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arXiv 2018年

作者： Long, Jun Zhu, Lei Zhang, Chengyuan Yang, Zhan Lin, Yunwu Chen, Ruipeng School of Information Science and Engineering Central South University China Big Data and Knowledge Engineering Institute Central South University China

Due to the advances in mobile computing and multimedia techniques, there are vast amount of multimedia data with geographical information collected in multifarious applications. In this paper, we propose a novel type of image search named interactive geo-tagged image search which aims to find out a set of images based on geographical proximity and similarity of visual content, as well as the preference of users. Existing approaches for spatial keyword query and geo-image query cannot address this problem effectively since they do not consider these three type of information together for query. In order to solve this challenge efficiently, we propose the definition of interactive top-k geo-tagged image query and then present a framework including candidate search stage , interaction stage and termination stage. To enhance the searching efficiency in a largescale database, we propose the candidate search algorithm named GI-SUPER Search based on a new notion called superior relationship and GIR-Tree, a novel index structure. Furthermore, two candidate selection methods are proposed for learning the preferences of the user during the interaction. At last, the termination procedure and estimation procedure are introduced in brief. Experimental evaluation on real multimedia dataset demonstrates that our solution has a really high performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Query processing

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Multi-view SVM Classification with Feature Selection

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Procedia Computer science 2019年 162卷 405-412页

作者： Yuting Niu Yuan Shang Yingjie Tian School of Information Engineering Zhengzhou University Zhengzhou 450001 China Smart City Institute Zhengzhou University Zhengzhou 450001 China Supercomputer Center Smart City Institute Zhengzhou University Henan 450001 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge management Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China

With the rapid development of data mining technology, multi-view learning (MVL) has become a new research field, which has attracted wide attention of scholars at home and abroad. Multi-view learning is to combine multiple view data of the same entity for data classification, thereby improving learning performance. Previous multi-view research methods mainly concentrate on the relationship between different data views for classification problems. However, when the data is in high dimensions, it is necessary to perform feature selection in the multi-view data classification process. In this paper, we proposed a Multi-view Support Vector Machine Classification with Feature Selection (MSVMCFS) algorithm, which can not only classify multi-view data, but also select features for each view data in the process of classification. In the model, feature selection is performed by the l 1 norm sparsity regularization, and consistency and complementarity between the two views are maintained. To achieve the optimization goal, we adopt linear programming to solve the model. The experimental results on 30 binary datasets demonstrate the validity of the model.

关键词： multi-view learning classification feature selection sparsity regularization linear programming

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License and template access control for geospatial linked data 3

License and template access control for geospatial linked da...

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3rd International Workshop on Geospatial Linked data and the 2nd Workshop on Querying the Web of data, GeoLD-QuWeDa 2018

作者： Meehan, Alan Fatema, Kaniz Brennan, Rob Clinton, Éamonn McNerney, Lorraine O'Sullivan, Declan ADAPT Centre Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland Department of Electronics Computing and Mathematics University of Derby Kedleston Road Derby United Kingdom Ordnance Survey Ireland Phoenix Park Dublin Ireland

As Linked data technologies mature geospatial data institutions are seeking to exploit these advantages and selectively serve their commercially valuable data to customers on the web. An access control solution is required to ensure customers are not accessing unauthorized data. In this paper we present a fine grained and flexible access control method for geospatial Linked data. Our declarative approach provides a vocabulary to model data licenses and templates. A license models what a customer is allowed to access at a fine granularity and a template models how data is allowed to be accessed. Each template contains variables and the values that these variables are allowed to contain are specified. This provides great flexibility and allows the creation of templates for a range of different data access use cases. We propose an implementation architecture for our approach which proposes a license and template-based access control web service that sits on top of a triple store, SPARQL processor and a RESTful API for access. We also present a case study where a prototype implementation of our access control approach was applied to Ordnance Survey Ireland's controlled access geospatial Linked data. © CEUR-WS. All rights reserved.

关键词： Access control

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Indefinite kernel spectral learning 30

Indefinite kernel spectral learning

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30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Mehrkanoon, Siamak Huang, Xiaolin Suykens, Johan A.K. Dept. of Data Science and Knowledge Engineering Maastricht University Netherlands Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China KU Leuven ESAT-STADIUS Kasteelpark Arenberg 10 LeuvenB-3001 Belgium

This paper introduces the indefinite learning in the framework of least squares support vector machines (LS-SVM). Here the analysis of the Multi-Class Semi-Supervised Kernel Spectral Clustering (MSS-KSC) model with indefinite kernels is provided. In indefinite MSS-KSC one finds the solution by solving a linear system of equations in the dual. In addition the use of the propped indefinite model for large scale data using Nyström approximation technique as well as unsupervised learning using Kernel Spectral Clustering are also discussed in the published full paper1. Experimental results on several real-life datasets are given to illustrate the efficiency of the proposed indefinite kernel spectral learning. © 2018 University of Groningen. All rights reserved.

关键词： Clustering algorithms

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The Computational Complexity of Angry Birds

arXiv

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arXiv 2018年

作者： Stephenson, Matthew Renz, Jochen Ge, Xiaoyu Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Research School of Computer Science Australian National University Canberra Australia

The physics-based simulation game Angry Birds has been heavily researched by the AI community over the past five years, and has been the subject of a popular AI competition that is currently held annually as part of a leading AI conference. Developing intelligent agents that can play this game effectively has been an incredibly complex and challenging problem for traditional AI techniques to solve, even though the game is simple enough that any human player could learn and master it within a short time. In this paper we analyse how hard the problem really is, presenting several proofs for the computational complexity of Angry Birds. By using a combination of several gadgets within this game’s environment, we are able to demonstrate that the decision problem of solving general levels for different versions of Angry Birds is either NP-hard, PSPACE-hard, PSPACE-complete or EXPTIME-hard. Proof of NP-hardness is by reduction from 3-SAT, whilst proof of PSPACE-hardness is by reduction from True Quantified Boolean Formula (TQBF). Proof of EXPTIME-hardness is by reduction from G2, a known EXPTIME-complete problem similar to that used for many previous games such as Chess, Go and Checkers. To the best of our knowledge, this is the first time that a single-player game has been proven EXPTIME-hard. This is achieved by using stochastic game engine dynamics to effectively model the real world, or in our case the physics simulator, as the opponent against which we are playing. These proofs can also be extended to other physics-based games with similar mechanics. Copyright © 2018, The Authors. All rights reserved.

关键词： Computational complexity

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Towards a topic discovery and tracking system with application to news items 2nd

Towards a topic discovery and tracking system with applicati...

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2nd International Workshop on Future and Emerging Trends in Language Technology, FETLT 2016

作者： Brüggermann, Daniel Hermey, Yannik Orth, Carsten Schneider, Darius Selzer, Stefan Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University Maastricht6200 MD Netherlands

ISBN: (纸本)9783319693644

Rapid proliferation of the World Wide Web led to an enormous increase in the availability of textual corpora. In this paper, the problem of topic detection and tracking is considered with application to news items. The proposed approach explores two algorithms (Non-Negative Matrix Factorization and a dynamic version of Latent Dirichlet Allocation (DLDA)) over discrete time steps and makes it possible to identify topics within storylines as they appear and track them through time. Moreover, emphasis is given to the visualization and interaction with the results through the implementation of a graphical tool (regardless the approach). Experimental analysis on Reuters RCV1 corpus and the Reuters 2015 archive reveals that explored approaches can be effectively used as tools for identifying topic appearances and their evolutions while at the same time allowing for an efficient visualization. © 2017, Springer International Publishing AG.

关键词： Non-negative matrix factorization

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Do Positive Twitter Messages Have Impact on Stock Liquidity?

Do Positive Twitter Messages Have Impact on Stock Liquidity?

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作者： Abdirahmani, Aliasghar Angulo, Cecilio Automatic Control Dept. Universitat Politècnica de Catalunya Pau Gargallo 14. BacelonaTech08028 Spain Knowledge Engineering Research Group Intelligent Data Science and Artificial Intelligence Research Center Univ. Politècnica Catalunya BarcelonaTech Spain

ISBN: (纸本)9781614999171

Social media is a phenomenon that has transformed the interaction and communication of individuals throughout the world. In recent times, social media has affected many aspects of human communication, as well as business. The main objective of our research is to figure out about the role of Twitter on stock market to find out how social media impacts on the business that predictions fail. © 2018 The authors and IOS Press.

关键词： Sentiment analysis

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Heterogeneous-length text topic modeling for reader-Aware multi-document summarization

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ACM Transactions on knowledge Discovery from data 2019年第4期13卷 42-42页

作者： Qiang, Jipeng Chen, Ping Ding, Wei Wang, Tong Xie, Fei Wu, Xindong Department of Computer Science Yangzhou University China Department of Computer Science University of Massachusetts Boston United States Department of Computer Science and Technology Hefei Normal University China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100084 China

More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as reflected in reader comments. In this article, we focus on how to generate a summary from multi-document documents by considering reader comments, named as reader-Aware multi-document summarization (RA-MDS).We present an innovative topic-based method for RA-MDA, which exploits latent topics to obtain the most salient and lessen redundancy summary from multiple documents. Since finding latent topics for RA-MDS is a crucial step, we also present a Heterogeneous-length Text Topic Modeling (HTTM) to extract topics from the corpus that includes both news reports and user comments, denoted as heterogeneous-length texts. In this case, the latent topics extract by HTTM cover not only important aspects of the event, but also aspects that attract reader interests. Comparisons on summary benchmark datasets also confirm that the proposed RA-MDS method is effective in improving the quality of extracted summaries. In addition, experimental results demonstrate that the proposed topic modeling method outperforms existing topic modeling algorithms. © 2019 Association for Computing Machinery. All rights reserved.

关键词： heterogeneous-length text LDA multi-document summarization Topic modeling

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Residual network with delayed max pooling for very large scale plant identification 18

Residual network with delayed max pooling for very large sca...

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18th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2017

作者： Hang, Siang Thye Aono, Masaki Knowledge Data Engineering and Information Retrieval Laboratory Department of Computer Science and Engineering Toyohashi University of Technology Japan

In our approach, we applied a few modifications to the 50-layered Residual Network. Our preliminary experiments with the Plant-CLEF 2016 dataset showed that the modifications improved classification performance. We have trained three models based on the modified Residual Network configuration with different combinations of trusted and noisy PlantCLEF 2017 datasets. Using confidence scores extracted from the three models, we have submitted four runs and our methods showed competitive classification performance.

关键词： Deep learning

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Resource-gathering algorithms in the game of starcraft

Resource-gathering algorithms in the game of starcraft

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2017 IEEE Conference on Computational Intelligence and Games, CIG 2017

作者： Rooijackers, Martin L.M. Winands, Mark H.M. Games AI Group Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9781538632338

StarCraft is a real-time strategy game, which has a large state space, and commonly features two opposing players, capable of acting simultaneously. One of the aspects of the game is resource gathering. Each agent playing StarCraft has to gather minerals from nearby mineral field in order to produce more units. The more resources can be gathered, the larger the army is to attack the opponent and win the game. We present five algorithms that can be used for resource gathering for an intelligent agent playing the game of StarCraft: Brood War. The results reveal that improving the scheduling or the path finding improve the resource gathering considerably. © 2017 IEEE.

关键词： Intelligent agents

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