta sigma DAC is implemented and SNR was measured with various sinusoids at different over sampling ratios. To reduce the number of transistors in the implementation, Minimal energy dual bit adder (MEDB adder) is used.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128865183","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416594
H. Hou, Tongge Xu, R. Zou
The high-speed development of the Internet has brought up a huge challenge for network management. Larger network traffic and more complex applications make it difficult for the original SNMP-based network management software to meet demands of analyzing users and protocols; widespread utility of dynamic ports greatly reduces the accuracy of distinguishing application layer protocols based on tranditional static ports. Since raw NetFlow is enormous, unfit for long-term storage and low in query efficiency, multiple time granularity aggregation schemes are designed. This paper describes characteristics of both real-time and history traffic from five aspects, thus providing a clearer view of network operating conditions. Besides, as the indicator of users' web access behavior, web sites and URLs are analyzed.
{"title":"Design and implementation of IP network behaviour analysis system based on NetFlow","authors":"H. Hou, Tongge Xu, R. Zou","doi":"10.1109/ISDA.2012.6416594","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416594","url":null,"abstract":"The high-speed development of the Internet has brought up a huge challenge for network management. Larger network traffic and more complex applications make it difficult for the original SNMP-based network management software to meet demands of analyzing users and protocols; widespread utility of dynamic ports greatly reduces the accuracy of distinguishing application layer protocols based on tranditional static ports. Since raw NetFlow is enormous, unfit for long-term storage and low in query efficiency, multiple time granularity aggregation schemes are designed. This paper describes characteristics of both real-time and history traffic from five aspects, thus providing a clearer view of network operating conditions. Besides, as the indicator of users' web access behavior, web sites and URLs are analyzed.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133720839","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416651
Kazuhiro Omura, Mineichi Kudo, Tomomi Endo, T. Murai
Recently we face classification problems with many categorical features, as seen in genetic data and text data. In this paper, we discuss some ways to give weights on features in the framework of naïve Bayes classifier, that is, under independent assumption of features. Because no order exists in a categorical feature, we consider a histogram over possible values (bins) in the feature. Taking into the difference of number of samples falling in each bin, we propose two kinds of weights: 1) one is derived from the probability that the majority class takes the majority even in samples, and 2) another reflects the expected conditional entropy. With the latter entropy weight, it will be shown that more discriminative features gain higher weights and non-discriminative feature diminishes as the number of samples goes infinity. We reveal the properties of these two kinds of weights through artificial data and some real-life data.
{"title":"Weighted naïve Bayes classifier on categorical features","authors":"Kazuhiro Omura, Mineichi Kudo, Tomomi Endo, T. Murai","doi":"10.1109/ISDA.2012.6416651","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416651","url":null,"abstract":"Recently we face classification problems with many categorical features, as seen in genetic data and text data. In this paper, we discuss some ways to give weights on features in the framework of naïve Bayes classifier, that is, under independent assumption of features. Because no order exists in a categorical feature, we consider a histogram over possible values (bins) in the feature. Taking into the difference of number of samples falling in each bin, we propose two kinds of weights: 1) one is derived from the probability that the majority class takes the majority even in samples, and 2) another reflects the expected conditional entropy. With the latter entropy weight, it will be shown that more discriminative features gain higher weights and non-discriminative feature diminishes as the number of samples goes infinity. We reveal the properties of these two kinds of weights through artificial data and some real-life data.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115352359","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416598
Ayman El-Kilany, Iman Saleh
In this paper, we investigate the problem of extractive single document summarization. We propose an unsupervised summarization method that is based on extracting and scoring keywords in a document and using them to find the sentences that best represent its content. Keywords are extracted and scored using clustering and dependency graphs of sentences. We test our method using different corpora including news, events and email corpora. We evaluate our method in the context of news summarization and email summarization tasks and compare the results with previously published ones.
{"title":"Unsupervised document summarization using clusters of dependency graph nodes","authors":"Ayman El-Kilany, Iman Saleh","doi":"10.1109/ISDA.2012.6416598","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416598","url":null,"abstract":"In this paper, we investigate the problem of extractive single document summarization. We propose an unsupervised summarization method that is based on extracting and scoring keywords in a document and using them to find the sentences that best represent its content. Keywords are extracted and scored using clustering and dependency graphs of sentences. We test our method using different corpora including news, events and email corpora. We evaluate our method in the context of news summarization and email summarization tasks and compare the results with previously published ones.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114888921","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416540
S. P. John
Mobile ad hoc networks play a vital role in the military tactical and other security-sensitive operations. Due to unreliable wireless media, host mobility and lack of infrastructure, providing secure communications is an immense challenge in this unique network environment. Specifically Key Distribution cryptographic techniques are typically applied in order to provide secure communications in wired and wireless networks. In Distributed Key Management Model Surplus memory usage and amplified Process Time were sufficiently needed. In order to address this current issue a novel one hop virtual group Shared Based Key Management system called is presented. It minimizes the number of key usage, memory usage and improves throughput; simultaneously acquiring increasing rate in Quality of Service. Experiment is made for the above supposed strategies wherein 10% to 20% rise is obtained in our proposed strategy.
{"title":"Novel one hop virtual group shared key management in mobile ad hoc networks","authors":"S. P. John","doi":"10.1109/ISDA.2012.6416540","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416540","url":null,"abstract":"Mobile ad hoc networks play a vital role in the military tactical and other security-sensitive operations. Due to unreliable wireless media, host mobility and lack of infrastructure, providing secure communications is an immense challenge in this unique network environment. Specifically Key Distribution cryptographic techniques are typically applied in order to provide secure communications in wired and wireless networks. In Distributed Key Management Model Surplus memory usage and amplified Process Time were sufficiently needed. In order to address this current issue a novel one hop virtual group Shared Based Key Management system called is presented. It minimizes the number of key usage, memory usage and improves throughput; simultaneously acquiring increasing rate in Quality of Service. Experiment is made for the above supposed strategies wherein 10% to 20% rise is obtained in our proposed strategy.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124597754","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}
Pub Date : 2012-11-01DOI: 10.1109/ISDA.2012.6416644
P. Conde, J. D. L. Calleja, A. Benítez, M. A. M. Nieto
In this paper we present experimental results of an automated method for image-based classification of diabetic retinopathy. The method is divided into three stages: image processing, feature extraction and image classification. In the first stage we have used two image processing techniques in order to enhance their features. Then, the second stage reduces the dimensionality of the images and finds features using the statistical method of principal component analysis. Finally, in the third stage the images are classified using machine learning algorithms, particularly, the naive Bayes classifier, neural networks, k-nearest neighbors and support vector machines. In our experimental study we classify two types of retinopathy: non-proliferative and proliferative. Preliminary results show that k-nearest neighbors obtained the best result with 68.7% using f-measure as metric, for a data set of 151 images with different resolutions.
{"title":"Image-based classification of diabetic retinopathy using machine learning","authors":"P. Conde, J. D. L. Calleja, A. Benítez, M. A. M. Nieto","doi":"10.1109/ISDA.2012.6416644","DOIUrl":"https://doi.org/10.1109/ISDA.2012.6416644","url":null,"abstract":"In this paper we present experimental results of an automated method for image-based classification of diabetic retinopathy. The method is divided into three stages: image processing, feature extraction and image classification. In the first stage we have used two image processing techniques in order to enhance their features. Then, the second stage reduces the dimensionality of the images and finds features using the statistical method of principal component analysis. Finally, in the third stage the images are classified using machine learning algorithms, particularly, the naive Bayes classifier, neural networks, k-nearest neighbors and support vector machines. In our experimental study we classify two types of retinopathy: non-proliferative and proliferative. Preliminary results show that k-nearest neighbors obtained the best result with 68.7% using f-measure as metric, for a data set of 151 images with different resolutions.","PeriodicalId":370150,"journal":{"name":"2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129525441","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：