2014 IEEE 26th International Conference on Tools with Artificial Intelligence最新文献_第4页

A Syntactic Approach to Revising Epistemic States with Uncertain Inputs 不确定输入下认知状态修正的句法方法

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.32

Kim Bauters, Weiru Liu, Jun Hong, L. Godo, C. Sierra

引用次数: 1

Text Summarization Based on Sentence Selection with Semantic Representation 基于语义表示的句子选择的文本摘要

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.93

Chi Zhang, Lei Zhang, Chong-Jun Wang, Junyuan Xie

{"title":"Text Summarization Based on Sentence Selection with Semantic Representation","authors":"Chi Zhang, Lei Zhang, Chong-Jun Wang, Junyuan Xie","doi":"10.1109/ICTAI.2014.93","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.93","url":null,"abstract":"Text summarization is of great importance to solve information overload. Salience and coverage are two most important issues for summaries. Most existing models extract summaries by selecting the top sentences with highest scores without using the relationships between sentences, and usually represent the sentences simply basing on lexical or statistical features. As a result, those models can not achieve salience or coverage very well. In this paper, we propose a novel summarization model called Sentence Selection with Semantic Representation (SSSR). SSSR ensures both salience and coverage by learning semantic representations for sentences and applying a well-designed selection strategy to select summary sentences. The selection strategy used in SSSR is to select sentences that can reconstruct the original document with least distortion by means of linear combination. Besides, we improve our selection strategy by reducing redundant information. Then we learn two semantic representations for sentences: (1) weighted mean of word embeddings, (2) deep coding. Both of them are semantic and compact, and can capture similarities between sentences. Extensive experiments on datasets DUC2006 and DUC2007 validate our model.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121117880","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: 8

Building Portfolios for Parallel Constraint Solving by Varying the Local Consistency Applied 基于局部一致性的并行约束求解组合

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.112

M. Dasygenis, Kostas Stergiou

{"title":"Building Portfolios for Parallel Constraint Solving by Varying the Local Consistency Applied","authors":"M. Dasygenis, Kostas Stergiou","doi":"10.1109/ICTAI.2014.112","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.112","url":null,"abstract":"Portfolio based approaches to constraint solving aim at exploiting the variability in performance displayed by different solvers or different parameter settings of a single solver. Such approaches have been quite successful in both a sequential and a parallel processing mode. Given the increasingly larger number of available processors for parallel processing, an important challenge when designing portfolios is to identify solver parameters that offer diversity in the exploration of the search space and to generate different solver configurations by automatically tuning these parameters. In this paper we propose, for the first time, a way to build porfolios for parallel solving by parameter zing the local consistency property applied during search. To achieve this we exploit heuristics for adaptive propagation proposed in stergiou08. We show how this approach can result in the easy automatic generation of portfolios that display large performance variability. We make an experimental comparison against a standard sequential solver as well as portfolio based methods that use randomization of the variable ordering heuristic as the source of diversity. Results demonstrate that our method constantly outperforms the sequential solver and in most cases it is more efficient than the other portfolio approaches.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116453664","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: 3

Knowledge Transfer for Reducing Calibration Time in Brain-Computer Interfacing 减少脑机接口校准时间的知识转移

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.100

Sami Dalhoumi, G. Dray, J. Montmain

{"title":"Knowledge Transfer for Reducing Calibration Time in Brain-Computer Interfacing","authors":"Sami Dalhoumi, G. Dray, J. Montmain","doi":"10.1109/ICTAI.2014.100","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.100","url":null,"abstract":"Reducing calibration time while maintaining good classification accuracy has been one of the most challenging problems in electroencephalography (EEG) -based brain-computer interfaces (BCIs) research during the last years. Most of machine learning approaches that have been attempted to address this issue are based on knowledge transfer between different BCIs users. Assuming that there is a common underlying data generating process, they try to learn a subject-independent classification model from multiple users in order to classify data of future users. In this paper, we propose a novel approach that allows inter-subjects classification of EEG signals without relying on the strong assumptions considered in previous work. It consists of learning a prediction model of a new BCI user through an ensemble of classifiers where base classifiers are trained on data from other users separately and weighted according to the performance of the ensemble on few labeled data of the new user. Evaluation on real EEG data showed that our approach allows achieving good classification accuracy when the size of calibration set is small.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127959952","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: 20

An Application of Adapted A* Decentralized Approach for On Demand Transportation Problem 适应A*分散方法在随需应变交通问题中的应用

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.141

Mohamad El Falou, A. Malas, M. Itmi, S. E. Falou, A. Cardon

引用次数: 5

Tri-Rank: An Authority Ranking Framework in Heterogeneous Academic Networks by Mutual Reinforce 三阶:异质学术网络中相互强化的权威排序框架

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.80

Zhirun Liu, Heyan Huang, Xiaochi Wei, Xian-Ling Mao

引用次数: 26

SAT-Based Approaches to Treewidth Computation: An Evaluation 基于sat的树宽计算方法:评价

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.57

J. Berg, M. Järvisalo

引用次数: 33

Intelligent Management of Brain Markers for Early Prognosis of the Spatiotemporal Growth of Gliomas 脑标记物智能管理对胶质瘤时空生长的早期预后的影响

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.102

Petros Toumpaniaris, D. Verganelakis, N. Spanoudakis, M. Zervakis

引用次数: 0

Agent Division and Fusion for Task Execution in Undependable Multiagent Systems 非可靠多智能体系统中任务执行的智能体划分与融合

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.66

Yifeng Zhou, Yichuan Jiang

{"title":"Agent Division and Fusion for Task Execution in Undependable Multiagent Systems","authors":"Yifeng Zhou, Yichuan Jiang","doi":"10.1109/ICTAI.2014.66","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.66","url":null,"abstract":"In multiagent systems, agents with limited capacity often cooperate in order to accomplish various types of tasks. Due to the openness of multiagent systems, agents may run the risk of their cooperations to accomplish tasks because of some involved undependable agents. The state of the art for handling this problem concentrates on the phase of task allocation, which aims to allocate tasks to more dependable agents (task allocation-oriented). In fact, accomplishing a task in a multiagent system requires two phases: i) task allocation, and ii) task execution. Hence, this paper, on the contrary, focuses on managing the phase of task execution to guarantee the performance of task accomplishment (task execution-oriented). To reduce the performance loss caused by undependable agents, the proposed agent division and fusion mechanism enables agents to autonomously divide themselves into sub-agents for executing tasks with different risks (more resources will be assigned to the sub-agent with lower risk in task execution), then the sub-agents can also fuse together and make a re-division to fit the current task environments. This work is also expected to be able to complement the existing state of the art (task allocation-oriented) for guaranteeing the performance of task accomplishment in undependable multiagent systems from task execution-oriented perspective.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"26 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134281032","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: 0

Asynchronous Computing of a Discrete Voronoi Diagram on a Cellular Automaton Using 1-Norm: Application to Roadmap Extraction 元胞自动机上离散Voronoi图的1-范数异步计算:在路线图提取中的应用

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.130

Nassim Kaldé, Olivier Simonin, F. Charpillet

引用次数: 2