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共 69 条记录，以下是21-30 订阅

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Toward New Evaluation Metrics for Relational learning 8th

Toward New Evaluation Metrics for Relational Learning

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8th international workshop on Combinations of Intelligent methods and Applications, CIMA 2018 held in conjunction with the 30th IEEE international Conference on Tools with Artificial Intelligence, IEEE ICTAI 2018

作者： Ben Ishak, Mouna LARODEC Laboratory Institut Supérieur de Gestion de Tunis Tunis Tunisia

ISBN: (纸本)9789811519178

statistical relational learning (SRL) is a field of machine learning that enables effective and robust reasoning about relational data structures. Several conventional data mining methods have been adapted for direct application to relational data representation. Probabilistic relational models extend Bayesian networks to a relational data mining context. To use this model, it is first necessary to build it: The structure and parameters of a probabilistic relational model must be set manually or learned from a relational observational dataset. learning the structure remains the most complicated issue as it is a NP-hard problem. Existing approaches for probabilistic relational models structure learning are inspired from classical methods of learning the structure of Bayesian networks. The evaluation of learning approaches requires testing datasets and evaluation measurements. Processes to randomly generate the model and the data are already established in the relational context. However, metrics to evaluate a probabilistic relational model structure learning algorithm are not yet proposed. In fact, only the classical Recall and Precision have been used as evaluation metrics. In this work, we discuss why these metrics are not appropriate in this context, and we propose an adaptation of them to fit probabilistic relational models representation. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Bayesian networks

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Data-driven powertrain component aging prediction using in-vehicle signals 46

Data-driven powertrain component aging prediction using in-v...

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46th international Conference on Current Trends in Theory and Practice of Computer Science Doctoral Student Research Forum, SOFSEM-SRF 2020

作者： Sass, Andreas Udo Esatbeyoglu, Enes Iwwerks, Till Volkswagen AG Brieffach 17772 Wolfsburg38436 Germany

Predictive maintenance has become an important tool to avoid unplanned downtime of modern vehicles. The exchanged data between Electronic Control Units (ECU) is simultaneously increasing with the functionality. A large number of in-vehicle signals are provided and facilitate the monitoring of physical component aging processes. In this work, we generated a training dataset and observed aging of a selected powertrain component. first, we preprocessed in-vehicle signals to generate a time equidistant signal database. Furthermore, the signals were segmented in various time periods and subsequently aggregated to statistical features. Second, the signal associated aging information were synchronized to an equal time frame. We investigated several signals preselection approaches to predict an aging-value for the powertrain component with machine learning methods. These approaches differ in the count of selected in-vehicle signals for the aging-value prediction. Our results show that in-vehicle signals can be used to predict powertrain component aging. The quality of estimation differs with respect to the selected regression methods. In this work we present an approach to narrow down the prediction quality of different preselection approaches for the estimation of a powertrain component aging. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： machine learning

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CMiGIN 2019 - Proceedings of the international workshop on Conflict Management in Global Information Networks, co-located with 1st international Conference on Cyber Hygiene and Conflict Management in Global Information Networks, CyberConf 2019

CMiGIN 2019 - Proceedings of the International Workshop on C...

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2019 international workshop on Conflict Management in Global Information Networks, CMiGIN 2019

The proceedings contain 53 papers. The topics discussed include: assessment of operator-pilot training in conflict situations;anonymous decentralized e-voting system;machine learning detection of DDoS attacks based on visualization of recurrence plots;transaction planning methods in hyperconverged architecture systems;integration deterministic, stochastic and non-stochastic uncertainty models in conflict situations;automated complex for aerial reconnaissance tasks in modern armed conflicts;statistical testing of blockchain hash algorithms;practices of using blockchain technology in ICT under the digitalization of the world economy;methods of nulling numbers in the system of residual classes;and mathematical model of the biometric system of fingerprint authentication for modern web-services.

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GUARANTEED deterministic BOUNDS ON THE TOTAL VARIATION DISTANCE BETWEEN UNIVARIATE MIXTURES 28

GUARANTEED DETERMINISTIC BOUNDS ON THE TOTAL VARIATION DISTA...

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IEEE 28th international workshop on machine learning for Signal Processing (MLSP)

作者： Nielsen, Frank Sun, Ke Sony Comp Sci Labs Inc Tokyo Japan CSIRO Data61 Canberra ACT Australia

ISBN: (纸本)9781538654774

The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in [0;1]. Since the total variation distance does not admit closed-form expressions for statistical mixtures, one often has to rely in practice on costly numerical integrations or on fast Monte Carlo approximations that however do not guarantee deterministic bounds. In this work, we consider two methods for bounding the total variation of univariate mixture models: The first is based on the information monotonicity property of the total variation to design guaranteed nested deterministic lower bounds. The second method relies on computing the geometric lower and upper envelopes of weighted mixture components to derive deterministic bounds based on density ratio. We demonstrate the tightness of our bounds through simulating Gaussian, Gamma and Rayleigh mixture models.

关键词： Total variation mixture models information theoretic bounds

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Adopting Markov Logic Networks for big spatial data and applications

Adopting Markov Logic Networks for big spatial data and appl...

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2019 international Conference on Very Large Database PhD workshop, VLDB-PhD 2019

作者： Sabek, Ibrahim Department of Computer Science and Engineering University of Minnesota MN United States

Spatial data has become ubiquitous everywhere, e.g., GPS data, medical data, with increasingly sheer sizes. This raises the need for efficient spatial machine learning and analysis solutions to extract useful insights from such data. Meanwhile, Markov Logic Networks (MLN) have emerged as powerful framework for building usable and scalable machine learning tools. Unfortunately, MLN is ill-equipped for spatial applications because it ignores the distinguished spatial data characteristics. This paper describes SMLN, the first full-fledged MLN framework with native support for spatial data. SMLN comes with a high-level datalog-like language with spatial constructs, and spatially-equipped grounding, inference and learning modules. We show the eectiveness of SMLN by illustrating three systems, namely, Sya, TurboReg, and Flash, that are already built using SMLN. Copyright (C) 2019 for this paper by its authors.

关键词： machine learning

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CML-IOT 2019: The first workshop on Continual and Multimodal learning for Internet of Things

CML-IOT 2019: The First Workshop on Continual and Multimodal...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / ACM international Symposium on Wearable Computers (ISWC)

作者： Yu, Tong Pan, Shijia Xu, Susu Shen, Yilin Hao, Botao Samsung Res Amer Mountain View CA 94043 USA Univ Calif Merced Merced CA USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Purdue Unvers W Lafayette IN USA

ISBN: (纸本)9781450368698

Internet of Things (IoT) provides streaming, large-amount, and multimodal sensing data over time. The statistical properties of these data are always characterized very differently by time and sensing modalities, which are hardly captured by conventional learning methods. Continual and multimodal learning allows integrating, adapting, and generalizing the knowledge learned from previous experiential data collected with heterogeneity to new situations. Therefore, continual and multimodal learning is an important step to improve the estimation, utilization, and security for the real-world data from IoT devices. A few major challenges to combine continual learning and multimodal learning with real-world data include 1) how to accurately match, fuse, and transfer knowledge between the multimodal data from the fast-changing dynamic physical environment, 2) how to learn accurately despite the missing, imbalanced or noisy data for continual learning under multimodal sensing scenarios, 3) how to effectively combine information collected in different sensing modalities to improve the understanding of CPS while retaining privacy and security, and 4) how to develop usable systems handling high volume streaming multimodal data on mobile devices. We organize this workshop to bring people working on different disciplines together to tackle these challenges in this topic. This workshop aims to explore the intersection and combination of continual machine learning and multimodal modeling with applications in the Internet of Things. The workshop welcomes works addressing these issues in different applications/domains as well as algorithmic and systematic approaches to leverage continual learning on multimodal data. We further seek to develop a community that systematically handles the streaming multimodal data widely available in real-world ubiquitous computing systems. Preliminary and on-going work is welcomed.

关键词： Ubiquitous computing internet of things multimodal continual learning

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Three-Dimensional Face Reconstruction from Uncalibrated Photographs: Application to Early Detection of Genetic Syndromes 1st

Three-Dimensional Face Reconstruction from Uncalibrated Phot...

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1st international workshop on Uncertainty for Safe Utilization of machine learning in Medical Imaging / 8th international workshop on Clinical Image-based Procedures - From Planning to Intervention (CLIP)

作者： Tu, Liyun Porras, Antonio R. Morales, Araceli Perez, Daniel A. Piella, Gemma Sukno, Federico Linguraru, Marius George Sheikh Zayed Inst Pediat Surg Innovat Childrens Natl Hlth Syst Washington DC 20010 USA Univ Pompeu Fabra Dept Informat & Commun Technol Barcelona Spain George Washington Univ Sch Med & Hlth Sci Washington DC 20052 USA

ISBN: (纸本)9783030326890;9783030326883

Facial analysis from photography supports the early identification of genetic syndromes, but clinically-acquired uncalibrated images suffer from image pose and illumination variability. Although 3D photography overcomes some of the challenges of 2D images, 3D scanners are not typically available. We present an optimization method for 3D face reconstruction from uncalibrated 2D photographs of the face using a novel statistical shape model of the infant face. first, our method creates an initial estimation of the camera pose for each 2D photograph using the average shape of the statistical model and a set of 2D facial landmarks. Second, it calculates the camera pose and the parameters of the statistical model by minimizing the distance between the projection of the estimated 3D face in the image plane of each camera and the observed 2D face geometry. Using the reconstructed 3D faces, we automatically extract a set of 3D geometric and appearance descriptors and we use them to train a classifier to identify facial dysmorphology associated with genetic syndromes. We evaluated our face reconstruction method on 3D photographs of 54 subjects (age range 0-3 years), and we obtained a point-to-surface error of 2.01 +/- 0.54%, which was a significant improvement over 2.98 +/- 0.64% using state-of-the-art methods (p < 0.001). Our classifier detected genetic syndromes from the reconstructed 3D faces from the 2D photographs with 100% sensitivity and 92.11% specificity.

关键词： Facial dysmorphology 3D face reconstruction 2D photography Morphable model statistical shape model

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statistical decision assistance for determining energy-efficient options in building design under uncertainty 26

Statistical decision assistance for determining energy-effic...

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26th international workshop on Intelligent Computing in Engineering, EG-ICE 2019

作者： Singh, M.M. Geyer, P. KU Leuven Belgium

Designers need to compare numerous design options in the process of designing an energy-efficient building. There are two impediments in this process, first probabilistic prediction of energy requirement at an early stage of design with uncertain design parameters and, second, selecting a design option based on the probabilistic energy prediction. The paper presents an integration of machine learning energy prediction model with building information modelling (BIM) tool to make probabilistic energy prediction, i.e. ranges of values. Wilcoxon rank-sum test is useful in this situation, which is capable of comparing alternatives based on probabilistic energy predictions. The tool has been developed to extract information from the BIM model, make probabilistic energy prediction using the Monte Carlo method, and perform statistical analysis. It has been found that BIM integrated machine learning model can make energy prediction of six design alternatives in 30-35 seconds with no additional modelling efforts. Higher uncertainty in the design parameters will result in larger uncertainty in the energy prediction, and the test may not be able to suggest the better option even using statistical comparison. This will require the more precise value of design parameters, i.e. reduced uncertainty. Different uncertainty levels in the design parameters have been tested to which extent they are sufficient to make a selection of the energy-efficient option. It is observed that uncertainty levels that are suitable for decision-making depend on the combination of design options to be compared. It is possible to differentiate among alternatives with high uncertainty in the design parameters if they are entirely different else more precise definition of the design parameters is required. This research provides a method to select a better option among the developed options based on energy performance at the early stage of design. © 2019 CEUR-WS. All rights reserved.

关键词： Monte Carlo methods

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Investment attractiveness modeling using multidimensional statistical analysis 8

Investment attractiveness modeling using multidimensional st...

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8th international Conference on Monitoring, Modeling and Management of Emergent Economy: Experimental Economics and machine learning for Prediction of Emergent Economy Dynamics, M3E2-EEMLPEED 2019

作者： Shinkarenko, Volodymyr Matskul, Maksym Linok, Dean Odessa National Economic University 8 Preobrazhenska Str. Odesa65000 Ukraine Fujitsu Technology Solutions Textorial Park 17 Fabryczna Str. L 90-344 Poland

The article examines the investment attractiveness of the mainbranches of the food industry of Ukraine as a latent variable. For the first time inthis area, a combination of various methods of multivariate statistical analysis isused for research (cluster analysis and factor analysis-the principal componentmethod). These methods made it possible to use a large number of variousindicators of the activities of industries to characterize investment *** a result, the set of the branches was divided into three groups-clusters:&leaders& are the most attractive sectors for investment, &middle peasants& areattractive branches for investment, and &outsiders& are the least attractivebranches for investment. The generalizing factors (principal components), whichinfluence the resulting factor-investment attractiveness, were found. Theinterrelation of the generalizing factors and initial indicators is established. As aresult of the research, it was possible to make an objective assessment of theinvestment attractiveness (as a latent indicator) of the main branches of the foodindustry in Ukraine, using instead of a multitude of indicators only three latentfactors. © 2019 CEUR-WS. All rights reserved.

关键词： Investments

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Network traffic classification based on class weight based K-NN classifier (CWK-NN) 2

Network traffic classification based on class weight based K...

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2nd international Conference on Big Data and Cyber-Security Intelligence, BDCSIntell 2019

作者： Hijazi, Mohamad Khalife, Jawad Verdejo, Jesus Diaz Al-Ghor, Hussein School of Arts and Sciences Lebanese International University Nabatieh Lebanon School of Arts and Sciences Lebanese International University Beirut Lebanon School of IT and Telecom. Eng University of Granada Granada Spain Faculty of Technology Lebanese University Saida Lebanon

— Network traffic identification is the first and most important step in network management and security. Numerous methods introduced by researchers. One solution depends on processing both packet header and payload, which is costly from both time and processing aspects. Another solution depends on the flow of statistical information such as packet header length, flow duration. Blind classifiers are not accurate yet very fast and do not violate privacy. machine learning fills the gap between accuracy and time by using the blind classifier method and comparing the results with grand truth then adapting and increasing the accuracy. K-NN is used widely for its effectiveness and simplicity. However, a major drawback of K-NN is its dependency on the training set, being a lazy classification algorithm with no classification model to build. In this work, we aim first at assessing the KNN algorithm in traffic classification. Then we introduce a new deficiency, related to the training samples distribution in the n-dimensional space we measure and propose an enhancement for K-NN adapting to the new problem and outperforming native K-NN classifier. We weight the classes, not the instance, based on the intersections of class clusters in the dataset. Finally, we propose a new Class Weight based K-NN Classifier (CWKNN), an enhanced K-NN algorithm that can easily adapt to the newly explored training set deficiency. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Classification (of information)

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