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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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Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research

arXiv

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arXiv 2024年

作者： Hille, Tobias Stubbemann, Maximilian Hanika, Tom Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Kassel Germany Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany Institute of Computer Science University of Hildesheim Hildesheim Germany

Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning research results are sound and reliable requires reproducibility, which verifies the reliability of research findings using the same code and data. This promotes open and accessible research, robust experimental workflows, and the rapid integration of new findings. Evaluating the degree to which research publications support these different aspects of reproducibility is one goal of the present work. For this we introduce an ontology of reproducibility in machine learning and apply it to methods for graph neural networks. Building on these efforts we turn towards another critical challenge in machine learning, namely the curse of dimensionality, which poses challenges in data collection, representation, and analysis, making it harder to find representative data and impeding the training and inference processes. Using the closely linked concept of geometric intrinsic dimension we investigate to which extend the used machine learning models are influenced by the intrinsic dimension of the data sets they are trained *** Codes 68T01 68T07 68T09 51F99 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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The use of Synthetic Data to solve the scalability and data availability problems in Smart City Digital Twins

arXiv

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arXiv 2022年

作者： Almirall, Esteve Callegaro, Davide Bruins, Peter Santamaría, Mar Martrínez, Pablo Cortés, Ulises Esade Business School URL University Spain 300.000km *** Spain Knowledge Engineering and Machine Learning Group Universitat Politècnica de Catalunya Spain

The A.I. disruption and the need to compete on innovation are impacting cities that have an increasing necessity to become innovation hotspots. However, without proven solutions, experimentation, often unsuccessful, is needed. But experimentation in cities has many undesirable effects not only for its citizens but also reputational if unsuccessful. Digital Twins, so popular in other areas, seem like a promising way to expand experimentation proposals but in simulated environments, translating only the "half-baked" ones, the ones with higher probability of success, to real environments and therefore minimizing risks. However, Digital Twins are data intensive and need highly localized data, making them difficult to scale, particularly to small cities, and with the high cost associated to data collection. We present an alternative based on synthetic data that given some conditions, quite common in Smart Cities, can solve these two problems together with a proof-of-concept based on NO2 pollution. © 2022, CC BY.

关键词： Smart city

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learning Fair Representations through Uniformly Distributed Sensitive Attributes

Learning Fair Representations through Uniformly Distributed ...

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Secure and Trustworthy machine learning (SaTML), IEEE Conference on

作者： Patrik Joslin Kenfack Adín Ramírez Rivera Adil Mehmood Khan Manuel Mazzara Machine Learning and Knowledge Representation Lab Innopolis University Innopolis Russia Department of Informatics Digital Signal Processing and Image Analysis (DSB) group University of Oslo Oslo Norway School of Computer Science University of Hull Hull UK Institute of Software Development and Engineering Innopolis University Innopolis Russia

machine learning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical. One approach to mitigate possible unfairness of ML models is to map the input data into a less-biased new space by means of training the model on fair representations. Several methods based on adversarial learning have been proposed to learn fair representation by fooling an adversary in predicting the sensitive attribute (e.g., gender or race). However, adversarial-based learning can be too difficult to optimize in practice; besides, it penalizes the utility of the representation. Hence, in this research effort we train bias-free representations from the input data by inducing a uniform distribution over the sensitive attributes in the latent space. In particular, we propose a probabilistic framework that learns these representations by enforcing the correct reconstruction of the original data, plus the prediction of the attributes of interest while eliminating the possibility of predicting the sensitive ones. Our method leverages the inability of Deep Neural Networks (DNNs) to generalize when trained on a noisy label space to regularize the latent space. We use a network head that predicts a noisy version of the sensitive attributes in order to increase the uncertainty of their predictions at test time. Our experiments in two datasets demonstrated that the proposed model significantly improves fairness while maintaining the prediction accuracy of downstream tasks.

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A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles

arXiv

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arXiv 2023年

作者： Masmitja, Ivan Martin, Mario Katija, Kakani Gomariz, Spartacus Navarro, Joan The Bioinspiration Lab MBARI Moss Landing CA95062 United States The Institut de Ciències del Mar CSIC Barcelona08003 Spain The Knowledge Engineering and Machine Learning Group Universitat Politècnica de Catalunya Barcelona Tech. Barcelona08034 Spain The SARTI Research Group Electronics Department Universitat Politècnica de Catalunya Barcelona Tech. Barcelona080934 Spain

Underwater target localization using range-only and single-beacon (ROSB) techniques with autonomous vehicles has been used recently to improve the limitations of more complex methods, such as long baseline and ultra-short baseline systems. Nonetheless, in ROSB target localization methods, the trajectory of the tracking vehicle near the localized target plays an important role in obtaining the best accuracy of the predicted target position. Here, we investigate a Reinforcement learning (RL) approach to find the optimal path that an autonomous vehicle should follow in order to increase and optimize the overall accuracy of the predicted target localization, while reducing time and power consumption. To accomplish this objective, different experimental tests have been designed using state-of-the-art deep RL algorithms. Our study also compares the results obtained with the analytical Fisher information matrix approach used in previous studies. The results revealed that the policy learned by the RL agent outperforms trajectories based on these analytical solutions, e.g. the median predicted error at the beginning of the target’s localisation is 17% less. These findings suggest that using deep RL for localizing acoustic targets could be successfully applied to in-water applications that include tracking of acoustically tagged marine animals by autonomous underwater vehicles. This is envisioned as a first necessary step to validate the use of RL to tackle such problems, which could be used later on in a more complex scenarios. © 2023, CC BY-NC-ND.

关键词： Autonomous vehicles

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Robust and Fast Measure of Information via Low-rank Representation

arXiv

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arXiv 2022年

作者： Dong, Yuxin Gong, Tieliang Yu, Shujian Chen, Hong Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China Machine Learning Group UiT - The Arctic University of Norway Norway College of Science Huazhong Agriculture University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China

The matrix-based Rényi’s entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it widely applied in statistical inference and machine learning tasks. However, this information theoretical quantity is not robust against noise in the data, and is computationally prohibitive in large-scale applications. To address these issues, we propose a novel measure of information, termed low-rank matrix-based Rényi’s entropy, based on low-rank representations of infinitely divisible kernel matrices. The proposed entropy functional inherits the specialty of of the original definition to directly quantify information from data, but enjoys additional advantages including robustness and effective calculation. Specifically, our low-rank variant is more sensitive to informative perturbations induced by changes in underlying distributions, while being insensitive to uninformative ones caused by noises. Moreover, low-rank Rényi’s entropy can be efficiently approximated by random projection and Lanczos iteration techniques, reducing the overall complexity from O(n3) to O(n2s) or even O(ns2), where n is the number of data samples and s n. We conduct large-scale experiments to evaluate the effectiveness of this new information measure, demonstrating superior results compared to matrix-based Rényi’s entropy in terms of both performance and computational efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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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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SPFLOW: An easy and extensible library for deep probabilistic learning using sum-product networks

arXiv

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arXiv 2019年

作者： Molina, Alejandro Vergari, Antonio Stelzner, Karl Peharz, Robert Subramani, Pranav Di Mauro, Nicola Poupart, Pascal Kersting, Kristian Machine Learning Group Computer Science Department TU Darmstadt Germany Probabilistic Learning Group Empirical Inference Department Max-Planck-Institute Germany Machine Learning Group Engineering Dept. University of Cambridge United Kingdom Knowledge Acquisition & Machine Learning Group University of Bari "Aldo Moro" Italy Waterloo AI Institute Vector Institute University of Waterloo CA Canada Centre for Cognitive Science TU Darmstadt Germany

We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs). The library allows one to quickly create SPNs both from data and through a domain specific language (DSL). It efficiently implements several probabilistic inference routines like computing marginals, conditionals and (approximate) most probable explanations (MPEs) along with sampling as well as utilities for serializing, plotting and structure statistics on an SPN. Moreover, many of the algorithms proposed in the literature to learn the structure and parameters of SPNs are readily available in SPFlow. Furthermore, SPFlow is extremely extensible and customizable, allowing users to promptly distill new inference and learning routines by injecting custom code into a lightweight functional-oriented API framework. This is achieved in SPFlow by keeping an internal Python representation of the graph structure that also enables practical compilation of an SPN into a TensorFlow graph, C, CUDA or FPGA custom code, significantly speeding-up computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Python

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A Web-Based Platform for People with Memory Problems and Their Caregivers (CAREGIVERSPRO-MMD):Mixed-Methods Evaluation of Usability

引用

JMIR Formative Research 2018年第1期2卷 e4-e4页

作者： Zafeiridi, Paraskevi Paulson, Kevin Dunn, Rosie Wolverson, Emma White, Caroline Thorpe, Jonathan Adrian Antomarini, Marco Cesaroni, Francesca Scocchera, Francesca Landrin-Dutot, Isabelle Malherbe, Laetitia Lingiah, Hendi Berard, Marie Girones, Xavier Quintana, Maria Cortes, Ulises Barrue, Cristian Cortes, Atia Paliokas, Ioannis Votis, Konstantinos Tzovaras, Dimitrios School of Engineering and Computer Science University of Hull School of Health and Social Work Aire Building Cottingham Road Hull HU67RX United Kingdom Cooperativa Sociale Onlus Marche Onlus Ancona Italy Department of Internal Medicine Geriatrics and Therapeutics Rouen University Hospital Department of Geriatrics Rouen Cedex France Faculty of Health Sciences University of Vic Central University of Catalonia Manresa Spain Knowledge Engineering & Machine Learning Group Universitat Polit cnica de Catalunya Barcelona Spain Information Technologies Institute Centre for Research and Technology Hellas Thessaloniki Greece

Background: The increasing number of people with dementia (PwD) drives research exploring Web-based support interventions to provide effective care for larger populations. In this concept, a Web-based platform (CAREGIVERSPRO-MMD, 620911) was designed to (1) improve the quality of life for PwD, (2) reduce caregiver burden, (3) reduce the financial costs for care, and (4) reduce administration time for health and social care professionals. Objective: The objective of this study was to evaluate the usability and usefulness of CAREGIVERSPRO-MMD platform for PwD or mild cognitive impairment (MCI), informal caregivers, and health and social care professionals with respect to a wider strategy followed by the project to enhance the user-centered approach. A secondary aim of the study was to collect recommendations to improve the platform before the future pilot study. Methods: A mixed methods design was employed for recruiting PwD or MCI (N=24), informal caregivers (N=24), and professionals (N=10). Participants were asked to rate their satisfaction, the perceived usefulness, and ease of use of each function of the platform. Qualitative questions about the improvement of the platform were asked when participants provided low scores for a function. Testing occurred at baseline and 1 week after participants used the platform. The dropout rate from baseline to the follow-up was approximately 10% (6/58). Results: After 1 week of platform use, the system was useful for 90% (20.75/23) of the caregivers and for 89% (5.36/6) of the professionals. When users responded tomore than 1 question per platform function, the mean of satisfied users per function was calculated. These user groups also provided positive evaluations for the ease of use (caregivers: 82%, 18.75/23;professionals: 97%, 5.82/6) and their satisfaction with the platform (caregivers: 79%, 18.08/23;professionals: 73%, 4.36/6). Ratings from PwD were lower than the other groups for usefulness (57%, 13/23), ease of use (41%

关键词： Caregivers Dementia Social support Technology

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