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System Design for a data-driven and Explainable Customer Sentiment Monitor

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arXiv 2021年

作者： Nguyen, An Foerstel, Stefan Kittler, Thomas Kurzyukov, Andrey Schwinn, Leo Zanca, Dario Hipp, Tobias Sun, Da Jun Schrapp, Michael Rothgang, Eva Eskofier, Bjoern Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany University of Applied Sciences Amberg Weiden Germany Siemens Healthcare GmbH Germany Siemens Shanghai Medical Equipment Ltd. China infoteam Software AG Germany

The most important goal of customer services is to keep the customer satisfied. However, service resources are always limited and must be prioritized. Therefore, it is important to identify customers who potentially become unsatisfied and might lead to escalations. Today this prioritization of customers is often done manually. data science on IoT data (esp. log data) for machine health monitoring, as well as analytics on enterprise data for customer relationship management (CRM) have mainly been researched and applied independently. In this paper, we present a framework for a data-driven decision support system which combines IoT and enterprise data to model customer sentiment. Such decision support systems can help to prioritize customers and service resources to effectively troubleshoot problems or even avoid them. The framework is applied in a real-world case study with a major medical device manufacturer. This includes a fully automated and interpretable machine learning pipeline designed to meet the requirements defined with domain experts and end users. The overall framework is currently deployed, learns and evaluates predictive models from terabytes of IoT and enterprise data to actively monitor the customer sentiment for a fleet of thousands of high-end medical devices. Furthermore, we provide an anonymized industrial benchmark dataset for the research community. Copyright © 2021, The Authors. All rights reserved.

关键词： Sales

CLIP: Cheap Lipschitz Training of Neural Networks

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arXiv 2021年

作者： Bungert, Leon Raab, René Roith, Tim Schwinn, Leo Tenbrinck, Daniel Department Mathematics Friedrich-Alexander University Erlangen-Nürnberg Cauerstraße 11 Erlangen91058 Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg Carl-Thiersch-Straße 2b Erlangen91052 Germany

Despite the large success of deep neural networks (DNN) in recent years, most neural networks still lack mathematical guarantees in terms of stability. For instance, DNNs are vulnerable to small or even imperceptible input perturbations, so called adversarial examples, that can cause false predictions. This instability can have severe consequences in applications which influence the health and safety of humans, e.g., biomedical imaging or autonomous driving. While bounding the Lipschitz constant of a neural network improves stability, most methods rely on restricting the Lipschitz constants of each layer which gives a poor bound for the actual Lipschitz constant. In this paper we investigate a variational regularization method named CLIP for controlling the Lipschitz constant of a neural network, which can easily be integrated into the training procedure. We mathematically analyze the proposed model, in particular discussing the impact of the chosen regularization parameter on the output of the network. Finally, we numerically evaluate our method on both a nonlinear regression problem and the MNIST and Fashion-MNIST classification databases, and compare our results with a weight regularization *** Codes 65K10, 68T07 Copyright © 2021, The Authors. All rights reserved.

关键词： Stability

Analyzing the Structure of Attention in a Transformer Language Model

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arXiv 2019年

作者： Vig, Jesse Belinkov, Yonatan Palo Alto Research Center Machine Learning and Data Science Group Interaction and Analytics Lab Palo AltoCA United States Harvard John A. Paulson School of Engineering and Applied Sciences MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States

The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography

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WebPPG: Feasibility and Usability of Self-Performed, Browser...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Michael Nissen Madeleine Flaucher Katharina M. Jaeger Hanna Huebner Nina Danzberger Adriana Titzmann Constanza A. Pontones Peter A. Fasching Bjoern M. Eskofier Heike Leutheuser Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Gynecology and Obstetrics Erlangen University Hospital Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made availab.e to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser’s insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical *** relevance— WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation

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Assessing Personality Traits of Team Athletes in Virtual Reality

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Assessing Personality Traits of Team Athletes in Virtual Rea...

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Markus Wirth Stefan Gradl Wolfgang A. Mehringer Richard Kulpa Hannes Rupprecht Dino Poimann Annemarie F. Laudanski Bjoern M. Eskofier Machine Learning & Data Analytics Univ. of Erlangen-Nürnberg (FAU) Erlangen Germany MM2S Lab University Rennes 2 Rennes France FC Red Bull Salzburg Academy Salzburg Austria Department of Kinesiology University of Waterloo Waterloo Ontario Canada

ISBN: (数字)9781728165325

ISBN: (纸本)9781728165332

Assessment of personality traits is highly relevant in team sports in order to analyze the performance of an athlete under pressure when in competitive situations, for team-strategic decisions, to optimize command transmission, and ultimately to understand top-level performers. It further facilitates the development and application of personalized exercises, coaching to improve performance in competition, and can be considered a valuable criterion for talent scouting and development. The current state of the art method to assess personality traits in sports relies on validated questionnaires. However, these often provide non-sport-specific, subjective self-reported information and lack the ability to measure how these characteristics are reflected in context-based *** developed a virtual reality (VR) tool for the assessment of personality traits in team sports, in our case for soccer. An evaluation of this tool within a study with 24 subjects yielded a benchmark of its immersion through user experience and provided an objective description of athletes’ personalities based on performance indicators extracted from activity-tracking. Within the tool, we implemented two realistic virtual soccer environments to assess the motivational orientation of soccer players (i.e. action- and state-orientation) which we discerned from the gold standard *** show that user experience and presence of the implemented virtual environments scored significantly higher compared to benchmark measurements. Additionally, a significant difference between the two groups of action and state-oriented athletes could be observed. Measures of failure rate, pass accuracy, number of perceived opponents, and achieved bonus goals are parameters that differ significantly among the two athlete groups. These findings show that VR technology is applicable for the assessment of athletes’ motivational orientation and thus demonstrate the feasibility of virtual environments as func

关键词： Training Task analysis Tools Decision making Games Virtual reality

Modern hopfield networks for few- And zero-shot reaction template prediction

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arXiv 2021年

作者： Seidl, Philipp Renz, Philipp Dyubankova, Natalia Neves, Paulo Verhoeven, Jonaes Segler, Marwin Wegner, Jörg K. Hochreiter, Sepp Klambauer, Günter ELLIS Unit Linz LIT AI Lab. Institute for Machine Learning Johannes Kepler University Linz Austria High Dimensional Biology and Discovery Data Sciences Janssen Research & Development Janssen Pharmaceutica N.V. Microsoft Research

Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed, which rely on a model of chemical reactivity. In this study, we model single-step retrosynthesis in a template-based approach using modern Hopfield networks (MHNs). We adapt MHNs to associate different modalities, reaction templates and molecules, which allows the model to leverage structural information about reaction templates. This approach significantly improves the performance of template relevance prediction, especially for templates with few or zero training examples. With inference speed several times faster than baseline methods, we improve predictive performance for top-k exact match accuracy for k ≥ 5 in the retrosynthesis benchmark USPTO-50k. Code to reproduce the results will be availab.e at ***/ml-jku/mhn-react. Copyright © 2021, The Authors. All rights reserved.

关键词： Molecules

Correction to: Wearable gait analysis systems: ready to be used by medical practitioners in geriatric wards?

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European geriatric medicine 2022年第4期13卷 825-826页

作者： Malte Ollenschläger Felix Kluge Matthias Müller-Schulz Rupert Püllen Claudia Möller Jochen Klucken Bjoern M Eskofier Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Carl-Thiersch-Str. 2b 91052 Erlangen Germany. malte.ollenschlaeger@fau.de. Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Carl-Thiersch-Str. 2b 91052 Erlangen Germany. AGAPLESION DIAKONIEKLINIKUM HAMBURG Hamburg Germany. AGAPLESION MARKUS KRANKENHAUS Frankfurt am Main Germany. AGAPLESION gAG Frankfurt am Main Germany. Centre Hospitalier de Luxembourg Luxembourg Institute of Health University of Luxembourg Esch-sur-Alzette Luxembourg.

XPASC: Measuring Generalization in Weak Supervision by Explainability and Association

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arXiv 2022年

作者： März, Luisa Asgari, Ehsaneddin Braune, Fabienne Zimmermann, Franziska Roth, Benjamin Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Vienna Austria AI Innovation & Pre-Development Data:Lab Volkswagen AG Munich Germany UniVie Docotoral School Computer Science Vienna Austria CAPE Analytics Mountain ViewCA United States

Weak supervision is leveraged in a wide range of domains and tasks due to its ability to create massive amounts of lab.led data, requiring only little manual effort. Standard approaches use lab.ling functions to specify signals that are relevant for the lab.ling. It has been conjectured that weakly supervised models over-rely on those signals and as a result suffer from overfitting. To verify this assumption, we introduce a novel method, XPASC (eXPlainability-Association SCore), for measuring the generalization of a model trained with a weakly supervised dataset. Considering the occurrences of features, classes and lab.ling functions in a dataset, XPASC takes into account the relevance of each feature for the predictions (explainability) of the model as well as the connection of the feature with the class and the lab.ling function (association), respectively. The explainability is measured using occlusion in this work. The association in XPASC can be measured in two variants: XPASC-CHI SQUARE measures associations relative to their statistical significance, while XPASC-PPMI measures association strength more generally. We use XPASC to analyze KNOWMAN, an adversarial architecture intended to control the degree of generalization from the lab.ling functions and thus to mitigate the problem of overfitting. On one hand, we show that KNOWMAN is able to control the degree of generalization through a hyperparameter. On the other hand, results and qualitative analysis show that generalization and performance do not relate one-to-one, and that the highest degree of generalization does not necessarily imply the best performance. Therefore methods that allow for controlling the amount of generalization can achieve the right degree of benign overfitting. Our contributions in this study are i) the XPASC score to measure generalization in weakly-supervised models, ii) evaluation of XPASC across datasets and models and iii) the release of the XPASC implementation. © 2022, CC BY.

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NC-ALG: Graph-Based Active learning Under Noisy Crowd

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NC-ALG: Graph-Based Active Learning Under Noisy Crowd

International Conference on data Engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active learning (AL) methods, they assume that the annotated lab.ls are always correct, which is contradictory to the error-prone lab.ling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the lab.ling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node lab.ling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node lab.ling, NC-ALG significantly reduces the lab.ling cost by considering the model-predicted lab.ls and the lab.ls of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms lab.ling efficiency. Notably, it only takes NC-ALG one-third of the lab.ling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks lab.ling Noise measurement