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2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献

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Multi-Robot Patrolling in Wireless Sensor Networks Using Bounded Cycle Coverage 基于有界周期覆盖的无线传感器网络多机器人巡逻
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0035
Mihai-Ioan Popescu, H. Rivano, Olivier Simonin
{"title":"Multi-Robot Patrolling in Wireless Sensor Networks Using Bounded Cycle Coverage","authors":"Mihai-Ioan Popescu, H. Rivano, Olivier Simonin","doi":"10.1109/ICTAI.2016.0035","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0035","url":null,"abstract":"Patrolling is mainly used in situations where the need of repeatedly visiting certain places is critical. In this paper, we consider a deployment of a wireless sensor network (WSN) that cannot be fully meshed because of the distance or obstacles. Several robots are then in charge of getting close enough to the nodes in order to connect to them, and perform a patrol to collect all the data in time. We discuss the problem of multi-robot patrolling within the constrained wireless networking settings. We show that this is fundamentally a problem of vertex coverage with bounded simple cycles (CBSC). We offer a formalization of the CBSC problem and prove it is NP-hard and at least as hard as the Traveling Salesman Problem (TSP). Then, we provide and analyze heuristics relying on clusterings and geometric techniques. The performances of our solutions are assessed in regards to networking parameters, robot energy, but also to random and particular graph models.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"15 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131759243","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: 11
A Fast Manifold Learning Algorithm for Dimensionality Reduction 一种快速的流形降维学习算法
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0152
Yu Liang, S. Furao, Jinxi Zhao, Yi Yang
{"title":"A Fast Manifold Learning Algorithm for Dimensionality Reduction","authors":"Yu Liang, S. Furao, Jinxi Zhao, Yi Yang","doi":"10.1109/ICTAI.2016.0152","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0152","url":null,"abstract":"This paper proposes a new manifold learning method called \"Soinnmanifold\". Traditional manifold learning method needs a lot of computation and appropriate priori parameters. This has somewhat restricted the domains in which manifold learning can potentially be applied. However, with the high-dimensional inputs, our method can generate a lowdimensional manifold in the high-dimensional space and determine the intrinsic dimension automatically. Then we will use this manifold to do dimensionality reduction quickly. Experiments demonstrate that our method can get promising results with less time and memory.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131286347","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
Learning Markov Blanket Bayesian Network for Big Data in MapReduce 基于MapReduce的大数据马尔可夫毛毯贝叶斯网络学习
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0138
Yuxin Che, Shaohui Hong, Defu Zhang, Liming Zhang
{"title":"Learning Markov Blanket Bayesian Network for Big Data in MapReduce","authors":"Yuxin Che, Shaohui Hong, Defu Zhang, Liming Zhang","doi":"10.1109/ICTAI.2016.0138","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0138","url":null,"abstract":"A challenge task of data mining is to process massive data in the big data era. MapReduce is an attractive model to overcome this challenge. This paper presents a new method to accelerate the process of learning Markov blanket Bayesian network(MBBN). Markov blanket is a better model type of Bayesian network in some complex datasets. The time and space cost of learning Markov blanket is large, and grows fast as the variables increase. Large amounts of data are needed for its independence test which makes the problem harder. The statistical phase and independence test are parallelized to make it find an appropriate relation among variables in the MapReduce framework. Computational results are reported by testing four datasets and show that the speed-up can be obtained by means of MapReduce. In particular, the Markov blanket in MapReduce has higher accuracy rate than naïve Bayesian and tree-augmented naïve Bayesian.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132720073","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
RakSOR: Ranking of Ontology Reasoners Based on Predicted Performances RakSOR:基于预测性能的本体推理器排序
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0165
N. Alaya, S. Yahia, M. Lamolle
{"title":"RakSOR: Ranking of Ontology Reasoners Based on Predicted Performances","authors":"N. Alaya, S. Yahia, M. Lamolle","doi":"10.1109/ICTAI.2016.0165","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0165","url":null,"abstract":"Over the last decade, several ontology reasoners have been proposed to overcome the computational complexity of inference tasks on expressive ontology languages. Nevertheless, it is well-accepted that there is no outstanding reasoner that can outperform in all input ontologies. Thus, an algorithm selection problem have emerged in this field of study. In this paper, we describe first steps to develop a new system to provide user support when looking for guidance over ontology reasoners. Our main goal is to be able to automatically rank a set of candidate reasoners for any given ontology. Robustness standing for the ability of reasoner to correctly achieve a reasoning task within a fixed time limit is our primary ranking criterion. Our ranking method follows a meta-learning approach and applies bucket order rules. An extensive experiments covering over 2500 well selected real-world ontologies and six state-of-the-art of the most performing reasoners was carried out to provide enough data for the study. Our prediction and ranking results are encouraging, witnessing the potential benefits of the proposed approach.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132464754","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: 5
Probabilistic Planning for Multiple Stocks of Financial Markets 金融市场中多种股票的概率规划
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0083
A. A. Branquinho, C. R. Lopes, A. Baffa
{"title":"Probabilistic Planning for Multiple Stocks of Financial Markets","authors":"A. A. Branquinho, C. R. Lopes, A. Baffa","doi":"10.1109/ICTAI.2016.0083","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0083","url":null,"abstract":"In the financial market the decision on when buying or selling stocks is fundamental in order to achieve profit. There are several techniques that can be used to help investors in order to make a decision. One of those is the employment of technical analysis that consists of chart studies concerning the behaviour of stock prices. In this paper we describe our approach for this problem of decision making, which is cast as a planning problem in the presence of uncertainties. We propose the use of Partially Observable Markov Decision Process (POMDP) for the task of planning the negotiation of stocks on the financial market. The main and desired contribution consist of exploring this type of planning using multiple stocks. The stocks are selected from correlation calculations. The use of multiple stocks provided better results, when compared to other researched strategies.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130986116","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
Recommending Personalized Asset Investments through Case-Based Reasoning: The SMARTFASI System 通过基于案例的推理推荐个性化资产投资:SMARTFASI系统
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0126
G. Leonardi, L. Portinale, P. Artusio, Marco Valsania
{"title":"Recommending Personalized Asset Investments through Case-Based Reasoning: The SMARTFASI System","authors":"G. Leonardi, L. Portinale, P. Artusio, Marco Valsania","doi":"10.1109/ICTAI.2016.0126","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0126","url":null,"abstract":"Personalized financial advisory systems based on Case-Based Reasoning and on historical user activity are an emerging trend. In the present paper, we report the experience related to the development of a case-based recommendation module in a project called SMARTFASI, where the knowledge about past experiences is exploited, in order to suggest suitable asset investments to the final user. We present a solution aimed at personalizing the asset picking phase, by taking into consideration choices made by customers who have a financial and personal data profile \"similar\" to the current one. We discuss the notion of distance-based similarity adopted in our system and how to actually implement an asset recommendation strategy integrated with the other software modules of SMARTFASI. We finally discuss the impact such a strategy may have both from the point of view of private investors and professional users.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124275240","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: 5
Identifying Domain-Independent Normative Indirect Conflicts 识别与领域无关的规范性间接冲突
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0088
J. S. Santos, V. Silva
{"title":"Identifying Domain-Independent Normative Indirect Conflicts","authors":"J. S. Santos, V. Silva","doi":"10.1109/ICTAI.2016.0088","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0088","url":null,"abstract":"The identification of normative conflicts is an essential task in Multi-Agent Systems that are governed by multiple norms. Two norms are in conflict if they contradict each other, that is, when it is impossible to comply with both norms without a violation. Our work aims to present a means to detect normative conflicts that can only be detected when relationships among elements of different norms are identified. We use the WordNet database in order to map norm elements to words and to identify relationships among the elements taking into account the meaning of the words. Therefore, we present the WordNet Conflict Checker, which is a tool that is able to detect conflicts among norms that regulate elements that are related by semantic relationships.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1106 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120870296","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
A Shapelet Learning Method for Time Series Classification 用于时间序列分类的Shapelet学习方法
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0071
Yi Yang, Qilin Deng, S. Furao, Jinxi Zhao, C. Luo
{"title":"A Shapelet Learning Method for Time Series Classification","authors":"Yi Yang, Qilin Deng, S. Furao, Jinxi Zhao, C. Luo","doi":"10.1109/ICTAI.2016.0071","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0071","url":null,"abstract":"Time series classification (TSC) problem is important due to the pervasiveness of time series data. Shapelet provides a mechanism for the problem by its ability to measure local shape similarity. However, shapelets need to be searched from massive sub-sequences. To address this problem, this paper proposes a novel shapelet learning method for time series classification. The proposed method uses a self-organizing incremental neural network to learn shapelet candidates. The learned candidates reduce greatly in quantity and improve much in quality. After that, an exponential function is proposed to transform the time series data. Besides, all shapelets are selected at the same time by using an alternative attribute selection technique. Experimental results demonstrate statistically significant improvement in terms of accuracies and running speeds against 10 baselines over 28 time series datasets.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116628552","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: 11
A Thermodynamic and Biologically Inspired Kernel Similarity Method 一种热力学和生物学启发的核相似方法
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0115
Alya Slimene, E. Zagrouba
{"title":"A Thermodynamic and Biologically Inspired Kernel Similarity Method","authors":"Alya Slimene, E. Zagrouba","doi":"10.1109/ICTAI.2016.0115","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0115","url":null,"abstract":"Assessment of image similarity is ubiquitous and essential task to a wide range multimedia applications. In this paper we propose a similarity method which aims at providing an image classification scheme using multi-instances based representation of an image. In other words, the similarity measure is defined to be used within two sample sets where each set, which can be defined in an arbitrary metric space, consists in a set of local features used in describing the content of an image. This measure is a kernel based similarity method inspired from an interesting biological behavior of trees, derived from an energy scheme and induced mathematically by formulating it as a quadratic optimization problem in a reproducing kernel Hilbert space (RKHS).","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123655148","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
An Investigation of Transfer Learning and Traditional Machine Learning Algorithms 迁移学习与传统机器学习算法研究
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0051
Karl R. Weiss, T. Khoshgoftaar
{"title":"An Investigation of Transfer Learning and Traditional Machine Learning Algorithms","authors":"Karl R. Weiss, T. Khoshgoftaar","doi":"10.1109/ICTAI.2016.0051","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0051","url":null,"abstract":"Previous research focusing on the evaluation of transfer learning algorithms has predominantly used real-world datasets to measure an algorithm's performance. A test with a real-world dataset exposes an algorithm to a single instance of distribution difference between the training (source) and test (target) datasets. These previous works have not measured performance over a wide-range of source and target distribution differences. We propose to use a test framework that creates many source and target datasets from a single base dataset, representing a diverse-range of distribution differences. These datasets will be used as a stress test to measure an algorithm's performance. The stress test process will measure and compare different transfer learning algorithms and traditional learning algorithms. The unique contributions of this paper, with respect to transfer learning, are defining a test framework, defining multiple distortion profiles, defining a stress test suite, and the evaluation and comparison of different transfer learning and traditional machine learning algorithms over a wide-range of distributions.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123822172","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: 10
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