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arXiv 2022年

作者： Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Germany Munich Center for Machine Learning Germany Department of Data Analysis and Mathematical Modeling Ghent University Belgium

Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The latter refers to the learner's (lack of) knowledge and appears to be especially difficult to measure and quantify. In this paper, we analyse a recent proposal based on the idea of a second-order learner, which yields predictions in the form of distributions over probability distributions. While standard (first-order) learners can be trained to predict accurate probabilities, namely by minimising suitable loss functions on sample data, we show that loss minimisation does not work for second-order predictors: The loss functions proposed for inducing such predictors do not incentivise the learner to represent its epistemic uncertainty in a faithful *** Codes 68T37 (Primary) 68T30 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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Blessemflood21: Advancing Flood analysis with a High-Resolution Georeferenced dataset for Humanitarian Aid Support

Blessemflood21: Advancing Flood Analysis with a High-Resolut...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vladyslav Polushko Alexander Jenal Jens Bongartz Immanuel Weber Damjan Hatic Ronald Rösch Thomas März Markus Rauhut Andreas Weinmann Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. Computer Vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery.

关键词： Deep learning Training Semantic segmentation Human in the loop Sensors Floods Task analysis

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Hierarchical Multiview Top-k Pooling with Deep-Q-Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2985-2996页

作者： Li, Zhi-Peng Su, Hai-Long Wu, Yong- Zhang, Qin-Hu Yuan, Chang-An Gribova, Valeriya Filaretov, Vladimir Fedorovich Huang, De-Shuang Eastern Institute of Technology Zhejiang Ningbo315201 China University of Science and Technology of China School of Life Sciences Anhui Hefei230026 China Tongji University Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Shanghai201804 China Guangxi Academy of Sciences Institute of Big Data and Intelligent Computing Research Center Nanning530007 China Far Eastern Branch of the Russian Academy of Sciences Institute of Automation and Control Processes Vladivostok690041 Russia

Graph neural networks (GNNs) are extensions of deep neural networks to graph-structured data. It has already attracted widespread attention for various tasks such as node classification and link prediction. Existing research focuses more on graph convolutional neural networks (GCNs). However, it is usually overlooked that graph pooling can obtain graph representations by summarizing and down-sampling node information. Meanwhile, existing graph pooling methods mainly use top-k for node selection, but most of them consider only single view information when scoring nodes, and the k values in top-k are usually selected empirically. This work proposes the hierarchical multiview top-k pooling (HMTPool) with deep-Q-networks, which scores nodes taking into account multiview information (considering graph structure and features) and does not rely on the empirical adaptive selection of the best k value. HMTPool is a two-stage process. It first uses a variant GCN and multilayer perceptron to score the nodes from structural and feature perspectives, respectively and then performs fusion operations on the multiview information scores of the nodes. In addition, to select the optimal pooling ratio of top-k, we propose a deep-Q-network-based top-k (DTop-k) node selection method, which can adaptively select the best pooling ratio without prior knowledge. Experimental results on six TUdatasets and two Benchmarking GNNs datasets demonstrate the effectiveness of our proposed approach. © 2020 IEEE.

关键词： Convolution

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Towards Highly Efficient Anomaly Detection for Predictive Maintenance

Towards Highly Efficient Anomaly Detection for Predictive Ma...

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International Conference on machine learning and Applications (ICMLA)

作者： Simon Klüttermann Vanlal Peka Philipp Doebler Emmanuel Müller TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Research Center Trustworthy Data Science and Security Dortmund Germany

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

This paper introduces SEAN, a novel anomaly detection algorithm designed for real-time applications in predictive maintenance. SEAN leverages an ensemble-based approach to deliver competitive performance while drastically reducing computational costs. In our comprehensive evaluation across 121 datasets, SEAN consistently outperforms comparable shallow anomaly detection algorithms. Our comparisons reveal that SEAN operates over 20,000 times faster than a similar state-of-the-art deep learning alternative, with negligible sacrifice in detection accuracy. We further demonstrate SEAN's versatility through an ablation study, highlighting how its hyperparameters can be tuned to balance runtime and performance effectively. Additionally, we present a practical C++ export tool that enables the deployment of SEAN on resource-constrained devices, meeting the stringent requirements of on-device predictive maintenance tasks. Our findings underscore SEAN as a powerful and efficient solution for anomaly detection in real-world engineering applications.

关键词： Performance evaluation Accuracy Runtime C++ languages Prediction algorithms Real-time systems Computational efficiency Time factors Anomaly detection Predictive maintenance

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MSI-UNet: A Flexible UNet-Based Multi-Scale Interactive Framework for 3D Gastric Tumor Segmentation on CT Scans

MSI-UNet: A Flexible UNet-Based Multi-Scale Interactive Fram...

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IEEE International Symposium on Biomedical Imaging

作者： Heyun Chen Zifan Chen Jie Zhao Haoshen Li Jiazheng Li Yiting Liu Mingze Yuan Peng Bao Xinyu Nan Bin Dong Lei Tang Li Zhang Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Peking University Cancer Hospital&Institute China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate segmentation of gastric tumors is critical yet presents a formidable challenge in medical imaging, where conventional UNet-based frameworks, despite their prevalence, falter on intricate tumor samples due to their limited interactive capacities. The SAM-based segmentation methods address this shortcoming yet with insufficient accuracy. By ingeniously blending images with mask inputs, our MSI-UNet leverages a U-shaped design to deliver pixel-perfect segmentation accuracy, while a novel multi-scale attention module adeptly harnesses interaction points for refined information extraction. When benchmarked on gastric tumor segmentation tasks, MSI-UNet surpasses existing state-of-the-art methods, elevating the Dice Similarity Coefficient (DSC) from 74.82% to 79.3% and minimizing Average Surface Distance (ASD) from 6.46 to 1.98, achieving a comparable accuracy with inter-radiologist consistency of 79.7% DSC. Furthermore, our framework demonstrates superior predictive prowess in survival analysis, enhancing the C-index from 61.7% to 68.68%. Ample experimental comparisons have substantiated that MSI-UNet holds the potential to offer considerable assistance to healthcare professionals in managing and decoding subsequent medical procedures.

关键词： Image segmentation Accuracy Three-dimensional displays Medical services Predictive models Information retrieval Decoding

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SiriusBI: Building End-to-End Business Intelligence Enhanced by Large Language Models

arXiv

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arXiv 2024年

作者： Jiang, Jie Xie, Haining Shen, Yu Zhang, Zihan Lei, Meng Zheng, Yifeng Fang, Yide Li, Chunyou Huang, Danqing Zhang, Wentao Li, Yang Yang, Xiaofeng Cui, Bin Chen, Peng Department of Data Platform TEG Tencent Inc. China School of Computer Science Peking University China Center of Machine Learning Research Peking University China

The rapid advancement of AI technologies, particularly Large Language Models (LLMs), is establishing a new paradigm for Business Intelligence (BI). Despite the emergence of pioneering work in enhancing BI systems with LLMs, we have identified the following three issues when deployed in real industrial scenarios: interaction limitations, performance bottlenecks, and functionality deficiencies. In this paper, we present SiriusBI, an end-to-end business intelligence system that is designed to address the three issues simultaneously. First, we propose an intelligent and application-oriented module called multi-round dialogue with querying, which aims to overcome the prevalent interaction limitations in current BI solutions. Next, to mitigate the performance bottlenecks caused by scenario migration, we introduce two SQL generation methods that strike a balance between accuracy and deployment costs. Finally, to tackle the practical challenges posed by functionality deficiencies, we develop an end-to-end workflow that covers the entire BI process, ensuring that SiriusBI delivers a robust and complete set of functionalities. As an independent cloud service in Tencent's data platform, SiriusBI has been applied across Tencent's finance, advertising, and cloud sectors, providing services to dozens of enterprise clients. Experiments on real-world datasets and practical applications in industrial BI scenarios demonstrate the practicality and effectiveness of SiriusBI. Remarkably, SiriusBI achieves remarkable accuracy rates of 97% in SQL generation for Tencent Finance, 89% for Tencent Advertisement, and 91% for Tencent Cloud. © 2024, CC BY.

关键词： Competitive intelligence

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Process-Aware Bayesian Networks for Sequential Event Log Queries

Process-Aware Bayesian Networks for Sequential Event Log Que...

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International Conference on Process Mining (ICPM)

作者： Simon Rauch Christian M. M. Frey Ludwig Zellner Thomas Seidl Database Systems and Data Mining LMU Munich Munich Germany Center for Applied Research on Supply Chain Services Fraunhofer IIS Nuremberg Germany Munich Center for Machine Learning Munich Germany

ISBN: (数字)9798350365030

ISBN: (纸本)9798350365047

Business processes from many domains like manufacturing, healthcare, or business administration suffer from different amounts of uncertainty concerning the execution of individual activities and their order of occurrence. As long as a process is not entirely serial, i.e., there are no forks or decisions to be made along the process execution, we are - in the absence of exhaustive domain knowledge - confronted with the question whether and in what order activities should be executed or left out for a given case and a desired outcome. As the occurrence or non-occurrence of events has substantial implications regarding process key performance indicators like throughput times or scrap rate, there is ample need for assessing and modeling that process-inherent uncertainty. We propose a novel way of handling the uncertainty by leveraging the probabilistic mechanisms of Bayesian Networks to model processes from the structural and temporal information given in event log data and offer a comprehensive evaluation of uncertainty by modelling cases in their entirety. In a thorough analysis of well-established benchmark datasets, we show that our Process-aware Bayesian Network is capable of answering process queries concerned with any unknown process sequence regarding activities and/or attributes enhancing the explainability of processes. Our method can infer execution probabilities of activities at different stages and can query probabilities of certain process outcomes. The key benefit of the Process-aware Query System over existing approaches is the ability to deliver probabilistic, case-diagnostic information about the execution of activities via Bayesian inference.

关键词： Process monitoring Process mining Uncertainty Medical services Predictive models Probabilistic logic Throughput

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Investor Risk Profile Determination Model

Investor Risk Profile Determination Model

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

An assessment of the investor’s risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market.

关键词：

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RVPD: An Automated System for Calculating the Tortuosity and Bifurcation Angles of Retinal Vessels to Predict Diseases

RVPD: An Automated System for Calculating the Tortuosity and...

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IEEE International Symposium on Biomedical Imaging

作者： Guangzhengao Yang Xingyu Luo Jie Zhao Fangfang Fan Haoshen Li Bin Dong Li Zhang Yan Zhang Center for Data Science Peking University China Department of Cardiology Peking University First Hospital China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Hypertension and diabetes are known to potentially cause morphological changes in the retinal capillary system, yet quantifying these changes presents significant challenges. This research addresses this issue by designing and developing an automated system capable of analyzing retinal images to detect and measure these changes. Our system automatically segments vessels and the optic disc, calculates tortuosity and bifurcation angles, and performs disease prediction based on these measurements along with clinical features. For tortuosity calculation, we adopted a novel curve fitting method, which provides more stable convergence compared to traditional discrete numerical methods. The system integrates clinical data and conducts prediction experiments on hypertensive and diabetic patients with varying sample sizes. Our results demonstrate the system's strong performance and scalability: in the hypertension group, the AUC reached a maximum of 0.814 ± 0.023, with an ACC of 0.735 ± 0.033, while in the diabetes group, the AUC reached a maximum of 0.710 ± 0.043, with an ACC of 0.641 ± 0.052. These findings suggest that our automated system could serve as a valuable tool for early detection and risk assessment of hypertension and diabetes, potentially improving patient outcomes through timely intervention.

关键词： Hypertension Image segmentation Scalability Bifurcation Optical variables measurement Optical imaging Retinal vessels Diabetes Risk management Diseases

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Perception datasets for Anomaly Detection in Autonomous Driving: A Survey

arXiv

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arXiv 2023年

作者： Bogdoll, Daniel Uhlemeyer, Svenja Kowol, Kamil Zöllner, J. Marius FZI Research Center for Information Technology Germany Karlsruhe Institute of Technology Germany University of Wuppertal Germany Interdisciplinary Center for Machine Learning and Data Analytics Germany

Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance in all kinds of situations. However, these DNN are usually restricted to a closed set of semantic classes available in their training data, and are therefore unreliable when confronted with previously unseen instances. Thus, multiple perception datasets have been created for the evaluation of anomaly detection methods, which can be categorized into three groups: real anomalies in real-world, synthetic anomalies augmented into real-world and completely synthetic scenes. This survey provides a structured and, to the best of our knowledge, complete overview and comparison of perception datasets for anomaly detection in autonomous driving. Each chapter provides information about tasks and ground truth, context information, and licenses. Additionally, we discuss current weaknesses and gaps in existing datasets to underline the importance of developing further data. © 2023, CC BY.

关键词： Autonomous vehicles

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