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AlbNews: A Corpus of Headlines for Topic Modeling in Albanian

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arXiv 2024年

作者： Çano, Erion Lamaj, Dario Digital Philology Data Mining and Machine Learning University of Vienna Austria Cognitive Science Department of Applied Informatics Comenius University Bratislava Slovakia

The scarcity of available text corpora for low-resource languages like Albanian is a serious hurdle for research in natural language processing tasks. This paper introduces AlbNews, a collection of 600 topically labeled news headlines and 2600 unlabeled ones in Albanian. The data can be freely used for conducting topic modeling research. We report the initial classification scores of some traditional machine learning classifiers trained with the AlbNews samples. These results show that basic models outrun the ensemble learning ones and can serve as a baseline for future experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： learning algorithms

More powerful multiple testing under dependence via randomization

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arXiv 2023年

作者： Xu, Ziyu Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Departments of Statistics and Data Science and Machine Learning Carnegie Mellon University United States

We show that two procedures for false discovery rate (FDR) control — the Benjamini-Yekutieli procedure for dependent p-values, and the e-Benjamini-Hochberg procedure for dependent e-values — can both be made more powerful by a simple randomization involving one independent uniform random variable. As a corollary, the Hommel test under arbitrary dependence is also improved. Importantly, our randomized improvements are never worse than the originals and are typically strictly more powerful, with marked improvements in simulations. The same technique also improves essentially every other multiple testing procedure based on e-values. Copyright © 2023, The Authors. All rights reserved.

关键词： Random processes

Pattern Discovery in an EEG database of Depression Patients: Preliminary Results 14

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Pattern Discovery in an EEG Database of Depression Patients:...

14th International Conference on Measurement, MEASUREMENT 2023

作者： Hlavackova-Schindler, Katerina Pacher, Christina Plant, Claudia Lazarenko, Mykola Palus, Milan Hlinka, Jaroslav Kathpalia, Aditi Brunovsky, Martin University of Vienna Data Mining and Machine Learning Research Group Faculty of Computer Science Vienna Austria Czech Academy of Sciences Institute of Computer Science Department of Complex Systems Prague Czech Republic National Institute of Mental Health Clinical Research Programme Klecany Czech Republic

ISBN: (纸本)9788097262976

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient's brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants"ongoing at the University of Vienna, the Czech Academy of sciences and the National Institute of Mental Health in the Czech Republic. © 2023 Institute of Measurement science, Slovak Academy o.

关键词： data mining

Online multiple testing with e-values

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arXiv 2023年

A scientist tests a continuous stream of hypotheses over time in the course of her investigation — she does not test a predetermined, fixed number of hypotheses. The scientist wishes to make as many discoveries as possible while ensuring the number of false discoveries is controlled — a well recognized way for accomplishing this is to control the false discovery rate (FDR). Prior methods for FDR control in the online setting have focused on formulating algorithms when specific dependency structures are assumed to exist between the test statistics of each hypothesis. However, in practice, these dependencies often cannot be known beforehand or tested after the fact. Our algorithm, e-LOND, provides FDR control under arbitrary, possibly unknown, dependence. We show that our method is more powerful than existing approaches to this problem through simulations. We also formulate extensions of this algorithm to utilize randomization for increased power, and for constructing confidence intervals in online selective inference. Copyright © 2023, The Authors. All rights reserved.

关键词： Inference engines

Exploring the Tangible Impact of Artificial Intelligence and machine learning: Bridging the Gap between Hype and Reality 1

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Exploring the Tangible Impact of Artificial Intelligence and...

1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dattangire, Rahul Vaidya, Ruchika Biradar, Divya Joon, Ashish Publicis Sapient Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India University of Texas at Arlington Computer Science ArlingtonTX76019 United States Andhus Technologies Inc Software Developer PensacolaFL United States

ISBN: (纸本)9798350367720

Beyond being merely trendy terms in technology, machine learning (ML) and artificial intelligence (AI) have directly influenced every industry. This article thus demystifies the true meaning of these technologies by sifting through the sultry descriptions. Here, we explore how AI and ML enhance efficiency, decision-making, and problem-solving in various sectors including health, Agriculture, finance, and production as we discover real-world applications. Continuing with the discussion of implementation challenges, the aspects of bias, ethical pitfalls, and report cards are examined. In addition, the article also covered how the integration of AI as well as ML remains a way of improving processes, creating better user experiences, and spurring innovation. This paper, which is also based on case studies, discusses the relationship between social welfare and AI and the importance of AI's development and use being done responsibly. Considering all the discussed aspects, this volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions. © 2024 IEEE.

关键词： Adversarial machine learning

Uncertainty Quantification For Learned ISTA 33

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Uncertainty Quantification For Learned ISTA

33rd IEEE International Workshop on machine learning for Signal Processing, MLSP 2023

作者： Hoppe, Frederik Verdun, Claudio Mayrink Laus, Hannah Krahmer, Felix Rauhut, Holger Rwth Aachen University Chair of Mathematics of Information Processing Aachen Germany Technical University of Munich Department of Mathematics Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Technical University of Munich Munich Germany

ISBN: (纸本)9798350324112

Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretability. In addition, the incorporated prior domain knowledge can make the training more efficient as the smaller number of parameters allows the training step to be executed with smaller datasets. Algorithm unrolling schemes stand out among these model-based learning techniques. Despite their rapid advancement and their close connection to traditional high-dimensional statistical methods, they lack certainty estimates and a theory for uncertainty quantification is still elusive. This work provides a step towards closing this gap proposing a rigorous way to obtain confidence intervals for the LISTA estimator. © 2023 IEEE.

关键词： Deep learning

Automated quality assessment using appearance-based simulations and hippocampus segmentation on Low-field paediatric brain MR images

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arXiv 2024年

作者： Sundaresan, Vaanathi Dinsdale, Nicola K. Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India Oxford Machine Learning in NeuroImaging Lab Department of Computer Science University of Oxford Oxford United Kingdom

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses. © 2024, CC BY.

关键词： Magnetic resonance imaging

Federated learning in Medical Imaging: Part I: Toward Multicentral Health Care Ecosystems

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Journal of the American College of Radiology 2022年第8期19卷 969-974页

作者： Darzidehkalani, Erfan Ghasemi-rad, Mohammad van Ooijen, P.M.A. Department of Radiotherapy University Medical Center Groningen University of Groningen Groningen Netherlands Machine Learning Lab Data Science Center in Health University Medical Center Groningen University of Groningen Netherlands Assistant Professor of Radiology Department of Interventional Radiology Baylor College of Medicine Houston Texas Department of Radiotherapy University Medical Center Groningen University of Groningen Groningen Netherlands Coordinator Machine Learning Lab Data Science Center in Health University Medical Center Groningen University of Groningen Netherlands

With recent developments in medical imaging facilities, extensive medical imaging data are produced every day. This increasing amount of data provides an opportunity for researchers to develop data-driven methods and deliver better health care. However, data-driven models require a large amount of data to be adequately trained. Furthermore, there is always a limited amount of data available in each data center. Hence, deep learning models trained on local data centers might not reach their total performance capacity. One solution could be to accumulate all data from different centers into one center. However, data privacy regulations do not allow medical institutions to easily combine their data, and this becomes increasingly difficult when institutions from multiple countries are involved. Another solution is to use privacy-preserving algorithms, which can make use of all the data available in multiple centers while keeping the sensitive data private. Federated learning (FL) is such a mechanism that enables deploying large-scale machine learning models trained on different data centers without sharing sensitive data. In FL, instead of transferring data, a general model is trained on local data sets and transferred between data centers. FL has been identified as a promising field of research, with extensive possible uses in medical research and practice. This article introduces FL, with a comprehensive look into its concepts and recent research trends in medical imaging. © 2022 American College of Radiology

关键词： Federated learning medical imaging privacy-preserving machine learning

Vehicle Prediction for BUS Identification Output from our Route Testing Real Time Algorithm 5

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Vehicle Prediction for BUS Identification Output from our Ro...

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Misba, M. Ramya, R. Dickson, Joel Sharmila, L. Kavitha, J. Udayakumar, K. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu India St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India Bethlehem Institute of Engineering Tamil Nadu Karungal India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331530013

Traffic generation in Indian cities is of a mixed nature, comprising over a dozen types of vehicles that can be broadly categorized into slow-moving and fast-moving vehicles. This study highlights the challenges associated with the inefficient operation of the existing bus system. GPS and mobile networks form integral components of the current system, enabling data storage and management using PostgreSQL. The system provides robust and reliable data to fulfill requests from other software applications. Operating on cloud platforms, it is compatible with all major web browsers. The proposed algorithm continuously tracks the real-time location of buses and their corresponding WMS (Web Map Service) location, alerting users through marker notifications. This paper also explores measures to evaluate the efficiency and effectiveness of bus services. Recognizing that an inadequate public transport system impedes economic growth and development, this study emphasizes the need to enhance the overall quality and accessibility of public transportation. The findings aim to improve the operational efficiency of bus services, contributing to a more effective and sustainable transport system. © 2024 IEEE.

关键词： Buses