Qiuyan Zhong, Xiaonan Zhang, Su Guo, Xin Ye, Jiangnan Qiu
How to make scientific and effective emergency decisions is a research hotspot in academic circles at present. This paper applies case-based reasoning to emergency aid decision-making, which provides a method of scientific and effective aid decision-making for the emergency leaders. The two-layer case retrieving algorithm based on structural similarity degree and attribute similarity degree is designed, which effectively overcomes the shortcomings of traditional Nearest Neighbor Algorithm that fails to calculate the similarities between the cases with the missing values. The application of case retrieving algorithm to the field of typhoon analysis is used to illustrate the practicality of case retrieving in emergency aid decision-making.
{"title":"The Method of Case Retrieving in the Emergency Field Based on CBR","authors":"Qiuyan Zhong, Xiaonan Zhang, Su Guo, Xin Ye, Jiangnan Qiu","doi":"10.1109/WI-IAT.2010.112","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.112","url":null,"abstract":"How to make scientific and effective emergency decisions is a research hotspot in academic circles at present. This paper applies case-based reasoning to emergency aid decision-making, which provides a method of scientific and effective aid decision-making for the emergency leaders. The two-layer case retrieving algorithm based on structural similarity degree and attribute similarity degree is designed, which effectively overcomes the shortcomings of traditional Nearest Neighbor Algorithm that fails to calculate the similarities between the cases with the missing values. The application of case retrieving algorithm to the field of typhoon analysis is used to illustrate the practicality of case retrieving in emergency aid decision-making.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129456176","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}
This paper presents a method for refining Chinese noun metaphor knowledge base, using two kinds of resources of Grammatical Knowledge Base of Contemporary Chinese (GKB) and Chinese Concept Dictionary(CCD). This utilizes the uniqueness of the storage number in the concept of the CCD and builds on the mapping relations from a source domain to most target domains. At the same time, the description specification of Chinese metaphor knowledge base also inherits some attributives of GKB. In addition, we conduct recognition experiment of noun metaphorical patterns by using knowledge base information. We show that the efficiency on the noun metaphor knowledge base has been proved for the recognition task.
{"title":"The Chinese Noun Metaphors Knowledge Base and its Use in the Recognition of Metaphors","authors":"Zhimin Wang, Shiwen Yu, Zhifang Sui","doi":"10.1109/WI-IAT.2010.168","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.168","url":null,"abstract":"This paper presents a method for refining Chinese noun metaphor knowledge base, using two kinds of resources of Grammatical Knowledge Base of Contemporary Chinese (GKB) and Chinese Concept Dictionary(CCD). This utilizes the uniqueness of the storage number in the concept of the CCD and builds on the mapping relations from a source domain to most target domains. At the same time, the description specification of Chinese metaphor knowledge base also inherits some attributives of GKB. In addition, we conduct recognition experiment of noun metaphorical patterns by using knowledge base information. We show that the efficiency on the noun metaphor knowledge base has been proved for the recognition task.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129696083","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}
Efficient metadata management is critical for distributed file system in cloud computing. In this paper we propose a new metadata management scheme which employs master metadata server (MMDS) and metadata look-up table server between the metadata servers and clients. The MMDS checks the state of MDSs for load-balancing, and thereby avoids hot spot. The proposed scheme significantly reduces the network traffic as well.
{"title":"Enhancing the Performance of Metadata Service for Cloud Computing","authors":"M. Hwang, Dae-Gun Kim, H. Youn","doi":"10.1109/WI-IAT.2010.188","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.188","url":null,"abstract":"Efficient metadata management is critical for distributed file system in cloud computing. In this paper we propose a new metadata management scheme which employs master metadata server (MMDS) and metadata look-up table server between the metadata servers and clients. The MMDS checks the state of MDSs for load-balancing, and thereby avoids hot spot. The proposed scheme significantly reduces the network traffic as well.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"41 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114010966","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}
In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context patterns. The label propagation algorithm is a graph based semi-supervised learning method which models the entire data set as a weighted graph and the label score is propagated on this graph. We test the proposed framework with four relationships, the results show that the label propagation is quite competitive comparing with existing methods.
{"title":"Relations Expansion: Extracting Relationship Instances from the Web","authors":"Haibo Li, Y. Matsuo, M. Ishizuka","doi":"10.1109/WI-IAT.2010.269","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.269","url":null,"abstract":"In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context patterns. The label propagation algorithm is a graph based semi-supervised learning method which models the entire data set as a weighted graph and the label score is propagated on this graph. We test the proposed framework with four relationships, the results show that the label propagation is quite competitive comparing with existing methods.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114760554","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}
Yuan Zhang, Ning Zhang, Jie Tang, Jinghai Rao, Wenbin Tang
Mobile is becoming a ubiquitous platform for context-aware intelligent computing. One fundamental but usually ignored issue is how to efficiently manage (e.g., index and query) the mobile context data. To this end, we present a unified framework and have developed a toolkit, referred to as MQuery. More specifically, the mobile context data is represented in the standard RDF (Resource Description Framework) format. We propose a compressed-index method which takes less than 50% of the memory cost (of the traditional method) to index the context data. Four query interfaces have been developed for efficiently querying the context data including: instance query, neighbor query, shortest path query, and connection subgraph query. Experimental results on two real datasets demonstrate the efficiency of MQuery.
{"title":"MQuery: Fast Graph Query via Semantic Indexing for Mobile Context","authors":"Yuan Zhang, Ning Zhang, Jie Tang, Jinghai Rao, Wenbin Tang","doi":"10.1109/WI-IAT.2010.137","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.137","url":null,"abstract":"Mobile is becoming a ubiquitous platform for context-aware intelligent computing. One fundamental but usually ignored issue is how to efficiently manage (e.g., index and query) the mobile context data. To this end, we present a unified framework and have developed a toolkit, referred to as MQuery. More specifically, the mobile context data is represented in the standard RDF (Resource Description Framework) format. We propose a compressed-index method which takes less than 50% of the memory cost (of the traditional method) to index the context data. Four query interfaces have been developed for efficiently querying the context data including: instance query, neighbor query, shortest path query, and connection subgraph query. Experimental results on two real datasets demonstrate the efficiency of MQuery.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126527722","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}
P. Paruchuri, Pradeep Varakantham, K. Sycara, P. Scerri
As human-agent teams get increasingly deployed in the real-world, agent designers need to take into account that humans and agents have different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resources impacts the performance of large, heterogeneous teams. In particular, we model the inclination of humans to simplify their preference functions and to exaggerate their utility for desired resources, and show the effect of these biases on the team performance. We demonstrate this on two different problems, which are representative of many resource allocation problems addressed in literature. In both these problems, the agents and humans optimize their constraints in a distributed manner. This paper makes two key contributions: (a) Proves theoretical properties of the algorithm used (named DSA) for solving distributed constraint optimization problems, which ensures robustness against human biases; and (b) Empirically illustrates that the effect of human biases on team performance for different problem settings and for varying team sizes is not significant. Both our theoretical and empirical studies support the fact that the solutions provided by DSA for mid to large sized teams are very robust to the common types of human biases.
{"title":"Effect of Human Biases on Human-Agent Teams","authors":"P. Paruchuri, Pradeep Varakantham, K. Sycara, P. Scerri","doi":"10.1109/WI-IAT.2010.104","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.104","url":null,"abstract":"As human-agent teams get increasingly deployed in the real-world, agent designers need to take into account that humans and agents have different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resources impacts the performance of large, heterogeneous teams. In particular, we model the inclination of humans to simplify their preference functions and to exaggerate their utility for desired resources, and show the effect of these biases on the team performance. We demonstrate this on two different problems, which are representative of many resource allocation problems addressed in literature. In both these problems, the agents and humans optimize their constraints in a distributed manner. This paper makes two key contributions: (a) Proves theoretical properties of the algorithm used (named DSA) for solving distributed constraint optimization problems, which ensures robustness against human biases; and (b) Empirically illustrates that the effect of human biases on team performance for different problem settings and for varying team sizes is not significant. Both our theoretical and empirical studies support the fact that the solutions provided by DSA for mid to large sized teams are very robust to the common types of human biases.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125455833","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}
This paper investigates the behavior of users judging the similarity of documents from the viewpoint of user feedback cost, in particular judgment time and accuracy. An experiment is conducted, in which 21 test participants were asked to judge the similarity of documents. As the clue for the judgment, 3 types of information: original text, snippet, and term, are mutually provided. The judgment accuracy and judgment time are analyzed using analysis of variance (ANOVA) and multiple comparison tests to examine the difference of snippet, term and text. The result shows that displaying term is the best in terms of time cost, whereas the judgment accuracy when a snippet is provided is improved with experience. The obtained result will contribute to the design of interfaces that can minimize the user’s feedback cost.
{"title":"Analysis of User Feedback Cost for Document Similarity Judgment","authors":"Minghuang Chen, S. Yamada, Y. Takama","doi":"10.1109/WI-IAT.2010.116","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.116","url":null,"abstract":"This paper investigates the behavior of users judging the similarity of documents from the viewpoint of user feedback cost, in particular judgment time and accuracy. An experiment is conducted, in which 21 test participants were asked to judge the similarity of documents. As the clue for the judgment, 3 types of information: original text, snippet, and term, are mutually provided. The judgment accuracy and judgment time are analyzed using analysis of variance (ANOVA) and multiple comparison tests to examine the difference of snippet, term and text. The result shows that displaying term is the best in terms of time cost, whereas the judgment accuracy when a snippet is provided is improved with experience. The obtained result will contribute to the design of interfaces that can minimize the user’s feedback cost.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132408281","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}
This paper presents an extension to the Rule-Based Similarity (RBS) model -- a novel rough set approach to the problem of learning a similarity relation from data. The original model, proposed in [1], applied the notion of Tversky's feature contrast model in a rough set framework to facilitate an accurate case-based classification. In the dynamic RBS model, a dynamic reducts technique is used to broaden the scope of the considered similarity aspects. This is especially important when dealing with objects described by numerous attributes. The extended model was tested on several microarray datasets from RSCTC'2010 Discovery Challenge. The results proved that it is significantly more accurate than the original RBS as well as some other popular classification algorithms, such as the emph{random forest} or $k$-NN combined with several attribute selection methods.
{"title":"Utilization of Dynamic Reducts to Improve Performance of the Rule-Based Similarity Model for Highly-Dimensional Data","authors":"Andrzej Janusz","doi":"10.1109/WI-IAT.2010.118","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.118","url":null,"abstract":"This paper presents an extension to the Rule-Based Similarity (RBS) model -- a novel rough set approach to the problem of learning a similarity relation from data. The original model, proposed in [1], applied the notion of Tversky's feature contrast model in a rough set framework to facilitate an accurate case-based classification. In the dynamic RBS model, a dynamic reducts technique is used to broaden the scope of the considered similarity aspects. This is especially important when dealing with objects described by numerous attributes. The extended model was tested on several microarray datasets from RSCTC'2010 Discovery Challenge. The results proved that it is significantly more accurate than the original RBS as well as some other popular classification algorithms, such as the emph{random forest} or $k$-NN combined with several attribute selection methods.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130075232","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}
Pierre Monier, Arnaud Doniec, S. Piechowiak, R. Mandiau
Many algorithms to solve Distributed Constraint Satisfaction Problems (DisCSP) have been introduced in the literature. In this paper, we propose to compare three different algorithms to solve DisCSP. Contrary to algorithms of the literature which are evaluated on graph coloring problems or uniform random binary DisCSPs, we use a multi-robot exploration problem. We show that, for this real world application, the comparison of algorithms may be improved by using additional metrics than those used in the literature. We will define other metrics that can be used for measuring different aspects of the multi-robot exploration problem. The aim of our attempt for defining metrics is to analyze and compare different aspects of complexity of this multi-robot problem. We will observe that using both classical and real world metrics is interesting to obtain a better and more precise comparison.
{"title":"Metrics for the Evaluation of DisCSP: Some Experiments on Multi-robot Exploration","authors":"Pierre Monier, Arnaud Doniec, S. Piechowiak, R. Mandiau","doi":"10.1109/WI-IAT.2010.71","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.71","url":null,"abstract":"Many algorithms to solve Distributed Constraint Satisfaction Problems (DisCSP) have been introduced in the literature. In this paper, we propose to compare three different algorithms to solve DisCSP. Contrary to algorithms of the literature which are evaluated on graph coloring problems or uniform random binary DisCSPs, we use a multi-robot exploration problem. We show that, for this real world application, the comparison of algorithms may be improved by using additional metrics than those used in the literature. We will define other metrics that can be used for measuring different aspects of the multi-robot exploration problem. The aim of our attempt for defining metrics is to analyze and compare different aspects of complexity of this multi-robot problem. We will observe that using both classical and real world metrics is interesting to obtain a better and more precise comparison.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134104884","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}
A biologically inspired cognitive model is presented for human decision making and applied to the simulation of the web user. The model is based on the Neurophysiology description of multiple decision process; this is a well proven psychological theory. The model simulates the behaviour of a real user on a website and it was observed that the distribution of artificial web users in sessions successfully simulates a genuine user’s web mode of behaviour. On the hypothesis that the adjusted artificial web user behaves statistically similar to the human web users, a system was created for the improvement of the structure of a web site based on stochastic simulations as a Proof of Concept. Since simulation recover observed statistical behaviour, changes on a web site are used to predict changes on navigational patterns.
{"title":"Stochastic Simulation of Web Users","authors":"P. Román, J. D. Velásquez","doi":"10.1109/WI-IAT.2010.60","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.60","url":null,"abstract":"A biologically inspired cognitive model is presented for human decision making and applied to the simulation of the web user. The model is based on the Neurophysiology description of multiple decision process; this is a well proven psychological theory. The model simulates the behaviour of a real user on a website and it was observed that the distribution of artificial web users in sessions successfully simulates a genuine user’s web mode of behaviour. On the hypothesis that the adjusted artificial web user behaves statistically similar to the human web users, a system was created for the improvement of the structure of a web site based on stochastic simulations as a Proof of Concept. Since simulation recover observed statistical behaviour, changes on a web site are used to predict changes on navigational patterns.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"344 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133896622","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：