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Higgs Boson Discovery using Machine Learning Methods with Pyspark

Procedia Computer science 2020年 170卷 1141-1146页

作者： Mourad Azhari Abdallah Abarda Badia Ettaki Jamal Zerouaoui Mohamed Dakkon Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and knowledge Engineering School of Information Sciences Rabat Morocco Département de Statistique et Informatique de Gestion Université Abdelmalek Essaadi Tetouan Morocco

Higgs Boson is an elementary particle that gives the mass to everything in the natural world. The discovery of the Higgs Boson is a major challenge for particle physics. This paper proposes to solve the Higgs Boson Classification Problem with four Machine Learning (ML) Methods, using the Pyspark environment: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and Gradient Boosted Tree (GBT). We compare the accuracy and AUC metrics of those ML Methods. We use a large dataset as Higgs Boson, collected from public site UCI and Higgs dataset downloaded from Kaggle site, in the experimentation stage.

关键词： Boson Higgs Spark Pyspark Machine Learning (ML) Logistic Regression (LR) Decision Tree (DT) Random Forest (RF) Gradient Boosted Tree (GBT) AUC Accuracy

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VAE-GAN Based Zero-shot Outlier Detection 2020

VAE-GAN Based Zero-shot Outlier Detection

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Proceedings of the 2020 4th International Symposium on Computer science and Intelligent Control

作者： Bekkouch Imad Ibrahim Dragoş Constantin Nicolae Adil Khan Syed Imran Ali Asad Khattak Machine Learning and Knowledge Representation Lab Institute of Data Science & AI Innopolis Tatarstan Russia Institutul de cercetări pentru Inteligenta Artificiala 'Mihai Draganescu' România Department of Computer Engineering Kyung Hee University Yongin-si South Korea College of Technological Innovations Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781450388894

Outlier detection is one of the main fields in machine learning and it has been growing rapidly due to its wide range of applications. In the last few years, deep learning-based methods have outperformed machine learning and handcrafted outlier detection techniques, and our method is no different. We present a new twist to generative models which leverages variational autoencoders as a source for uniform distributions which can be used to separate the inliers from the outliers. Both the generative and adversarial parts of the model are used to obtain three main losses (Reconstruction loss, KL-divergence, Discriminative loss) which in return are wrapped with a one-class SVM which is used to make the predictions. We evaluated our method against several datasets both for images and tabular data and it has shown great results for the zero-shot outlier detection problem and was able to easily generalize it for supervised outlier detection tasks on which the performance has increased. For comparison, we evaluated our method against several of the common outlier detection techniques such as DBSCAN-based outlier detection, GMM, K-means and one class SVM directly, and we have outperformed all of them on all datasets.

关键词： Deep Learning Generative Models Machine Learning Outlier Detection

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Learning-based diagnosis and repair 1

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29th Benelux Conference on Artificial Intelligence, BNAIC 2017

作者： Roos, Nico Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (数字)9783319768922

ISBN: (纸本)9783319768915

This paper proposes a new form of diagnosis and repair based on reinforcement learning. Self-interested agents learn locally which agents may provide a low quality of service for a task. The correctness of learned assessments of other agents is proved under conditions on exploration versus exploitation of the learned assessments. Compared to collaborative multi-agent diagnosis, the proposed learning-based approach is not very efficient. However, it does not depend on collaboration with other agents. The proposed learning based diagnosis approach may therefore provide an incentive to collaborate in the execution of tasks, and in diagnosis if tasks are executed in a suboptimal way. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Multi agent systems

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Detecting anomalous events over time using RDF triple extraction and a dynamic implementation of oddball 30

Detecting anomalous events over time using RDF triple extrac...

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30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Heinrichs, Benedikt Scholtes, Jan C. Department of Data Science and Knowledge Engineering Maastricht University Netherlands

This paper shows a new approach for anomaly detection by combining the extraction of so-called triples consisting of a subject, predicate, and object using dynamic anomaly-detection. First, the methods used to extract triples and general principles of anomaly detection and event detection are discussed. Next, a novel approach is presented where extracted triples are converted into time-lapsed networks of triples on which anomaly and event detection methods from social network analysis are applied. Subsequently, the results of the experiments are presented together with the evaluation method used. Considering the results of the tested methods, the dynamic variation of the OddBall algorithm, which measures network changes over time, displays the connection between the predictions of our model and real-life events accurately. © 2018 University of Groningen. All rights reserved.

关键词： Anomaly detection

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Interactive visual labelling versus active learning:an experimental comparison

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Frontiers of Information Technology & Electronic engineering 2020年第4期21卷 524-535页

作者： Mohammad CHEGINI Jurgen BERNARD Jian CUI Fatemeh CHEGINI Alexei SOURIN Keith ANDREWS Tobias SCHRECK Institute of Computer Graphics and Knowledge Visualisation Graz University of TechnologyGraz 8010Austria School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore InfoVis Group University of British ColumbiaVancouver V6T1Z4Canada Max Planck Institute for Meteorology Hamburg 20146Germany Institute of Interactive Systems and Data Science Graz University of TechnologyGraz 8010Austria

Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data *** quality of supervised maching learning depends not only on the type of algorithm used,but also on the quality of the labelled dataset used to train the *** instances in a training dataset is often done manually relying on selections and annotations by expert analysts,and is often a tedious and time-consuming *** learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning *** visual labelling techniques are a promising alternative,providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a *** putting the analyst in the loop,higher accuracy can be achieved in the resulting *** initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning,many aspects of these techniques are still largely *** paper presents a study conducted using the mVis tool to compare three interactive visualisations,similarity map,scatterplot matrix(SPLOM),and parallel coordinates,with each other and with active learning for the purpose of labelling a multivariate *** results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy,and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for *** also employ different labelling strategies depending on the visualisation used.

关键词： Interactive visual labelling Active learning Visual analytics

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Edge-minimum saturated k-planar drawings

arXiv

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arXiv 2020年

作者： Chaplick, Steven Klute, Fabian Parada, Irene Rollin, Jonathan Ueckerdt, Torsten Department of Data Science and Knowledge Engineering Maastricht University Netherlands Utrecht University Netherlands TU Eindhoven Netherlands Department of Mathematics and Computer Science FernUniversität in Hagen Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Germany

For a class D of drawings of loopless (multi-)graphs in the plane, a drawing D ∈ D is saturated when the addition of any edge to D results in D0 ∈/ D—this is analogous to saturated graphs in a graph class as introduced by Turán (1941) and Erdös, Hajnal, and Moon (1964). We focus on k-planar drawings, that is, graphs drawn in the plane where each edge is crossed at most k times, and the classes D of all k-planar drawings obeying a number of restrictions, such as having no crossing incident edges, no pair of edges crossing more than once, or no edge crossing itself. While saturated k-planar drawings are the focus of several prior works, tight bounds on how sparse these can be are not well understood. We establish a generic framework to determine the minimum number of edges among all n-vertex saturated k-planar drawings in many natural classes. For example, when incident crossings, multicrossings and selfcrossings are all allowed, the sparsest n-vertex saturated k-planar drawings have 2/k−(k mod 2) (n − 1) edges for any k ≥ 4, while if all that is forbidden, the sparsest such drawings have 2(k + 1)/k (k-1) (n − 1) edges for any k ≥ 6. Copyright © 2020, The Authors. All rights reserved.

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Improving Automatic Speech Recognition Utilizing Audio-codecs for data Augmentation

Improving Automatic Speech Recognition Utilizing Audio-codec...

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IEEE Workshop on Multimedia Signal Processing

作者： Nirayo Hailu Ingo Siegert Andreas Nürnberger Faculty of Computer Science Data and Knowledge Engineering Group Otto-von-Guericke University Magdeburg Magdeburg Germany Faculty of Electrical Engineering and Information Technology Mobile Dialog Systems Otto-von-Guericke University Magdeburg Magdeburg Germany

ISBN: (数字)9781728193205

ISBN: (纸本)9781728193236

To train end-to-end automatic speech recognition models, it requires a large amount of labeled speech data. This goal is challenging for languages with fewer resources. In contrast to the commonly used feature level data augmentation, we propose to expand the training set by using different audio codecs at the data level. The augmentation method consists of using different audio codecs with changed bit rate, sampling rate, and bit depth. The change reassures variation in the input data without drastically affecting the audio quality. Besides, we can ensure that humans still perceive the audio, and any feature extraction is possible later. To demonstrate the general applicability of the proposed augmentation technique, we evaluated it in an end-to-end automatic speech recognition architecture in four languages. After applying the method, on the Amharic, Dutch, Slovenian, and Turkish datasets, we achieved a 1.57 average improvement in the character error rates (CER) without integrating language models. The result is comparable to the baseline result, showing CER improvement of 2.78, 1.25, 1.21, and 1.05 for each language. On the Amharic dataset, we reached a syllable error rate reduction of 6.12 compared to the baseline result.

关键词： Training Codecs Speech coding Error analysis Bit rate Feature extraction Automatic speech recognition

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TU-231. Global brain network modularity dynamics after local optic nerve damage: an EEG-tracking study

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Clinical Neurophysiology 2022年 141卷 S51-S51页

作者： Wu, Zheng Xu, Jiahua Nürnberger, Andreas Sabel, Bernhard A. Institute of Medical Psychology Medical Faculty Otto-von-Guericke University of Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University of Magdeburg Magdeburg Germany

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PC024 / #1011 DEEP LEARNING OF BRAIN SPACETIME TO PREDICT OUTCOME OF VISION RESTORATION THERAPY USING NON-INVASIVE BRAIN STIMULATION

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Neuromodulation: Technology at the Neural Interface 2022年第7期25卷 S20-S20页

作者： Zheng Wu Jiahua Xu Andreas Nürnberger Bernhard A. Sabel Institute of Medical Psychology Medical Faculty Otto-von-guericke University Of Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-guericke University Of Magdeburg Magdeburg Germany

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An AO-ADMM approach to constraining PARAFAC2 on all modes

arXiv

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arXiv 2021年

作者： Roald, Marie Schenker, Carla Calhoun, Vince D. Adalı, Tülay Bro, Rasmus Cohen, Jeremy E. Acar, Evrim Department of Data Science and Knowledge Discovery Simula Metropolitan Center for Digital Engineering Oslo Norway Faculty of Technology Art and Design Oslo Metropolitan University Oslo Norway Department of Psychology Georgia State University AtlantaGA United States Department of Computer Science and Electrical Engineering UMBC BaltimoreMD United States Department of Food Science University of Copenhagen Copenhagen Denmark Univ Lyon INSA-Lyon UCBL UJM-Saint Etienne CNRS Inserm CREATIS UMR 5220 U1206 VilleurbanneF-69100 France

Analyzing multi-way measurements with variations across one mode of the dataset is a challenge in various fields including data mining, neuroscience and chemometrics. For example, measurements may evolve over time or have unaligned time profiles. The PARAFAC2 model has been successfully used to analyze such data by allowing the underlying factor matrices in one mode (i.e., the evolving mode) to change across slices. The traditional approach to fit a PARAFAC2 model is to use an alternating least squares-based algorithm, which handles the constant cross-product constraint of the PARAFAC2 model by implicitly estimating the evolving factor matrices. This approach makes imposing regularization on these factor matrices challenging. There is currently no algorithm to flexibly impose such regularization with general penalty functions and hard constraints. In order to address this challenge and to avoid the implicit estimation, in this paper, we propose an algorithm for fitting PARAFAC2 based on alternating optimization with the alternating direction method of multipliers (AO-ADMM). With numerical experiments on simulated data, we show that the proposed PARAFAC2 AO-ADMM approach allows for flexible constraints, recovers the underlying patterns accurately, and is computationally efficient compared to the state-of-the-art. We also apply our model to two real-world datasets from neuroscience and chemometrics, and show that constraining the evolving mode improves the interpretability of the extracted *** Codes 15A69, 90C26 © 2021, CC BY.

关键词： Matrix algebra

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