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IEEE/ACS International Conference on Computer Systems and Applications

作者： Chaima Bejaoui Alexandre Guitton Abdennaceur Kachouri Sfax University National School of Engineers of Sfax Road Soukra km 4 - 3038 Tunisia

ISBN: (纸本)9781509004799

Wireless Sensor Networks (WSNs) are a promising technology to monitor distant or inaccessible areas. As nodes have a limited energy supply, many routing protocols are based on a clustering mechanism: some nodes are elected as cluster heads and have to deal with most of the communication burden of the network, while the other nodes perform only simple operations. In this paper, we propose a new election mechanism with important features: it ensures that all nodes are in range of a cluster head while keeping the number of cluster heads low, it takes into account the residual energy of nodes, and it requires a small communication overhead. We compare the performance of our election mechanism with an optimal election, as well as with the election mechanism of LEACH, which is the main clustering algorithm for WSNs.

关键词： ILP LEACH WSN clustering leaching Wireless sensor networks inductive logic programming WSN gene Ganglia cluster head Head Cluster Analysis residual energy of node Routing protocols Residual energy

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Partitional Clustering of Protein Sequences - An inductive logic programming Approach

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10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Fonseca, Nuno A. Costa, Vitor S. Camacho, Rui Vieira, Cristina Vieira, Jorge Univ Porto IBMC Rua Campo Alegre 823 P-4150180 Oporto Portugal Univ Porto INESC Porto LA CRACS Porto Portugal Univ Porto LIAAD INSEC Porto LA & FEUP Porto Portugal

ISBN: (纸本)9783642024801

We present a novel approach to cluster sets of protein sequences, based on inductive logic programming (ILP). Preliminary results show that;the method proposed Produces understand able descriptions/explanations of the clusters. Furthermore, it can be used as a knowledge elicitation tool to explain clusters proposed by other clustering approaches, such as standard phylogenetic programs.

关键词： Clustering inductive logic programming

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An integrated ILP model for Routing, Modulation Level and Spectrum Allocation in the next generation DCN

An integrated ILP model for Routing, Modulation Level and Sp...

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Third International Conference on Cyberspace Technology (CCT 2015)

作者： Xiao Jia Fan Ning Shan Yin Dajiang Wang Jie Zhang Shanguo Huang State Key Laboratory of Information Photonics and Optical Communication Beijing University of Posts and Telecommunications 100876 China Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications 100876 China ZTE Corporation Beijing China

This paper focus on the routing, modulation level and spectrum allocation (RMLSA) problem in the next generation datacentre networks, which own the advantages of both Elastic Optical Networks (EON) and Software Defined Networking (SDN). We proposes an Integer Linear programming (ILP) model for this problem. Simulation results show the ILP model achieves an outstanding load balancing performance.

关键词： Datacenter Networks Integer Linear programming model Routing Modulation Level and Spectrum Allocation Spectrum allocation inductive logic programming linear programming models Network routing

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TEMPLATE BASED INTER-LAYER PREDICTION FOR HIGH DYNAMIC RANGE SCALABLE COMPRESSION

TEMPLATE BASED INTER-LAYER PREDICTION FOR HIGH DYNAMIC RANGE...

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IEEE International Conference on Image Processing

作者： Mikael Le Pendu Christine Guillemot Dominique Thoreau INRIA Technicolor

ISBN: (纸本)9781479983407

This paper presents a scalable high dynamic range (HDR) image coding framework in which the base layer is a low dynamic range (LDR) version of the image that may have been generated by an arbitrary Tone Mapping Operator (TMO). Our method successfully handles the case of complex local TMOs thanks to a block-wise and non-linear approach. A novel template based Inter Layer Prediction (ILP) is designed in order to perform the inverse tone mapping of a block without the need to transmit any additional parameter to the decoder. This method enables the use of a more accurate inverse tone mapping model than the simple linear regression commonly used for block-wise ILP. Our experiments have shown an average bitrate saving of 34% on the HDR enhancement layer, compared to state of the art methods.

关键词： High Dynamic Range (HDR) Tone Mapping Inverse Tone mapping Scalability HEVC Inter Layer Prediction (ILP) Scalability jigs(templates) High efficiency video coding HIGH DYNAMIC RANGE inductive logic programming BASE COURSE GATA3 gene hot dry rocks Dynamic range MAPPING(MATHEMATICAL)

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Plane-based object categorisation using relational learning

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MACHINE LEARNING 2014年第1期94卷 3-23页

作者： Farid, Reza Sammut, Claude Univ New S Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

We use inductive logic programming (ILP) to learn classifiers for generic object recognition from point clouds, as generated by 3D cameras, such as the Kinect. Each point cloud is segmented into planar surfaces. Each subset of planes that represents an object is labelled and predicates describing those planes and their relationships are used for learning. Our claim is that a relational description for classes of 3D objects can be built for robust object categorisation in real robotic application. To test the hypothesis, labelled sets of planes from 3D point clouds gathered during the RoboCup Rescue Robot competition are used as positive and negative examples for an ILP system. The robustness of the results is evaluated by 10-fold cross validation. In addition, common household objects that have curved surfaces are used for evaluation and comparison against a well-known non-relational classifier. The results show that ILP can be successfully applied to recognise objects encountered by a robot especially in an urban search and rescue environment.

关键词： Object classification inductive logic programming Machine learning Urban search and rescue 3D point cloud

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Relational machine learning for electronic health record-driven phenotyping

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JOURNAL OF BIOMEDICAL INFORMATICS 2014年 52卷 260-270页

作者： Peissig, Peggy L. Costa, Vitor Santos Caldwell, Michael D. Rottscheit, Carla Berg, Richard L. Mendonca, Eneida A. Page, David Marshfield Clin Res Fdn Biomed Informat Res Ctr Marshfield WI 54449 USA Univ Porto DCC FCUP P-4100 Oporto Portugal Univ Porto Dept Ciencia Comp CRACS INESC TEC P-4100 Oporto Portugal Marshfield Clin Fdn Med Res & Educ Dept Surg Marshfield WI USA Univ Wisconsin Dept Biostat & Med Informat Madison WI 53706 USA Univ Wisconsin Dept Pediat Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

Objective: Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping. Methods: Two relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance. Results: We developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p = 0.039), J48 (p = 0.003) and JRIP (p = 0.003). Discussion: ILP has the potential to impro

关键词： Machine learning Electronic health record inductive logic programming Phenotyping Relational machine learning

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Predicting virus mutations through statistical relational learning

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BMC BIOINFORMATICS 2014年第1期15卷 1-12页

作者： Cilia, Elisa Teso, Stefano Ammendola, Sergio Lenaerts, Tom Passerini, Andrea Univ Livre Bruxelles MLG Dept Informat B-1050 Brussels Belgium ULB VUB Interuniv Inst Bioinformat Brussels IB 2 B-1050 Brussels Belgium Univ Trento Dept Comp Sci & Informat Engn I-38123 Trento Povo Italy Ambiotec Sas R&D I-00142 Cisterna Latina LT Italy Vrije Univ Brussel Vakgroep Computerwetenschappen AI Lab B-1050 Brussels Belgium

Background: Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant mutants. Results: We propose a simple statistical relational learning approach for mutant prediction where the input consists of mutation data with drug-resistance information, either as sets of mutations conferring resistance to a certain drug, or as sets of mutants with information on their susceptibility to the drug. The algorithm learns a set of relational rules characterizing drug-resistance and uses them to generate a set of potentially resistant mutants. Learning a weighted combination of rules allows to attach generated mutants with a resistance score as predicted by the statistical relational model and select only the highest scoring ones. Conclusions: Promising results were obtained in generating resistant mutations for both nucleoside and non-nucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations.

关键词： Nucleoside Reverse Transcriptase Inhibitor Amino Acid Mutation inductive logic programming Definite Clause Single Amino Acid Mutation

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Learning from interpretation transition

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MACHINE LEARNING 2014年第1期94卷 51-79页

作者： Inoue, Katsumi Ribeiro, Tony Sakama, Chiaki Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan Grad Univ Adv Studies Sokendai Dept Informat Chiyoda Ku Tokyo 1018430 Japan Wakayama Univ Dept Comp & Commun Sci Wakayama 6408510 Japan

We propose a novel framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I,J) such that J=T (P) (I), where T (P) is the immediate consequence operator, we infer the program P. The learning framework can be repeatedly applied for identifying Boolean networks from basins of attraction. Two algorithms have been implemented for this learning task, and are compared using examples from the biological literature. We also show how to incorporate background knowledge and inductive biases, then apply the framework to learning transition rules of cellular automata.

关键词： Dynamical systems Boolean networks Cellular automata Attractors Supported models Learning from interpretation inductive logic programming

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Finding relational redescriptions

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MACHINE LEARNING 2014年第3期96卷 225-248页

作者： Galbrun, Esther Kimmig, Angelika Univ Helsinki Dept Comp Sci Helsinki 00014 Finland Univ Helsinki Helsinki Inst Informat Technol FIN-00014 Helsinki Finland Katholieke Univ Leuven Dept Computerwetenschappen B-3001 Heverlee Belgium

We introduce relational redescription mining, that is, the task of finding two structurally different patterns that describe nearly the same set of object pairs in a relational dataset. By extending redescription mining beyond propositional and real-valued attributes, it provides a powerful tool to match different relational descriptions of the same concept. We propose an alternating scheme for solving this problem. Its core consists of a novel relational query miner that efficiently identifies discriminative connection patterns between pairs of objects. Compared to a baseline inductive logic programming (ILP) approach, our query miner is able to mine more complex queries, much faster. We performed extensive experiments on three real world relational datasets, and present examples of redescriptions found, exhibiting the power of the method to expressively capture relations present in these networks.

关键词： Redescription mining Relational query mining inductive logic programming Graph mining Relational data mining

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Improving Hit Ratio of ILP-based Concept Discovery System with Memoization

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COMPUTER JOURNAL 2014年第1期57卷 138-153页

作者： Mutlu, Alev Senkul, Pinar Middle E Tech Univ Dept Comp Engn TR-06531 Ankara Turkey

Although inductive logic programming (ILP)-based concept discovery systems have applications in a wide range of domains, they still suffer from scalability and efficiency issues. One of the reasons for the efficiency problem is the high number of query executions necessary in the concept discovery process. Owing to the refinement operator of ILP-based concept discovery systems, these queries repeat frequently. In this work, we propose a method to improve the look-up table hit ratio for repeating queries of ILP-based concept discovery systems with memoization capabilities. The proposed method introduces modifications on search space evaluation and the covering steps of such systems so that query results of the previous iterations can be exploited. Experimental results show that the proposed method improves the hash table hit ratio of ILP-based concept discovery systems with an affordable cost of extra memory consumption.

关键词： inductive logic programming concept discovery efficiency memoization hit count

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