2012 IEEE 24th International Conference on Tools with Artificial Intelligence最新文献_第4页

On Effective XML Clustering by Path Commonality: An Efficient and Scalable Algorithm 基于路径共性的有效XML聚类:一种高效的可扩展算法

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.60

G. Costa, R. Ortale

{"title":"On Effective XML Clustering by Path Commonality: An Efficient and Scalable Algorithm","authors":"G. Costa, R. Ortale","doi":"10.1109/ICTAI.2012.60","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.60","url":null,"abstract":"XML clustering by structure is, in its most general form, the process of partitioning a corpus of XML documents into disjoint clusters, such that intra-cluster structural homogeneity is high and inter-cluster structural homogeneity is low. In this paper, we propose an algorithm that implements a partitioning strategy, in which root-to-leaf paths are used to separate the XML documents. Paths are discriminatory substructures and, thus, the effectiveness of our algorithm is accordingly high. Moreover, a suitable encoding is adopted for representing and testing the occurrence of the individual paths within each XML document independently of the length of such paths. Not only this expedites clustering, but it also makes our algorithm scalable to process large-scale corpora of XML documents. A comparative evaluation over several standard (real-word and synthetic) XML corpora reveals that our algorithm outperforms all of its competitors in efficiency and scalability, while being as effective as the top-notch competitors. One especially appealing property of the proposed algorithm is that it achieves these levels of performance by automatically establishing a natural number of clusters to be discovered in the underlying XML corpus.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129542225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Geometric Construction Problem Solving in Computer-Aided Learning 计算机辅助学习中的几何构造问题求解

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.162

P. Schreck, P. Mathis, Julien Narboux

引用次数: 3

User Modeling on Communication Characteristics Using Machine Learning in Computer-Supported Collaborative Multiple Language Learning 计算机支持的协同多语言学习中使用机器学习的用户通信特征建模

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.154

M. Virvou, Efthymios Alepis, C. Troussas

引用次数: 2

Revising Qualitative Constraint Networks: Definition and Implementation 修正定性约束网络:定义与实现

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.80

J. Hué, Matthias Westphal

引用次数: 6

A New Exploration Method Based on Multi-layer Evidence Grid Map (MLEGM) and Improved A* Algorithm for Mobile Robots 基于多层证据网格图(MLEGM)和改进A*算法的移动机器人探索新方法

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.134

E. Esmaeili, V. Azizi, S. Samizadeh, Sajjad Ziyadloo, M. Meybodi

{"title":"A New Exploration Method Based on Multi-layer Evidence Grid Map (MLEGM) and Improved A* Algorithm for Mobile Robots","authors":"E. Esmaeili, V. Azizi, S. Samizadeh, Sajjad Ziyadloo, M. Meybodi","doi":"10.1109/ICTAI.2012.134","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.134","url":null,"abstract":"An efficient exploration of unknown environments is a fundamental problem in mobile robots. This paper proposes a new exploration method, in this method each specific area in environment is considered as a cell that these cells are represented by 3 abstract layers. The value of each cell in first layer is calculated by range finder's free beams. In other layers, the value of each cell is calculated by visual information, the information is received by other sensors' data and image processing that used in potential filed algorithm. We merge the value of these layers to have a single meaning value. We can use this value in many purposes e.g. finding optimal path for exploration or using this value as reward for learning methods. Then it mixed with a new improved version of A* algorithm that introduces for first time to find optimal path in unknown areas. This method implemented in official simulator of Virtual Robots League in Robocop competitions and compared with random search method. The simulation result of this method covers more unknown area compared to last methods.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126929574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multiclass Semi-supervised Learning for Animal Behavior Recognition from Accelerometer Data 基于加速度计数据的动物行为识别的多类半监督学习

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.98

J. Tanha, M. Someren, M. D. Bakker, Willem Bouten, J. Shamoun‐Baranes, H. Afsarmanesh

{"title":"Multiclass Semi-supervised Learning for Animal Behavior Recognition from Accelerometer Data","authors":"J. Tanha, M. Someren, M. D. Bakker, Willem Bouten, J. Shamoun‐Baranes, H. Afsarmanesh","doi":"10.1109/ICTAI.2012.98","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.98","url":null,"abstract":"In this paper we present a new Multiclass semi-supervised learning algorithm that uses a base classifier in combination with a similarity function applied to all data to find a classifier that maximizes the margin and consistency over all data. A novel multiclass loss function is presented and used to derive the algorithm. We apply the algorithm to animal behavior recognition from accelerometer data. Animal-borne accelerometer data are collected from free-ranging animals and then labeled by a human expert. The resulting data are used to train a classifier. However, labeling is not easy from accelerometer data only and it is often not feasible to observe animals fitted with an accelerometer. All current approaches to this behavior recognition task use supervised or unsupervised learning. Since unlabeled data are easy to acquire and collect, a semi-supervised approach seems appropriate and reduces the human efforts for labeling. Experiments with accelerometer data collected from free-ranging gulls and benchmark UCI datasets show that the algorithm is effective and compares favorably with existing algorithms for multiclass semi-supervised learning.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124347358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Interleaved Asynchronous Arc Consistency in Distributed Constraint Networks 分布式约束网络中的交错异步弧一致性

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.32

Saida Hammoujan, E. Bouyakhf, Imade Benelallam

引用次数: 0

Dynamic Path Consistency for Spatial Reasoning 空间推理的动态路径一致性

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.142

Lamia Belouaer, M. Bouzid, Malek Mouhoub

{"title":"Dynamic Path Consistency for Spatial Reasoning","authors":"Lamia Belouaer, M. Bouzid, Malek Mouhoub","doi":"10.1109/ICTAI.2012.142","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.142","url":null,"abstract":"Dealing with spatial knowledge requires the consistency of spatial information. This consistency is usually enforced by constraint satisfaction techniques including constraint propagation through arc and path consistency. While theses techniques often assume that spatial information are static, this is in general not the case in the real world. Our goal is to propose an approach to maintain the consistency of spatial knowledge in a dynamic environment. To our best knowledge no work in spatial reasoning has addressed this issue. In this paper we use a spatial ontology called Space Ontology to describe both objects and spatial relations namely topological and distance relations between these objects. Based on a dynamic path consistency algorithm, our proposed method maintains the consistency of spatial information after adding new instances of topological relations described by Space Ontology of a given environment. In order to evaluate the performance of our dynamic path consistency method, we conducted several tests on instantiations of Space Ontology in addition to randomly generated spatial constraint problems. The results of these tests demonstrate the efficiency of our method to deal with large size problems in a dynamic environment.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131928330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Link Prediction in Complex Networks by Supervised Rank Aggregation 基于监督秩聚合的复杂网络链路预测

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.111

Manisha Pujari, R. Kanawati

{"title":"Link Prediction in Complex Networks by Supervised Rank Aggregation","authors":"Manisha Pujari, R. Kanawati","doi":"10.1109/ICTAI.2012.111","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.111","url":null,"abstract":"Link prediction is a central task in the field of dynamic complex network analysis. A major trend in this area consists of applying a dyadic topological approach. Most of existing approaches apply machine learning algorithms where the link prediction problem is converted into a binary classification task. In this work, we propose a new dyadic topological link prediction approach applying supervised social choice algorithm. Given a training graph observed over a period [t0, t0'], this interval is divided into two sub-intervals: the learning interval and the labeling one. For each unlinked couple of vertices in the learning interval, a topological feature vector is computed. The labeling interval is used to fix the class of each example (e.g. linking, not-linking). Instead of learning a classification model as it is the case when applying machine learning approaches, we use these data to learn weights to associate to each computed feature based on the ability of each attribute to predict observed links. These weights are then used within weighted/supervised computational social choice algorithms to predict new links at time t > t0'. Two weighting schemes are experimented. We introduce weighted social choice rules by modifying classical voting approaches, namely: the Borda rule and the Kemeny aggregation rule. We also introduce our own concept of finding weights. We have implemented our approach on an academic coauthoring dataset (DBLP dataset). The preliminary results have been quite good, so we are working further on experimentation.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134158778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Tagging Choreographic Data for Data Mining and Classification 用于数据挖掘和分类的编舞数据标记

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.102

Catalina-Anca Ioan, Julien Velcin, Stefan Trausan-Matu

引用次数: 2