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26th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2024

作者： Angerri, Xavier Delgado, Oscar Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Center Universtitat Politècnica de Catalunya Spain

ISBN: (纸本)9781643685434

When a Knowledge Discovery from data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtain preliminary insights from data before using KDD models is by generating a basic descriptive analysis. It is one of the most popular ways to describe experimental data and should be the beginning of all data projects. Nevertheless some of the main knowledge that can be extracted in a descriptive analysis is hidden due to underlying multivariate structures which could be elicited through multivariate analysis techniques. Moreover, the domain expert is key for a proper interpretation of descriptive results. At the same time, there is a lack of automatic reporting techniques that can report and help in the interpretation of complex patterns and the use of advanced multivariate techniques. This paper shows the tool developed to generate automatic interpretation of Multiple Correspondence Analysis (MCA) and Principal Components Analysis (PCA) by using RMarkdown. This tool generates a Word document which contains the automatic interpretation of the results, built on the basis of regular expressions ellaborating over the R analytical outputs (either numerical or graphical results). The proposal is being applied with some real data, like INSESS database on social vulnerabilities of the Catalan population. In conclusion, the developed tool contributes to facilitate the factorial methods results, avoiding the misinterpretation of the results and the involuntary skipping of conclusions due to the large amount of knowledge that can be extracted from a complete factorial analysis. Also, this software enables non-expert users to read multivariate analysis results in a friendly way. Moreover, this tool saves time in the interpretation step and is a basis to support the expert to start the report with the results, even the output of the software could become the report or

关键词： automatic interpretation Automatic reporting explainability

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From Clustering to Intelligent Decision Support System: An Application to 3D Printing 26

From Clustering to Intelligent Decision Support System: An A...

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26th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2024

作者： Karna, Ashutosh Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Centre Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9781643685434

This study focuses on developing an intelligent decision support system (IDSS) that helps a human operator make data-driven decisions. To put IDSS in production, it is necessary to develop two additional components: one oriented to recognize the cluster of new data and the other a knowledge-based resulting from the interpretation of clusters and further association of actions to each cluster, constituting a knowledge base with the alerts and recommendations associated to every profile. Bootstrap-CURE technique is used to handle the initial component, whereas a meta-clustering framework is suggested for interpreting the clusters and providing recommendations. A detailed strategy is presented for handling a print job, examining patterns, and executing actions through IDSS, thus improving predictive accuracy and operational efficiency. Two distinct machine learning models were developed, one to detect the operational mode and another to choose the best meta-cluster for the type of printing jobs and detained steps are provided for implementing the recommendations. © 2024 The Authors.

关键词： Printing presses

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Correction to: Code is law: how COMPAS affects the way the judiciary handles the risk of recidivism (Artificial Intelligence and Law, (2024), 10.1007/s10506-024-09389-8)

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Artificial Intelligence and Law 2024年 1-2页

作者： Engel, Christopher Linhardt, Lorenz Schubert, Marcel Max-Planck-Institute for Research on Collective Goods Bonn53113 Germany Machine Learning Group Technische Universität Berlin Berlin10623 Germany Berlin Institute for the Foundations of Learning and Data – BIFOLD Berlin10586 Germany Max-Planck-Institute for Research on Collective Goods Bonn Germany

In the original version of the article the funding information and author affiliation was incorrect for Lorenz Linhardt. The corrected information is shown below. Author affiliation: machine learning group, Technische Universit?t Berlin, Berlin, 10623, Germany Berlin Institute for the Foundations of learning and data – BIFOLD, Berlin, 10586, Germany Funding information: Lorenz Linhardt gratefully acknowledges funding from German Federal Ministry of Education and research under the grant BIFOLD23B and BIFOLD22B. The original article has been corrected. © The Author(s) 2024.

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Batch Layer Normalization A new normalization layer for CNNs and RNNs 22

Batch Layer Normalization A new normalization layer for CNNs...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Amir Ziaee Erion ÇAno Design Computing Group TU Wien Austria Research Group Data Mining and Machine Learning University of Vienna Austria

ISBN: (纸本)9781450396943

This study introduces a new normalization layer termed Batch Layer Normalization (BLN) to reduce the problem of internal covariate shift in deep neural network layers. As a combined version of batch and layer normalization, BLN adaptively puts appropriate weight on mini-batch and feature normalization based on the inverse size of mini-batches to normalize the input to a layer during the learning process. It also performs the exact computation with a minor change at inference times, using either mini-batch statistics or population statistics. The decision process to either use statistics of mini-batch or population gives BLN the ability to play a comprehensive role in the hyper-parameter optimization process of models. The key advantage of BLN is the support of the theoretical analysis of being independent of the input data, and its statistical configuration heavily depends on the task performed, the amount of training data, and the size of batches. Test results indicate the application potential of BLN and its faster convergence than batch normalization and layer normalization in both Convolutional and Recurrent Neural Networks. The code of the experiments is publicly available online.1

关键词： Neural Networks Internal Covariate Shift Normalization

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

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

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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

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Motif Discovery Framework for Psychiatric EEG data Classification

arXiv

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arXiv 2025年

作者： Kraljevska, Melanija Hlavackova-Schindler, Katerina Miklautz, Lukas Plant, Claudia Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Währingerstrasse 29 Vienna1090 Austria ds:UniVie University of Vienna Austria

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants. Identification of a treatment response at any earlier stage is of great importance, since it can reduce the emotional and economic burden connected with the treatment. We approach the prediction of a patient response to a treatment as a classification problem, by utilizing the dynamic properties of EEG recordings on the 7th day of the treatment. We present a novel framework that applies motif discovery to extract meaningful features from EEG data distinguishing between depression treatment responders and non-responders. We applied our framework also to classification tasks in other psychiatric EEG datasets, namely to patients with symptoms of schizophrenia, pediatric patients with intractable seizures, and Alzheimer disease and dementia. We achieved high classification precision in all data sets. The results demonstrate that the dynamic properties of the EEGs may support clinicians in decision making both in diagnosis and in the prediction depression treatment response as early as on the 7th day of the treatment. To our best knowledge, our work is the first one using motifs in the depression diagnostics in general. © 2025, CC BY.

关键词： Electroencephalography

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Topic Segmentation of research Article Collections

arXiv

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arXiv 2022年

作者： Çano, Erion Roth, Benjamin Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria

Collections of research article data harvested from the web have become common recently since they are important resources for experimenting on tasks such as named entity recognition, text summarization, or keyword generation. In fact, certain types of experiments require collections that are both large and topically structured, with records assigned to separate research disciplines. Unfortunately, the current collections of publicly available research articles are either small or heterogeneous and unstructured. In this work, we perform topic segmentation of a paper data collection that we crawled and produce a multitopic dataset of roughly seven million paper data records. We construct a taxonomy of topics extracted from the data records and then annotate each document with its corresponding topic from that taxonomy. As a result, it is possible to use this newly proposed dataset in two modalities: as a heterogeneous collection of documents from various disciplines or as a set of homogeneous collections, each from a single research topic. Copyright © 2022, The Authors. All rights reserved.

关键词： Taxonomies

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Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?

arXiv

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arXiv 2022年

作者： Thoma, Lukas Roth, Benjamin Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria

In recent years, deep neural language models have made strong progress in various NLP tasks. This work explores one facet of the question whether state-of-the-art NLP models exhibit elementary mechanisms known from human cognition. The exploration is focused on a relatively primitive mechanism for which there is a lot of evidence from various psycholinguistic experiments with infants. The computation of "abstract sameness relations" is assumed to play an important role in human language acquisition and processing, especially in learning more complex grammar rules. In order to investigate this mechanism in BERT and other pre-trained language models (PLMs), the experiment designs from studies with infants were taken as the starting point. On this basis, we designed experimental settings in which each element from the original studies was mapped to a component of language models. Even though the task in our experiments was relatively simple, the results suggest that the cognitive faculty of computing abstract sameness relations is stronger in infants than in all investigated PLMs. Our implementation is available on https://***/lmthoma/computation_ASRs. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

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SepLL: Separating Latent Class Labels from Weak Supervision Noise

SepLL: Separating Latent Class Labels from Weak Supervision ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Stephan, Andreas Kougia, Vasiliki Roth, Benjamin Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria UniVie Doctoral School Computer Science Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Vienna Austria

In the weakly supervised learning paradigm, labeling functions automatically assign heuristic, often noisy, labels to data samples. In this work, we provide a method for learning from weak labels by separating two types of complementary information associated with the labeling functions: information related to the target label and information specific to one labeling function only. Both types of information are reflected to different degrees by all labeled instances. In contrast to previous works that aimed at correcting or removing wrongly labeled instances, we learn a branched deep model that uses all data as-is, but splits the labeling function information in the latent space. Specifically, we propose the end-to-end model SepLL which extends a transformer classifier by introducing a latent space for labeling function specific and task-specific information. The learning signal is only given by the labeling functions matches, no pre-processing or label model is required for our method. Notably, the task prediction is made from the latent layer without any direct task signal. Experiments on Wrench text classification tasks show that our model is competitive with the state-of-the-art, and yields a new best average performance. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

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hmBERT: Historical Multilingual Language Models for Named Entity Recognition

hmBERT: Historical Multilingual Language Models for Named En...

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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Schweter, Stefan März, Luisa Schmid, Katharina Çano, Erion Bayerische Staatsbibliothek München Digital Library/ Munich Digitization Center Munich Germany Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria Natural Language Processing Expert Center Data:Lab Volkswagen AG Munich Germany

Compared to standard Named Entity Recognition (NER), identifying persons, locations, and organizations in historical texts constitutes a big challenge. To obtain machine-readable corpora, the historical text is usually scanned and Optical Character Recognition (OCR) needs to be performed. As a result, the historical corpora contain errors. Also, entities like location or organization can change over time, which poses another challenge. Overall, historical texts come with several peculiarities that differ greatly from modern texts and large labeled corpora for training a neural tagger are hardly available for this domain. In this work, we tackle NER for historical German, English, French, Swedish, and Finnish by training large historical language models. We circumvent the need for large amounts of labeled data by using unlabeled data for pretraining a language model. We propose hmBert, a historical multilingual BERT-based language model, and release the model in several versions of different sizes. Furthermore, we evaluate the capability of hmBert by solving downstream NER as part of this year’s HIPE-2022 shared task and provide detailed analysis and insights. For the Multilingual Classical Commentary coarse-grained NER challenge, our tagger HISTeria outperforms the other teams’ models for two out of three languages. © 2022 Copyright for this paper by its authors.

关键词： Optical character recognition

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