Pub Date : 2010-04-21DOI: 10.1109/ICISA.2010.5480395
SeungJin Lim
As sensor networks become ubiquitous, the need for data mining of sensor network data is gaining momentum. Sensor network data is typically large, noisy and imbalanced, which makes it challenging to build a robust model from the data. In addition, traditional data mining often requires postmortem processing of the resulting statistically significant patterns to identify interesting patterns by means of visualization. For this reason, interactive visual data mining is employed for mining patterns from the fire detector dataset of the National Fire Incident Reporting System (NFIRS) database in this work. The suitability of interactive visual data mining, in place of its traditional counterpart, is demonstrated.
{"title":"Interactive Visual Data Mining of a Large Fire Detector Database","authors":"SeungJin Lim","doi":"10.1109/ICISA.2010.5480395","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480395","url":null,"abstract":"As sensor networks become ubiquitous, the need for data mining of sensor network data is gaining momentum. Sensor network data is typically large, noisy and imbalanced, which makes it challenging to build a robust model from the data. In addition, traditional data mining often requires postmortem processing of the resulting statistically significant patterns to identify interesting patterns by means of visualization. For this reason, interactive visual data mining is employed for mining patterns from the fire detector dataset of the National Fire Incident Reporting System (NFIRS) database in this work. The suitability of interactive visual data mining, in place of its traditional counterpart, is demonstrated.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123134735","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 : 2010-04-21DOI: 10.1109/ICISA.2010.5480357
Hyuncheol Kim, Wonhyuk Lee, Jeom-goo Kim
The explosive growth of Internet traffic has led to a dramatic increase in demand for data transmission capacity, which has spurred tremendous research activities in real-time traffic estimation and related methodologies. High-speed services and bulk transmission were available and various next-generation converged services are undergoing a change by various services. This paper presents improved real-time simulated traffic generation and verification schemes based on the actual traffic pattern analysis. For this, we analyzed traffic patterns of actual application system and generated simulated traffics. We also suggested scheme that verify similarity of simulated traffic and actual traffic.
{"title":"Real-Time Simulated Traffic Generation and Verification Methods Based on the Traffic Pattern Analysis","authors":"Hyuncheol Kim, Wonhyuk Lee, Jeom-goo Kim","doi":"10.1109/ICISA.2010.5480357","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480357","url":null,"abstract":"The explosive growth of Internet traffic has led to a dramatic increase in demand for data transmission capacity, which has spurred tremendous research activities in real-time traffic estimation and related methodologies. High-speed services and bulk transmission were available and various next-generation converged services are undergoing a change by various services. This paper presents improved real-time simulated traffic generation and verification schemes based on the actual traffic pattern analysis. For this, we analyzed traffic patterns of actual application system and generated simulated traffics. We also suggested scheme that verify similarity of simulated traffic and actual traffic.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122179733","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 : 2010-04-21DOI: 10.1109/ICISA.2010.5480553
Melisa Hajyvahabzadeh, Elina Eidkhani, S. A. Mortazavi, Alireza Nemaney Pour
This paper presents a new group key management protocol, CKC (Code for Key Calculation). This protocol is based on logical key hierarchy. When a new member joins the group, server sends only the group key for that member. Then, current members and the new member calculate the necessary keys using node codes and one-way hash function. Node code is a code which is assigned to each node of the key tree. Again at leave, server just sends the new group key to remaining members. By this key, members calculate necessary keys using node codes and one-way hash function. The security of the keys is based on one-wayness of hash function. The results show that CKC reduces computational and communication overhead and message size at join while it keeps minimized the overhead of key update at leave.
提出了一种新的组密钥管理协议CKC (Code for key Calculation)。该协议基于逻辑密钥层次结构。当新成员加入组时，服务器只发送该成员的组密钥。然后，当前成员和新成员使用节点代码和单向散列函数计算所需的密钥。节点代码是分配给键树的每个节点的代码。同样，在离开时，服务器只向剩余成员发送新的组密钥。通过此键，成员使用节点代码和单向散列函数计算必要的键。密钥的安全性基于哈希函数的单向性。结果表明，CKC在连接时减少了计算和通信开销以及消息大小，同时在离开时保持了最小的键更新开销。
{"title":"A New Group Key Management Protocol Using Code for Key Calculation: CKC","authors":"Melisa Hajyvahabzadeh, Elina Eidkhani, S. A. Mortazavi, Alireza Nemaney Pour","doi":"10.1109/ICISA.2010.5480553","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480553","url":null,"abstract":"This paper presents a new group key management protocol, CKC (Code for Key Calculation). This protocol is based on logical key hierarchy. When a new member joins the group, server sends only the group key for that member. Then, current members and the new member calculate the necessary keys using node codes and one-way hash function. Node code is a code which is assigned to each node of the key tree. Again at leave, server just sends the new group key to remaining members. By this key, members calculate necessary keys using node codes and one-way hash function. The security of the keys is based on one-wayness of hash function. The results show that CKC reduces computational and communication overhead and message size at join while it keeps minimized the overhead of key update at leave.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117075033","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 : 2010-04-21DOI: 10.1109/ICISA.2010.5480564
Xi Luo, Xu Ji, Myong-Soon Park
Traffic analysis attacks are passive attacks that try to deduce the traffic pattern based on the eavesdropped information. Through analyzing the packet traffic, it can deduce the location of strategic nodes, and then launch an active attack to those locations, such as DoS attack. Therefore, defending against a traffic analysis attack is to prevent the adversary from tracing the location of critical sensor nodes. Due to the open wireless communication media exposing the context information to adversaries, we cannot use traditional encryption and authentication to prevent the adversaries from eavesdropping on the wireless communication. In this paper, we propose three schemes to defend against the traffic analysis attacks. Firstly, a random routing scheme (RRS) is proposed to provide path diversity. Secondly, we combine RRS with a dummy packet injection scheme (DPIS) to confuse the adversary by tracing or tracing back the forwarded packet to reach the receiver or source. Finally, an anonymous communication scheme (ACS) is proposed to hide the identities of all nodes that participate in packets transmission. Through security analysis and simulation, we can see that our proposed schemes can efficiently defend against traffic analysis attacks, take less delivery time and achieve uniform energy consumption.
{"title":"Location Privacy against Traffic Analysis Attacks in Wireless Sensor Networks","authors":"Xi Luo, Xu Ji, Myong-Soon Park","doi":"10.1109/ICISA.2010.5480564","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480564","url":null,"abstract":"Traffic analysis attacks are passive attacks that try to deduce the traffic pattern based on the eavesdropped information. Through analyzing the packet traffic, it can deduce the location of strategic nodes, and then launch an active attack to those locations, such as DoS attack. Therefore, defending against a traffic analysis attack is to prevent the adversary from tracing the location of critical sensor nodes. Due to the open wireless communication media exposing the context information to adversaries, we cannot use traditional encryption and authentication to prevent the adversaries from eavesdropping on the wireless communication. In this paper, we propose three schemes to defend against the traffic analysis attacks. Firstly, a random routing scheme (RRS) is proposed to provide path diversity. Secondly, we combine RRS with a dummy packet injection scheme (DPIS) to confuse the adversary by tracing or tracing back the forwarded packet to reach the receiver or source. Finally, an anonymous communication scheme (ACS) is proposed to hide the identities of all nodes that participate in packets transmission. Through security analysis and simulation, we can see that our proposed schemes can efficiently defend against traffic analysis attacks, take less delivery time and achieve uniform energy consumption.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128512280","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 : 2010-04-21DOI: 10.1109/ICISA.2010.5480541
Dong-Chul Park
A new model called intuitive fuzzy c-means (IFCM) model is proposed for the segmentation of magnetic resonance image in this paper. Fuzzy c-means (FCM) is one of the most widely used clustering algorithms and assigns memberships to which are inversely related to the relative distance to the point prototypes that are cluster centers in the FCM model. In order to overcome the problem of outliers in data, several models including possibilistic c-means (PCM) and possibilistic-fuzzy cmeans (PFCM) models have been proposed. In IFCM, a new measurement called intuition level is introduced so that the intuition level helps to alleviate the effect of noise. Several numerical examples are first used for experiments to compare the clustering performance of IFCM with those of FCM, PCM, and PFCM. A practical magnetic resonance image data set is then used for image segmentation experiment. Results show that IFCM compares favorably to several clustering algorithms including the SOM, FCM, CNN, PCM, and PFCM models. Since IFCM produces cluster prototypes less sensitive to outliers and to the selection of involved parameters than the other algorithms, IFCM is a good candidate for data clustering and image segmentation problems.
{"title":"Intuitive Fuzzy C-Means Algorithm for MRI Segmentation","authors":"Dong-Chul Park","doi":"10.1109/ICISA.2010.5480541","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480541","url":null,"abstract":"A new model called intuitive fuzzy c-means (IFCM) model is proposed for the segmentation of magnetic resonance image in this paper. Fuzzy c-means (FCM) is one of the most widely used clustering algorithms and assigns memberships to which are inversely related to the relative distance to the point prototypes that are cluster centers in the FCM model. In order to overcome the problem of outliers in data, several models including possibilistic c-means (PCM) and possibilistic-fuzzy cmeans (PFCM) models have been proposed. In IFCM, a new measurement called intuition level is introduced so that the intuition level helps to alleviate the effect of noise. Several numerical examples are first used for experiments to compare the clustering performance of IFCM with those of FCM, PCM, and PFCM. A practical magnetic resonance image data set is then used for image segmentation experiment. Results show that IFCM compares favorably to several clustering algorithms including the SOM, FCM, CNN, PCM, and PFCM models. Since IFCM produces cluster prototypes less sensitive to outliers and to the selection of involved parameters than the other algorithms, IFCM is a good candidate for data clustering and image segmentation problems.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129898490","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 : 2010-04-21DOI: 10.1109/ICISA.2010.5480537
Masood Uzzafer
Risk measurement of the software projects is presented based on analogies with financial risk measure models. Financial risk measure models assign a monetary value to a risk event to gauge the financial impact of the risk event on the financial investment. Estimated software cost is modeled as a financial investment and financial risk measure models are adopted to measure the monetary loss due to a risk event associated with the estimate cost of a software project. Further risk measure models are utilized for resource contingency planning needed to combat the worst case risk events.
{"title":"A Financial Tool for Software Risk Measurement","authors":"Masood Uzzafer","doi":"10.1109/ICISA.2010.5480537","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480537","url":null,"abstract":"Risk measurement of the software projects is presented based on analogies with financial risk measure models. Financial risk measure models assign a monetary value to a risk event to gauge the financial impact of the risk event on the financial investment. Estimated software cost is modeled as a financial investment and financial risk measure models are adopted to measure the monetary loss due to a risk event associated with the estimate cost of a software project. Further risk measure models are utilized for resource contingency planning needed to combat the worst case risk events.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124012224","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 : 2010-04-21DOI: 10.1109/ICISA.2010.5480585
Seungkeun Lee, Daesung Lee
Our research targets collaborative environments with focus on mobility of application. As mobile and wireless technology advances people are no longer bound to their offices. Sharing context information thus becomes a vital part of collaborative environments. However, challenges such as heterogeneous devices, connectivity, and bandwidth arise due to the dynamic nature of distributed, mobile teams. We present a methodology for context modeling and employ a framework that reduces costs such as computing information and usage of network resources by transferring context at relevant levels of detail. At the same time, robustness of the system is improved by dealing with uncertain context information. Our framework is implemented on an OSGi container platform using Web services for communication means.
{"title":"Application Coordination Model for Mobile Ubiquitous Environment","authors":"Seungkeun Lee, Daesung Lee","doi":"10.1109/ICISA.2010.5480585","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480585","url":null,"abstract":"Our research targets collaborative environments with focus on mobility of application. As mobile and wireless technology advances people are no longer bound to their offices. Sharing context information thus becomes a vital part of collaborative environments. However, challenges such as heterogeneous devices, connectivity, and bandwidth arise due to the dynamic nature of distributed, mobile teams. We present a methodology for context modeling and employ a framework that reduces costs such as computing information and usage of network resources by transferring context at relevant levels of detail. At the same time, robustness of the system is improved by dealing with uncertain context information. Our framework is implemented on an OSGi container platform using Web services for communication means.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129964377","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 : 2010-04-21DOI: 10.1109/ICISA.2010.5480362
C. Douglas, Hyoseop Lee, D. Sheen
We introduce an inverse problem for the local volatility model in option pricing. We solve the problem using the Levenberg-Marquardt algorithm and use the notion of the Frechet derivative when calculating the Jacobian matrix. We analyze the existence of the Frechet derivative and its numerical computation. To reduce the computational time of the inverse problem, a GP-GPU environment is considered for parallel computation. Numerical results confirm the validity and efficiency of the proposed method.
{"title":"Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration","authors":"C. Douglas, Hyoseop Lee, D. Sheen","doi":"10.1109/ICISA.2010.5480362","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480362","url":null,"abstract":"We introduce an inverse problem for the local volatility model in option pricing. We solve the problem using the Levenberg-Marquardt algorithm and use the notion of the Frechet derivative when calculating the Jacobian matrix. We analyze the existence of the Frechet derivative and its numerical computation. To reduce the computational time of the inverse problem, a GP-GPU environment is considered for parallel computation. Numerical results confirm the validity and efficiency of the proposed method.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132965169","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 : 2010-04-21DOI: 10.1109/ICISA.2010.5480307
M. A. Khan, M. A. Khan, Z. Jan, Hamid Ali, Anwar M. Mirza
In protein fold recognition problem an effort is made to assign a fold to given proteins, this is of practical importance and has diverse application in the field of bioinformatics such as the discovery of new drugs, the individual implication of amino acid in a protein and bringing improvement in a specific protein function. In this paper, we have studied various machine learning techniques for protein fold recognition problem, and compared Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel and Multilayer Perceptron (MLP) on a number of measures like the recognition accuracy of protein fold, the 10-fold cross validation accuracies and Kappa statistics. These techniques are applied to the well known Structural Classification of Proteins (SCOP) dataset in extensive experimentations. In this study Multilayer Perceptron (MLP) shows better accuracy on single protein feature (C, S, H, P, V, Z) of the SCOP dataset as compared to Support Vector Machine (SVM). A plausible reason of the better performance of MLP is that it uses all the available data for classification where as the SVM model cannot exploit all the available data.
{"title":"Performance of Machine Learning Techniques in Protein Fold Recognition Problem","authors":"M. A. Khan, M. A. Khan, Z. Jan, Hamid Ali, Anwar M. Mirza","doi":"10.1109/ICISA.2010.5480307","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480307","url":null,"abstract":"In protein fold recognition problem an effort is made to assign a fold to given proteins, this is of practical importance and has diverse application in the field of bioinformatics such as the discovery of new drugs, the individual implication of amino acid in a protein and bringing improvement in a specific protein function. In this paper, we have studied various machine learning techniques for protein fold recognition problem, and compared Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel and Multilayer Perceptron (MLP) on a number of measures like the recognition accuracy of protein fold, the 10-fold cross validation accuracies and Kappa statistics. These techniques are applied to the well known Structural Classification of Proteins (SCOP) dataset in extensive experimentations. In this study Multilayer Perceptron (MLP) shows better accuracy on single protein feature (C, S, H, P, V, Z) of the SCOP dataset as compared to Support Vector Machine (SVM). A plausible reason of the better performance of MLP is that it uses all the available data for classification where as the SVM model cannot exploit all the available data.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131095068","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 : 2010-04-21DOI: 10.1109/ICISA.2010.5480411
M. A. Qureshi, A. Younus, F. Rojas
A web crawler forms the backbone of a search engine and this backbone needs a careful re- assessment that could enhance the efficiency of search engines. This paper conducts such a re- assessment from the perspective of systems and this is achieved through implementation and analysis of a web crawler "VisionerBOT" as a feed forward engine for search engines using the MapReduce distributed programming model. Our crawler implementations revisit the classical OS debate of threads vs. events, with a significant contribution from our work which concludes that events is the ideal way forward for web crawlers. Furthermore, in implementing the feed forward mechanisms within the web crawler, we came up with some important design considerations for the operating system research community which can lead to a whole new class of operating systems.
{"title":"Analyzing the Web Crawler as a Feed Forward Engine for an Efficient Solution to the Search Problem in the Minimum Amount of Time through a Distributed Framework","authors":"M. A. Qureshi, A. Younus, F. Rojas","doi":"10.1109/ICISA.2010.5480411","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480411","url":null,"abstract":"A web crawler forms the backbone of a search engine and this backbone needs a careful re- assessment that could enhance the efficiency of search engines. This paper conducts such a re- assessment from the perspective of systems and this is achieved through implementation and analysis of a web crawler \"VisionerBOT\" as a feed forward engine for search engines using the MapReduce distributed programming model. Our crawler implementations revisit the classical OS debate of threads vs. events, with a significant contribution from our work which concludes that events is the ideal way forward for web crawlers. Furthermore, in implementing the feed forward mechanisms within the web crawler, we came up with some important design considerations for the operating system research community which can lead to a whole new class of operating systems.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123677215","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：