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Overview of the Tenth Dialog System Technology Challenge: DSTC10

IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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Understanding telecom customer churn with machine learning: From prediction to causal inference 31st

Understanding telecom customer churn with machine learning: ...

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31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on machine learning, BNAIC/BENELEARN 2019

作者： Verhelst, Théo Caelen, Olivier Dewitte, Jean-Christophe Lebichot, Bertrand Bontempi, Gianluca Machine Learning Group Computer Science Department Université Libre de Bruxelles Brussels Belgium Data Science Team Orange Belgium

ISBN: (纸本)9783030651534

Telecommunication companies are evolving in a highly competitive market where attracting new customers is much more expensive than retaining existing ones. Though retention campaigns may be used to prevent customer churn, their success depends on the availability of accurate prediction models. Churn prediction is notoriously a difficult problem because of the large amount of data, non-linearity, imbalance and low separability between the classes of churners and non-churners. In this paper, we discuss a real case of churn prediction based on Orange Belgium customer data. In the first part of the paper we focus on the design of an accurate prediction model. The large class imbalance between the two classes is handled with the EasyEnsemble algorithm using a random forest classifier. We assess also the impact of different data preprocessing techniques including feature selection and engineering. Results show that feature selection can be used to reduce computation time and memory requirements, though engineering variables does not necessarily improve performance. In the second part of the paper we explore the application of data-driven causal inference, which aims to infer causal relationships between variables from observational data. We conclude that the bill shock and the wrong tariff plan positioning are putative causes of churn. This is supported by the prior knowledge of experts at Orange Belgium. Finally, we present a novel method to evaluate, in terms of the direction and magnitude, the impact of causally relevant variables on churn, making the assumption of no confounding factors. © 2020, Springer Nature Switzerland AG.

关键词： Big data Causal inference Churn prediction machine learning

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Florid – a Nationwide Identification Service for Plants from Photos and Habitat Information

SSRN

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SSRN 2024年

作者： Brun, Philipp de Witte, Lucienne Popp, Manuel Richard Zurell, Damaris Karger, Dirk Nikolaus Descombes, Patrice de Lutio, Riccardo Wegner, Jan Dirk Bornand, Christophe Eggenberg, Stefan Olevski, Tasko Zimmermann, Niklaus E. Swiss Federal Research Institute WSL Birmensdorf8903 Switzerland Musée et jardins botaniques cantonaux Lausanne1007 Switzerland Institute of Biochemistry and Biology University of Potsdam Potsdam14469 Germany EcoVision Lab Photogrammetry and Remote Sensing ETH Zurich Zürich8092 Switzerland Department of Mathematical Modeling and Machine Learning University of Zurich Zürich8057 Switzerland InfoFlora Switzerland Bern3013 Switzerland Swiss Data Science Center ETH Zurich Zürich8092 Switzerland University of Basel Switzerland

Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and many non-native plants of Switzerland. FlorID can identify >3000 species, using vision transformers trained on 1.5M photos, and ecological predictions from multilayer perceptrons, trained on 6.7M occurrence observations and 20 high-resolution environmental variables. Embedded in a free-to-use application programming interface, FlorID can be accessed directly, via webservice, and via FlorApp smartphone application. If multiple images and spatiotemporal location are available, FlorID correctly identifies 93% of field observations and has a top-5 accuracy of 99%. Ecological predictions boost identification success especially for native species with distinct distributions. By evaluating information on appearance and fine-grained ecology, FlorID is a blueprint for similar solutions targeting different taxa or regions, and a basis for developments like automated community inventories. © 2024, The Authors. All rights reserved.

关键词： Blueprints

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

arXiv

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on Computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

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TEEM: Two-Factor Energy Evaluation Metric Toward Green Big data System

TEEM: Two-Factor Energy Evaluation Metric Toward Green Big D...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Weidong Fang Chunsheng Zhu Mohsen Guizani Zhiqi Li Wuxiong Zhang Joel J.P.C. Rodrigues Science and Technology on Micro-system Laboratory Shanghai Institute of Micro-system and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China Shanghai Research and Development Center for Micro-Nano Electronics Shanghai China College of Big Data and Internet Shenzhen Technology University Shenzhen China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE COPELABS Lusófona University Lisbon Portugal

Toward green Big data System (BDS), one of the key requirements is to save energy consumption so that the system lifetime can be prolonged. Hence, the energy evaluation metric for the measurement of energy efficiency in green BDS, is very critical. Unfortunately, most current energy evaluation metrics are based on a single factor, which might be difficult to meet the diverse application requirements. In this paper, we propose a novel two-factor energy evaluation metric (TEEM) for green BDS. Specifically, the transmission distance and the modulation level are taken into account simultaneously, generating a metric named the bit-per energy consumption (BEC). Extensive simulation results demonstrate that the system performance in energy consumption can be more effectively evaluated by BEC.

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Effective resampling approach for skewed distribution on imbalanced data set

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2020年第2期47卷 234-249页

作者： Nwe, Mar Mar Lynn, Khin Thidar Data Mining and Machine Learning Lab University of Computer Studies Mandalay Myanmar Faculty of Information Science Department University of Computer Studies Mandalay Myanmar

Accurate classification of unknown input data for imbalanced data sets is difficult, because the predictions of learning classifiers tend to be biased towards the majority class and ignore the minority class. Moreover, the class distribution of imbalanced data has a significant impact on the misclassification rate of the learning classifier. So, this paper introduces an effective data pre-processing approach to improve the efficiency of imbalanced data classification, focusing on the skewed distribution of data points in the imbalanced data set. This proposed approach involves over-sampling and under-sampling techniques based on k-means clustering to overcome the problems associated with imbalanced learning of small disjuncts and small sample size. And, Tomek Link-based under-sampling method is also incorporated into the proposed cluster-based resampling methods to solve the class overlapping problem by eliminating the majority samples in overlapping regions. Experiments are performed on the 25 standard imbalanced data sets by applying four learning classifiers, and validated with the three popular metrics (i.e. Area Under the Curve (AUC), Geometric Mean (G-mean) and Balanced Accuracy (BA)). Specially, we show that the proposed approach has outperformed the other state-of-the-art resampling methods using performance metrics, probabilistic estimation, statistical analysis and multicriteria decision-making methodology (MCDM). © 2020, International Association of Engineers.

关键词： Classification (of information)

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Computationally Efficient Approximations for Matrix-based Rényi's Entropy

arXiv

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arXiv 2021年

作者： Gong, Tieliang Dong, Yuxin Yu, Shujian Dong, Bo The School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an710049 China The Machine Learning Group UiT - The Arctic University of Norway Department of Computer Science Vrije University Amsterdam Amsterdam Netherlands

The recently developed matrix-based Rényi's αorder entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi-definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution. This intriguing property makes this new information measurement widely adopted in multiple statistical inference and learning tasks. However, the computation of such quantity involves the trace operator on a PSD matrix G to power α (i.e., tr(Gα)), with a normal complexity of nearly O(n3), which severely hampers its practical usage when the number of samples (i.e., n) is large. In this work, we present computationally efficient approximations to this new entropy functional that can reduce its complexity to even significantly less than O(n2). To this end, we leverage the recent progress on Randomized Numerical Linear Algebra, developing Taylor, Chebyshev and Lanczos approximations to tr(Gα) for arbitrary values of α by converting it into a matrix-vector multiplication problem. We also establish the connection between the matrix-based Rényi's entropy and PSD matrix approximation, which enables exploiting both clustering and block low-rank structure of G to further reduce the computational cost. We theoretically provide approximation accuracy guarantees and illustrate the properties for different approximations. Large-scale experimental evaluations on both synthetic and real-world data corroborate our theoretical findings, showing promising speedup with negligible loss in accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Matrix algebra

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Distribution-free binary classification: Prediction sets, confidence intervals and calibration

arXiv

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arXiv 2020年

作者： Gupta, Chirag Podkopaev, Aleksandr Ramdas, Aaditya Machine Learning Department Carnegie Mellon University United States Department of Statistics and Data Science Carnegie Mellon University United States

We study three notions of uncertainty quantification-calibration, confidence intervals and prediction sets-for binary classification in the distribution-free setting, that is without making any distributional assumptions on the data. With a focus towards calibration, we establish a 'tripod' of theorems that connect these three notions for score-based classifiers. A direct implication is that distribution-free calibration is only possible, even asymptotically, using a scoring function whose level sets partition the feature space into at most countably many sets. Parametric calibration schemes such as variants of Platt scaling do not satisfy this requirement, while nonparametric schemes based on binning do. To close the loop, we derive distribution-free confidence intervals for binned probabilities for both fixed-width and uniform-mass binning. As a consequence of our 'tripod' theorems, these confidence intervals for binned probabilities lead to distribution-free calibration. We also derive extensions to settings with streaming data and covariate shift. Copyright © 2020, The Authors. All rights reserved.

关键词： Calibration

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On conditional versus marginal bias in multi-armed bandits

arXiv

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arXiv 2020年

作者： Shin, Jaehyeok Rinaldo, Alessandro Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

The bias of the sample means of the arms in multiarmed bandits is an important issue in adaptive data analysis that has recently received considerable attention in the literature. Existing results relate in precise ways the sign and magnitude of the bias to various sources of data adaptivity, but do not apply to the conditional inference setting in which the sample means are computed only if some specific conditions are satisfied. In this paper, we characterize the sign of the conditional bias of monotone functions of the rewards, including the sample mean. Our results hold for arbitrary conditioning events and leverage natural monotonicity properties of the data collection policy. We further demonstrate, through several examples from sequential testing and best arm identification, that the sign of the conditional and marginal bias of the sample mean of an arm can be different, depending on the conditioning event. Our analysis offers new and interesting perspectives on the subtleties of assessing the bias in data adaptive settings. Copyright © 2020, The Authors. All rights reserved.

关键词： machine learning

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machine learning to detect recent recreational drug use in intensive cardiac care units

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Archives of Cardiovascular Diseases 2025年第5期118卷 277-286页

作者： El Bèze, Nathan Hamzi, Kenza Henry, Patrick Trimaille, Antonin El Ouahidi, Amine Zakine, Cyril Nallet, Olivier Delmas, Clément Aboyans, Victor Goralski, Marc Albert, Franck Bonnefoy-Cudraz, Eric Bochaton, Thomas Schurtz, Guillaume Lim, Pascal Lequipar, Antoine Gonçalves, Trecy Gall, Emmanuel Pommier, Thibaut Lemarchand, Léo Meune, Christophe Azzakani, Sonia Bouleti, Claire Amar, Jonas Dillinger, Jean-Guillaume Steg, P. Gabriel Vicaut, Eric Toupin, Solenn Pezel, Théo Inserm MASCOT – UMRS 942 Department of Cardiology University Hospital of Lariboisière Université Paris-Cité AP–HP Paris 75010 France Multimodality Imaging Research for Analysis Core Laboratory: Artificial Intelligence (MIRACL.ai) Department of Data Science Machine Learning and Artificial Intelligence in Health University Hospital of Lariboisière AP–HP Paris 75010 France Department of Cardiovascular Medicine Nouvel Hôpital Civil Strasbourg University Hospital Strasbourg 67000 France Department of Cardiology University Hospital of Brest Brest 29609 France NCT+ Saint-Cyr-sur-Loire 37540 France Department of Cardiology Hôpital Montfermeil Montfermeil 93370 France Department of Cardiology Rangueil University Hospital Toulouse 31000 France Department of Cardiology University Hospital of Limoges Limoges 87000 France Department of Cardiology Centre Hospitalier d'Orléans Orléans 45100 France Department of Cardiology Centre Hospitalier de Chartres Le Coudray 28630 France Intensive Cardiological Care Division Louis-Pradel Hospital Hospices Civils de Lyon Bron 69500 France Department of Cardiology University Hospital of Lille Lille 59000 France Intensive Cardiac Care Unit Henri-Mondor University Hospital Créteil 94000 France Department of Cardiology Dijon University Hospital Dijon 21000 France Department of Cardiology and Vascular Diseases CHU of Rennes Rennes 35000 France Department of Cardiology Hôpital Avicenne AP–HP Bobigny 93000 France Department of Cardiology Clinical Investigation Centre (Inserm 1204) University Hospital of Poitiers Poitiers 86000 France Inserm_U1148/LVTS hôpital Bichat université Paris-Cité AP–HP Paris 75877 France Unité de recherche clinique hôpital Fernand-Widal AP–HP Paris 75010 France

Background: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this issue, machine learning methods could assist in the detection of recreational drug use. Aims: To investigate the accuracy of a machine learning model using clinical, biological and echocardiographic data for detecting recreational drug use in patients admitted to intensive cardiac care units. Methods: From 07 to 22 April 2021, systematic screening for all traditional recreational drugs (cannabis, opioids, cocaine, amphetamines, 3,4-methylenedioxymethamphetamine) was performed by urinary testing in all consecutive patients admitted to intensive cardiac care units in 39 French centres. The primary outcome was recreational drug detection by urinary testing. The framework involved automated variable selection by eXtreme Gradient Boosting (XGBoost) and model building with multiple algorithms, using 31 centres as the derivation cohort and eight other centres as the validation cohort. Results: Among the 1499 patients undergoing urinary testing for drugs (mean age 63 ± 15 years;70% male), 161 (11%) tested positive (cannabis: 9.1%;opioids: 2.1%;cocaine: 1.7%;amphetamines: 0.7%;3,4-methylenedioxymethamphetamine: 0.6%). Of these, only 57% had reported drug use. Using nine variables, the best machine learning model (random forest) showed good performance in the derivation cohort (area under the receiver operating characteristic curve = 0.82) and in the validation cohort (area under the receiver operating characteristic curve = 0.76). Conclusions: In a large intensive cardiac care unit cohort, a comprehensive machine learning model exhibited good performance in detecting recreational drug use, and provided valuable insights into the relationships between clinical variables and drug use through explainable machine learning techniques. ©

关键词： Intensive cardiac care machine learning Recreational drug use

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