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5th international conference on Scale Space and Variational methods in Computer Vision, SSVM 2015

作者： Raad, Lara Desolneux, Agnès Morel, Jean-Michel CMLA Ecole Normale Supérieure de Cachan Cachan France

ISBN: (纸本)9783319184609

An ideal exemplar-based texture synthesis algorithm should create a new texture that is perceptually equivalent to its texture example. To this goal it should respect the statistics of the example and avoid proceeding to a "copy-paste" process, which is the main drawback of the non-parametric approaches. In a previous work we modeled textures as a locally Gaussian patch model. this model was estimated for each patch before stitching it to the preceding ones. In the present work, we extend this model to a local conditional Gaussian patch distribution. the condition is taken over the already computed values. Our experiments here show that the conditional model reproduces well periodic and pseudoperiodic textures without requiring the use of any stitching technique. the experiments put also in evidence the importance of the right choice for the patch size. We conclude by pointing out the remaining limitations of the approach and the necessity of a multiscale approach. © Springer international Publishing Switzerland 2015.

关键词： Gaussian distribution

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How to increase the effectiveness of the hepatitis diagnostics by means of appropriate machine learning methods 6th

How to increase the effectiveness of the hepatitis diagnosti...

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6th international conference on Information Technology in Bio- and Medical Informatics, ITBAM 2015

作者： Lukáčová, Alexandra Babič, František Paraličová, Zuzana Paralič, Ján Department of Cybernetics and Artificial Intelligence Technical University of Košice Letná 9/B Košice042 00 Slovakia University of Pavol Jozef Šafárik Tr. SNP 1 Košice Slovakia

ISBN: (纸本)9783319227405

this paper presents how to improve the diagnostic process of hepatitis B and C based on collected questionnaires from patients hospitalized in all regional departments of infectology in Slovakia. Performed experiments were oriented in two directions: economic demands of the recommended treatment based on realized diagnostics and possible improvement of hepatitis diagnostics by means of exploratory and predictive analysis of additional information provided by patients. Exploratory data analysis was used to confirm or to reject some expected relationships between input attributes (e.g. ager or gender) and target diagnosis. Also, predictive mining resulted into interesting decision rules that can be used in medical practice as supporting information at an early stage of the diagnostic process. Finally, analysis of the treatment economic demands based on the estimated costs showed the need for timely and quality diagnostics to minimize the percentage of patients for which was hepatitis diagnosed late. © Springer international Publishing Switzerland 2015.

关键词： Diagnosis

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Does the Inclusion of Data Sampling Improve the Performance of Boosting algorithms on Imbalanced bioinformatics Data?

Does the Inclusion of Data Sampling Improve the Performance ...

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international conference on Machine Learning and Applications (ICMLA)

作者： Alireza Fazelpour Taghi M. Khoshgoftaar David J. Dittman Amri Napolitano Florida Atlantic University Boca Raton FL

bioinformatics datasets contain many challenging characteristics, such as class imbalance, which adversely impacts the performance of supervised classification models built on these datasets. Techniques such as ensemble learning and data sampling from the domain of data mining can be deployed to alleviate the problem and to improve the classification performance. In this study, we sought to seek whether inclusion of data sampling within the ensemble framework can further improve the performance of classification models. To this end, we performed an experimental study using two newly hybrid ensemble techniques, one integrates feature selection within the boosting process and the other incorporates random under-sampling followed by feature selection within the boosting framework, two learners, three forms of feature rankers, and four feature subset sizes on 15 highly imbalanced bioinformatics datasets. Our results and statistical analysis demonstrate that the difference between the two boosting methods is statistically insignificant. therefore, as the inclusion of data sampling has no significant positive effect on the performance of ensemble classifiers, it is not required to achieve maximum classification performance. To our knowledge, this is the first empirical study that examined the effects of data sampling, random under-sampling, to enhance classification performance of boosting algorithm for highly imbalanced bioinformatics data.

关键词： Boosting bioinformatics Data models Buildings Data mining Training Measurement

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Using LSTM Neural Networks as Resource Utilization Predictors: the Case of Training Deep Learning models on the Edge 17th

Using LSTM Neural Networks as Resource Utilization Predictor...

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17th international conference on Economics of Grids, Clouds, Systems, and Services, GECON 2020

作者： Violos, John Psomakelis, Evangelos Danopoulos, Dimitrios Tsanakas, Stylianos Varvarigou, theodora School of Electrical and Computer Engineering National Technical University of Athens Zografou15771 Greece Dept. of Informatics and Telematcs Harokopio University of Athens Tavros Greece

ISBN: (纸本)9783030630577

Cloud and Fog technologies are steadily gaining momentum and popularity in the research and industry circles. Both communities are wondering about the resource usage. the present work aims to predict the resource usage of a machine learning application in an edge environment, utilizing Raspberry Pies. It investigates various experimental setups and machine learning methods that are acting as benchmarks, allowing us to compare the accuracy of each setup. We propose a prediction model that leverages the time series characteristics of resource utilization employing an LSTM Recurrent Neural Network (LSTM-RNN). To conclude to a close to optimal LSTM-RNN architecture we use a genetic algorithm. For the experimental evaluation we used a real dataset constructed by training a well known model in Raspberry Pies3. the results encourage us for the applicability of our method. © 2020, Springer Nature Switzerland AG.

关键词： Genetic algorithms

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14th international conference of the Italian Association for Artificial Intelligence, AIIA 2015

14th International Conference of the Italian Association for...

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14th international conference of the Italian Association for Artificial Intelligence, 2015

ISBN: (纸本)9783319243085

the proceedings contain 35 papers. the special focus in this conference is on Swarm Intelligence;Genetic algorithms;Computer Vision;Multi Agent Systems;Knowledge Representation;Reasoning;Machine Learning and Natural Language. the topics include: Collective self-awareness and self-expression for efficient network exploration;swarm-based controller for traffic lights management;path relinking for a constrained simulation-optimization team scheduling problem arising in hydroinformatics;adaptive tactical decisions in pedestrian simulation;using stochastic optimization to improve the detection of small checkerboards;empowering agent coordination with social engagement;anticipatory coordination in socio-technical knowledge-intensive environments;a kinetic study of opinion dynamics in multi-agent systems;cooperating with trusted parties would make life easier;agent based simulation of incentive mechanisms on photovoltaic adoption;feature-based modelling and information systems for engineering;a multi-engine theorem prover for a description logic of typicality;defeasible logic programming in satisfiability modulo CHR;abstract solvers for quantified Boolean formulas and their applications;learning accurate cutset networks by exploiting decomposability;common-sense knowledge for natural language understanding;testing a learn-verify-repair approach for safe human-robot interaction;empowered negative specialization in inductive logic programming;open data integration using SPARQL and SPIN;bootstrapping large scale polarity lexicons through advanced distributional methods;using semantic models for robust natural language human robot interaction;automatic identification and disambiguation of concepts and named entities in the multilingual Wikipedia and a logic-based approach to named-entity disambiguation in the web of data.

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Property-Based Testing for Parameter Learning of Probabilistic Graphical models 4th

Property-Based Testing for Parameter Learning of Probabilist...

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4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 international Cross-Domain conference for Machine Learning and Knowledge Extraction, CD-MAKE 2020

作者： Saranti, Anna Taraghi, Behnam Ebner, Martin Holzinger, Andreas Medical University Graz Auenbruggerplatz 2 Graz8036 Austria xAI Lab Alberta Machine Intelligence Institute EdmontonT6G 2H1 Canada Department Educational Technology Graz University of Technology Münzgrabenstrasse 36/I Graz8010 Austria

ISBN: (纸本)9783030573201

Code quality is a requirement for successful and sustainable software development. the emergence of Artificial Intelligence and data driven Machine Learning in current applications makes customized solutions for both data as well as code quality a requirement. the diversity and the stochastic nature of Machine Learning algorithms require different test methods, each of which is suitable for a particular method. Conventional unit tests in test-automation environments provide the common, well-studied approach to tackle code quality issues, but Machine Learning applications pose new challenges and have different requirements, mostly as far the numerical computations are concerned. In this research work, a concrete use of property-based testing for quality assurance in the parameter learning algorithm of a probabilistic graphical model is described. the necessity and effectiveness of this method in comparison to unit tests is analyzed with concrete code examples for enhanced retraceability and interpretability, thus highly relevant for what is called explainable AI. © 2020, IFIP international Federation for Information Processing.

关键词： Quality assurance

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Towards the orchestration of secured services under non-disclosure policies

Towards the orchestration of secured services under non-disc...

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6th international conference on Mathematical methods, models and Architectures for Computer Network Security, MMM-ACNS 2012

作者： Avanesov, Tigran Chevalier, Yannick Rusinowitch, Michaël Turuani, Mathieu INRIA Nancy Grand Est 615 allée du jardin botanique 54000 Vandœuvre-lés-Nancy France IRIT Université de Toulouse 118 route de Narbonne F-31062 Toulouse France SnT Université du Luxebmourg 6 rue Richard Coudenhove-Kalergi L-1359 Luxembourg Luxembourg

ISBN: (纸本)9783642337031

the problem of finding a mediator to compose secured services has been reduced in our former work to the problem of solving deducibility constraints similar to those employed for cryptographic protocol analysis. We extend in this paper the mediator synthesis procedure by a construction for expressing that some data is not accessible to the mediator. then we give a decision procedure for verifying that a mediator satisfying this non-disclosure policy can be effectively synthesized. this procedure has been implemented in CL-AtSe, our protocol analysis tool. the procedure extends constraint solving for cryptographic protocol analysis in a significative way as it is able to handle negative deducibility constraints without restriction. In particular it applies to all subterm convergent theories and therefore covers several interesting theories in formal security analysis including encryption, hashing, signature and pairing. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Web services

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Practical Approach to Designing Processes with Generalized Nets and Linked Data Tokens

Practical Approach to Designing Processes with Generalized N...

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IEEE international conference Intelligent Systems

作者： Pavel Tcheshmedjiev Bioinformatics and Mathematical Modelling Department IBPhBME Bulgarian Academy of Sciences Sofia 1113 Bulgaria

In the present paper, a new extension to Generalized Nets is proposed. Tokens are represented as linked data and business process activities are represented as GN transitions. We will demonstrate how a GN model can be... 详细信息

ISBN: (纸本)9781467322768

关键词： component Generalized nets modelling parallel processes linked data RDF practical methods token guide number data models semantic networks Nets

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High dimensional model representation of log likelihood ratio: binary classification with SNP data

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BMC MEDICAL GENOMICS 2020年第9期13卷 1-22页

作者： Pour, Ali Foroughi Pietrzak, Maciej Sucheston-Campbell, Lara E. Karaesmen, Ezgi Dalton, Lori A. Rempala, Grzegorz A. Ohio State Univ Dept Elect & Comp Engn 2015 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Math 231 West 18th Ave Columbus OH 43210 USA Math Biosci Inst 1735 Neil Ave Columbus OH 43210 USA Ohio State Univ Coll Publ Hlth 1841 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat 1585 Neil Ave Columbus OH 43210 USA Ohio State Univ Coll Pharm 500 West 12th Ave Columbus OH 43210 USA

Background Developing binary classification rules based on SNP observations has been a major challenge for many modern bioinformatics applications, e.g., predicting risk of future disease events in complex conditions such as cancer. Small-sample, high-dimensional nature of SNP data, weak effect of each SNP on the outcome, and highly non-linear SNP interactions are several key factors complicating the analysis. Additionally, SNPs take a finite number of values which may be best understood as ordinal or categorical variables, but are treated as continuous ones by many algorithms. methods We use the theory of high dimensional model representation (HDMR) to build appropriate low dimensional glass-box models, allowing us to account for the effects of feature interactions. We compute the second order HDMR expansion of the log-likelihood ratio to account for the effects of single SNPs and their pairwise interactions. We propose a regression based approach, calledlinear approximation for block second order HDMR expansion of categorical observations(LABS-HDMR-CO), to approximate the HDMR coefficients. We show how HDMR can be used to detect pairwise SNP interactions, and propose thefixed pattern test(FPT) to identify statistically significant pairwise interactions. Results We apply LABS-HDMR-CO and FPT to synthetically generated HAPGEN2 data as well as to two GWAS cancer datasets. In these examples LABS-HDMR-CO enjoys superior accuracy compared with several algorithms used for SNP classification, while also taking pairwise interactions into account. FPT declares very few significant interactions in the small sample GWAS datasets when bounding false discovery rate (FDR) by 5%, due to the large number of tests performed. On the other hand, LABS-HDMR-CO utilizes a large number of SNP pairs to improve its prediction accuracy. In the larger HAPGEN2 dataset FTP declares a larger portion of SNP pairs used by LABS-HDMR-CO as significant. Conclusion LABS-HDMR-CO and FPT are interestin

关键词： Single nucleotide polymorphism Binary classification High dimensional model representation Pairwise SNP interactions Log likelihood ratio

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Fast hoeffding drift detection method for evolving data streams 1

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15th European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016

作者： Pesaranghader, Ali Viktor, Herna L. Faculty of Engineering School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada

ISBN: (数字)9783319462271

ISBN: (纸本)9783319462264

Decision makers increasingly require near-instant models to make sense of fast evolving data streams. Learning from such evolving environments is, however, a challenging task. this challenge is partially due to the fact that the distribution of data often changes over time, thus potentially leading to degradation in the overall performance. In particular, classification algorithms need to adapt their models after facing such distributional changes (also referred to as concept drifts). Usually, drift detection methods are utilized in order to accomplish this task. It follows that detecting concept drifts as soon as possible, while resulting in fewer false positives and false negatives, is a major objective of drift detectors. To this end, we introduce the Fast Hoeffding Drift Detection Method (FHDDM) which detects the drift points using a sliding window and Hoeffding’s inequality. FHDDM detects a drift when a significant difference between the maximum probability of correct predictions and the most recent probability of correct predictions is observed. Experimental results confirm that FHDDM detects drifts with less detection delay, less false positive and less false negative, when compared to the state-of-the-art. © Springer international Publishing AG 2016.

关键词： Decision making

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