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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Vijaya Krishna Yalavarthi Johannes Burchert Lars Schmidt-Thieme Information Systems and Machine Learning Lab University of Hildesheim Germany

ISBN: (纸本)9781665473316

Asynchronous Time Series is a multivariate time series where all the channels are observed asynchronously-independently, making the time series extremely sparse when aligning them. We often observe this effect in applications with complex observation processes, such as health care, climate science, and astronomy, to name a few. Because of the asynchronous nature, they pose a significant challenge to deep learning architectures, which presume that the time series presented to them are regularly sampled, fully observed, and aligned with respect to time. This paper proposes a novel framework, that we call Deep Convolutional Set Functions (DCSF), which is highly scalable and memory efficient, for the asynchronous time series classification task. With the recent advancements in deep set learning architectures, we introduce a model that is invariant to the order in which time series’ channels are presented to it. We explore convolutional neural networks, which are well researched for the closely related problem-classification of regularly sampled and fully observed time series, for encoding the set elements. We evaluate DCSF for AsTS classification, and online (per time point) AsTS classification. Our extensive experiments on multiple real world and synthetic datasets verify that the suggested model performs substantially better than a range of state-of-the-art models in terms of accuracy and run time. We increase the accuracy of the mini-Physionet dataset upto 2%; real datasets with synthetic setups of both AsTS, and TSMV upto 30%.

关键词： Time series analysis Memory management Deep architecture Medical services Predictive models Encoding Convolutional neural networks

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End-to-End Image-Based Fashion Recommendation

arXiv

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arXiv 2022年

作者： Elsayed, Shereen Brinkmeyer, Lukas Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany

In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited to matrix factorization, auto-encoders, and nearest neighbor models. While there are numerous image-based recommender approaches that utilize dedicated deep neural networks, comparisons to attribute-aware models are often disregarded despite their ability to be easily extended to leverage items' image features. In this paper, we propose a simple yet effective attribute-aware model that incorporates image features for better item representation learning in item recommendation tasks. The proposed model utilizes items' image features extracted by a calibrated ResNet50 component. We present an ablation study to compare incorporating the image features using three different techniques into the recommender system component that can seamlessly leverage any available items' attributes. Experiments on two image-based real-world recommender systems datasets show that the proposed model significantly outperforms all state-of-the-art image-based models. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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On the Potential of Using ERP Business and System Data for Fraud Detection

On the Potential of Using ERP Business and System Data for F...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Schnepf, Jonas Vetter, Paula Temel, Tarik Scheuermann, Bernd Schmidt-Thieme, Lars University of Applied Sciences Karlsruhe Faculty of Management Science and Engineering Karlsruhe Germany Institute of Computer Science University of Hildesheim Information Systems and Machine Learning Lab Hildesheim Germany

ISBN: (纸本)9781665480451

Enterprise Resource Planning (ERP) systems are used to support and to control the business processes of a company or organization. Such systems integrate the data across the entire company into a complete system that is capable of enhancing the key operations in virtually any department. Commonly, running an ERP system helps companies operate more efficiently, however, this also leads to problems. Employees are able to enrich themselves through insider knowledge or by exploiting incomplete or incorrect permission settings. This is referred to as occupational fraud. Since ERP systems keep records of all executed business activities and log system events and permission checks, they provide a variety of different data sources that can be used to detect occupational fraud. This paper reviews existing literature in the area of fraud detection and fraud cases based on ERP data including business data and system data. Using the most widespread ERP system from SAP, the potential and the suitability of the various data sources with respect to fraud detection is examined. © 2022 IEEE.

关键词： Enterprise resource planning

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Is Data All That Matters? the Role of Control Frequency for learning-Based Sampled-Data Control of Uncertain systems

Is Data All That Matters? the Role of Control Frequency for ...

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American Control Conference (ACC)

作者： Ralf Römer Lukas Brunke Siqi Zhou Angela P. Schoellig Learning Systems and Robotics Lab (***) School of Computation Information and Technology and the Munich Institute for Robotics and Machine Intelligence (MIRMI) Technical University of Munich Germany

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

learning models or control policies from data has become a powerful tool to improve the performance of uncertain systems. While a strong focus has been placed on increasing the amount and quality of data to improve performance, data can never fully eliminate uncertainty, making feedback necessary to ensure stability and performance. We show that the control frequency at which the input is recalculated is a crucial design parameter, yet it has hardly been considered before. We address this gap by combining probabilistic model learning and sampled-data control. We use Gaussian processes (GPs) to learn a continuous-time model and compute a corresponding discrete-time controller. The result is an uncertain sampled-data control system, for which we derive robust stability conditions. We formulate semidefinite programs to compute the minimum control frequency required for stability and to optimize performance. As a result, our approach enables us to study the effect of both control frequency and data on stability and closed-loop performance. We show in numerical simulations of a quadrotor that performance can be improved by increasing either the amount of data or the control frequency, and that we can trade off one for the other. For example, by increasing the control frequency by 33%, we can reduce the number of data points by half while still achieving similar performance.

关键词： Uncertain systems Uncertainty Robust stability Computational modeling Control systems Stability analysis Data models

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GQFormer: A Multi-Quantile Generative Transformer for Time Series Forecasting

GQFormer: A Multi-Quantile Generative Transformer for Time S...

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IEEE International Conference on Big Data

作者： Shayan Jawed Lars Schmidt-Thieme Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany

ISBN: (纸本)9781665480468

We propose GQFormer, a probabilistic time series forecasting method that models the quantile function of the forecast distribution. Our methodology is rooted in the Implicit Quantile modeling approach, where samples from the Uniform distribution $\mathcal{U}\left( {0,1} \right)$ are reparameterized to quantile values of the target distribution. This allows implicit generative quantile modeling without any prior assumptions on the data distribution like Gaussianity, common in prior works. Our work is distinguished from prior quantile forecasting methods by novel methodological advances that relate to directly modeling the correlations among multiple quantile estimations at each forecasting horizon. To this end, we firstly develop a parameters haring architecture that implicitly models multiple quantile estimations efficiently and secondly regularize these through a novel multi-task loss function formulation that optimizes for quantile estimations to be sharper estimations individually and on the whole be spread maximally apart to capture the various modes of the underlying distribution. We experimentally validate the superiority of the method to state-of-the-art probabilistic forecasting baselines and ablations to the loss formulation.

关键词： Correlation Time series analysis Estimation Predictive models Big Data Probabilistic logic Multitasking

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Anomaly Heterogeneity learning for Open-Set Supervised Anomaly Detection

Anomaly Heterogeneity Learning for Open-Set Supervised Anoma...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jiawen Zhu Choubo Ding Yu Tian Guansong Pang School of Computing and Information Systems Singapore Management University Australian Institute for Machine Learning University of Adelaide Harvard Ophthalmology AI Lab Harvard University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to de-tect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies, current OSAD methods can often largely reduce false positive errors. However, these methods are trained in a closed-set setting and treat the anomaly examples as from a homogeneous distribution, rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. This paper proposes to learn heterogeneous anomaly distributions using the limited anomaly examples to address this issue. To this end, we introduce a novel approach, namely Anomaly Heterogeneity learning (AHL), that simulates a diverse set of heterogeneous anomaly distributions and then utilizes them to learn a unified heterogeneous abnormality model in surrogate open-set environments. Further, AHL is a generic framework that existing OSAD models can plug and play for enhancing their abnormality modeling. Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art OSAD models in detecting seen and unseen anomalies, and 2) effectively generalize to unseen anomalies in new domains. Code is available at https://***/mala-lab/AHL.

关键词： Training Computer vision Codes Computational modeling Collaboration Rendering (computer graphics) Pattern recognition

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DeepStay: Stay Region Extraction from Location Trajectories Using Weak Supervision

DeepStay: Stay Region Extraction from Location Trajectories ...

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International Conference on Intelligent Transportation

作者： Christian Löwens Daniela Thyssens Emma Andersson Christina Jenkins Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim Hildesheim Germany Devoteam Creative Tech Malmö Sweden

Nowadays, mobile devices enable constant tracking of the user's position and location trajectories can be used to infer personal points of interest (POIs) like homes, workplaces, or stores. A common way to extract POIs is to first identify spatio-temporal regions where a user spends a significant amount of time, known as stay regions (SRs). Common approaches to SR extraction are evaluated either solely unsupervised or on a small-scale private dataset, as popular public datasets are unlabeled. Most of these methods rely on hand-crafted features or thresholds and do not learn beyond hyperparameter optimization. Therefore, we propose a weakly and self-supervised transformer-based model called DeepStay, which is trained on location trajectories to predict stay regions. To the best of our knowledge, this is the first approach based on deep learning and the first approach that is evaluated on a public, labeled dataset. Our SR extraction method outperforms state-of-the-art methods. In addition, we conducted a limited experiment on the task of transportation mode detection from GPS trajectories using the same architecture and achieved significantly higher scores than the state-of-the-art. Our code is available at https://***/christianll9/deepstay.

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Leveraging Group Annotations in Object Detection Using Graph-Based Pseudo-labeling 43rd

Leveraging Group Annotations in Object Detection Using Grap...

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43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021

作者： Pototzky, Daniel Kirschner, Matthias Schmidt-Thieme, Lars Robert Bosch GmbH Gerlingen Germany Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany

ISBN: (纸本)9783030926588

We address the problem of dealing with group annotations in object detection where a multitude of items is included in a single bounding box. The standard training protocols in use for most datasets either ignore anchors overlapping with group annotations in the loss function or discard group annotations completely. In this paper, we argue that group annotations contain unexplored potential in many commonly used datasets. We propose to leverage group annotations by generating pseudo-labels on top of them. We develop a novel graph-based pseudo-label generation method that interprets pseudo-label creation as a graph clustering problem that is suited to deal with overlapping objects. In experiments on CityPersons, MS COCO, and a subset of OpenImages we show that our approach outperforms the usual training strategies on the respective datasets when dealing with group annotations as well as other pseudo-label generation methods. We find that the greater the share of group annotations, the larger the increase in performance. © 2021, Springer Nature Switzerland AG.

关键词： Graphic methods

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Self-supervised learning for Object Detection in Autonomous Driving 43rd

Self-supervised Learning for Object Detection in Autonomou...

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43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021

作者： Pototzky, Daniel Sultan, Azhar Kirschner, Matthias Schmidt-Thieme, Lars Robert Bosch GmbH Gerlingen Germany Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany

ISBN: (纸本)9783030926588

Recently, self-supervised pretraining methods have achieved impressive results, matching ImageNet weights on a variety of downstream tasks including object detection. Despite their success, these methods have some limitations. Most of them are optimized for image classification and compute only a global feature vector describing an entire image. On top of that, they rely on large batch sizes, a huge amount of unlabeled data and vast computing resources to work well. To address these issues, we propose SSLAD, a Self-Supervised learning approach especially suited for object detection in the context of Autonomous Driving. SSLAD computes local image descriptors that are invariant to augmentations and scale. In experiments, we show that our method outperforms state-of-the-art self-supervised pretraining methods, on various object detection datasets from the automotive domain. By leveraging just 20,000 unlabeled images taken from a video camera on a car, SSLAD almost matches ImageNet weights trained on 1,200,000 labeled images. If as few as 20 unlabeled images are available, SSLAD generates weights that are far better than a random initialization, whereas competing self-supervised methods just do not work given so little data. Furthermore, SSLAD is very robust with respect to batch size. Even with a batch size of one, SSLAD generates weights that are clearly superior to a random initialization while greatly outperforming other self-supervised methods. This property of SSLAD even allows for single-GPU training with only a minor decrease in performance. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Deep learning-Based Prediction of Daily COVID-19 Cases Using X (Twitter) Data

Deep Learning-Based Prediction of Daily COVID-19 Cases Using...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Ahmed, Nourhan Saeed, Khansa Rodrigues, Jeevitha Lora Naeem, Maha Correa, Andrea Sanabboon, Chairungroj Rostam Niakan Kalhori, Sharareh Deserno, Thomas M. Information Systems and Machine Learning Lab Department of Mathematics Natural Science Economics and Computer Science Institute of Computer Science University of Hildesheim Germany Peter L. Reichertz Institute for Medical Informatics TU Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran

ISBN: (纸本)9781643685335

Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer. We develop and evaluate a time series mixer (TSMixer) predictive model for multivariate time series. The mean squared error (MSE) loss on the test dataset was 0.0063 for 24-month Gvalue prediction when using the MinMax normalization with recursive feature elimination (RFE) and average or min aggregation method. Our findings illuminate the potential of integrating social media data to enhance daily COVID-19 case predictions and are applicable also for epidemiological forecasting purposes. © 2024 The Authors.

关键词： Tweets

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