RECLAIM: Reverse Engineering Classification Metrics

Being able to compare machine learning models in terms of performance is a fundamental part of improving the state of the art in a field. However, there is a risk of getting locked into only using a few -- possibly not ideal -- performance metrics, only for comparability with earlier works. In this work, we explore the possibility of reconstructing new classification metrics starting from what little information may be available in existing works. We propose three approaches to reconstruct confusion matrices and, as a consequence, other classification metrics. We empirically verify the quality of the reconstructions, drawing conclusions on the usefulness that various classification metrics have for the reconstruction task

Time-of-Flight Cameras in Space: Pose Estimation with Deep Learning Methodologies

Recently introduced 3D Time-of-Flight (ToF) cameras have shown a huge potential for mobile robotic applications, proposing a smart and fast technology that outputs 3D point clouds, lacking however in measurement precision and robustness. With the development of this low-cost sensing hardware, 3D perception gathers more and more importance in robotics as well as in many other fields, and object registration continues to gain momentum. Registration is a transformation estimation problem between a source and a target point clouds, seeking to find the transformation that best aligns them. This work aims at building a full pipeline, from data acquisition to transformation identification, to robustly detect known objects observed by a ToF camera within a short range, estimating their 6 degrees of freedom position. We focus this work to demonstrating the capability of detecting a part of a satellite floating in space, to support in-orbit servicing missions (e.g. for space debris removal). Experiments reveal that deep learning techniques can obtain higher accuracy and robustness w.r.t. classical methods, handling significant amount of noise while still keeping real-time performance and low complexity of the models themselves

Cross-Lingual Propagation of Sentiment Information Based on Bilingual Vector Space Alignment

Deep learning methods have shown to be particularly effective in inferring the sentiment polarity of a text snippet. However, in cross-domain and cross-lingual scenarios there is often a lack of training data. To tackle this issue, propagation algorithms can be used to yield sentiment information for various languages and domains by transferring knowledge from a source language(usually English). To propagate polarity scores to the target language, these algorithms take as input an initial vocabulary and a bilingual lexicon. In this paper we propose to enrich lexicon in-formation for cross-lingual propagation by inferring the bilingual semantic relationships from an aligned bilingual vector space.This allows us to exploit the underlying text similarities that are not made explicit by the lexicon. The experiments show that our approach outperforms the state-of-the-art propagation method on multilingual datasets

Semantic Image Collection Summarization with Frequent Subgraph Mining

Applications such as providing a preview of personal albums (e.g., Google Photos) or suggesting thematic collections based on user interests (e.g., Pinterest) require a semantically-enriched image representation, which should be more informative with respect to simple low-level visual features and image tags. To this aim, we propose an image collection summarization technique based on frequent subgraph mining. We represent images with a novel type of scene graphs including fine-grained relationship types between objects. These scene graphs are automatically derived by our method. The resulting summary consists of a set of frequent subgraphs describing the underlying patterns of the image dataset. Our results are interpretable and provide more powerful semantic information with respect to previous techniques, in which the summary is a subset of the collection in terms of images or image patches. The experimental evaluation shows that the proposed technique yields non-redundant summaries, with a high diversity of the discovered patterns

Dissecting a Data-driven Prognostic Pipeline: A Powertrain use case

Nowadays, cars are instrumented with thousands of sensors continuously collecting data about its components. Thanks to the concept of connected cars, this data can be now transferred to the cloud for advanced analytics functionalities, such as prognostic or predictive maintenance. In this paper, we dissect a data-driven prognostic pipeline and apply it in the automotive scenario. Our pipeline is composed of three main steps: (i) selection of most important signals and features describing the scenario for the target problem, (ii) creation of machine learning models based on different classification algorithms, and (iii) selection of the model that works better for a deployment scenario. For the development of the pipeline, we exploit an extensive experimental campaign where an actual engine runs in a controlled test bench under different working conditions. We aim to predict failures of the High-Pressure Fuel System, a key part of the diesel engine responsible for delivering high-pressure fuel to the cylinders for combustion. Our results show the advantage of data-driven solutions to automatically discover the most important signals to predict failures of the High-Pressure Fuel System. We also highlight how an accurate model selection step is fundamental to identify a robust model suitable for deployment

Efficacy of team work in health promotion and secondary prevention in patients admitted for cardiovascular rehabilitation

The object of the study was to evaluate the immediate efficacy of periodical educational meetings organized in Cardiovascular Rehabilitation Department aimed to improve knowledge about cardiovascular pathology, risk factors and correct life style. Methods: from October 2008 a multiprofessional group organized educational meetings for patients and their relatives, using two questionnaires to explore patients’ level of knowledge, before and after the meeting. Results: 124 patients (90 males) answered the questionnaire 1, while questionnaire 2 was completed by 93 subjects (70 males). From the answers to questionnaire 1, a significant improvement of knowledge about coronary anatomy and cardiovascular therapy emerged. Indeed, 99% of patients vs 81% before the meeting (p=0.001) understood the coronary artery function, 69% vs 44% (p=0,0001) of participants was familiar with coronary angioplasty, 81% vs. 64% (p=0,003) demonstrated to understand the coronary artery bypass and finally 85% vs. 52% (p=0,0001) were able to distinguish mechanical from biological prosthesis. From answers to questionnaire 2, a trend in favour to an improvement of knowledge regarding coronary risk factors and correct life style emerged. Younger patients (<70 ys) had a higher baseline level knowledge (p=0,003 and p=0.001 group 1 and 2, respectively) compared to older subjects, but in the latter a trend in favour of enhanced knowledge (p=0.06) after the educational meetings emerged. Conclusions: educational meetings are significantly correlated with an improvement of patients’ knowledge regarding cardiovascular pathology and treatments independently from patients’ age

Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning

Exploring exoplanets has transformed our understanding of the universe by revealing many planetary systems that defy our current understanding. To study their atmospheres, spectroscopic observations are used to infer essential atmospheric properties that are not directly measurable. Estimating atmospheric parameters that best fit the observed spectrum within a specified atmospheric model is a complex problem that is difficult to model. In this paper, we present a multi-target probabilistic regression approach that combines deep learning and inverse modeling techniques within a multimodal architecture to extract atmospheric parameters from exoplanets. Our methodology overcomes computational limitations and outperforms previous approaches, enabling efficient analysis of exoplanetary atmospheres. This research contributes to advancements in the field of exoplanet research and offers valuable insights for future studies

Fast Self-Organizing Maps Training

Self-organizing maps are an unsupervised machine learning technique that offers interpretable results by identifying topological properties in high-dimensional datasets and projecting them on a 2-dimensional grid. An important problem of self-organizing maps is the computational expensiveness of their training phase. In this paper, we propose a fast approach to train self-organizing maps. The approach consists of 2 steps. First, a small map identifies the most relevant areas from the entire high-dimensional input space. Then a larger map (initialized from the small one) is fine-tuned to further explore the local areas identified in the first step. The resulting map has performance (measured in terms of accuracy and quantization error) on par with self-organizing maps trained with the standard approach, but with a significantly reduced training time

Method and System for Predicting System Status

A system and method of determining system status in a vehicle system. The method including collecting, by a computing system, a plurality of data associated with a test specimen and the vehicle system, selecting a relevant data set of the plurality of data, the selecting based on at least one correlation coefficient associated with the plurality of data, and transforming at least a portion of the selected relevant data to form a transformed data set, the transforming based on mathematical properties. The method also includes collecting statistics associated with the selected relevant data set and the transformed data set to form a statistics data set, classifying the selected relevant data set, transformed data set, and the statistics data set; and predicting a status of a system based on the classifying

baρtti at GeoLingIt: Beyond Boundaries, Enhancing Geolocation Prediction and Dialect Classification on Social Media in Italy

The proliferation of social media platforms has presented researchers with valuable avenues to examine language usage within diverse sociolinguistic frameworks. Italy, renowned for its rich linguistic diversity, provides a distinctive context for exploring diatopic variation, encompassing regional languages, dialects, and variations of Standard Italian. This paper presents our contributions to the GeoLingIt shared task, focusing on predicting the locations of social media posts in Italy based on linguistic content. For Task A, we propose a novel approach, combining data augmentation and contrastive learning, that outperforms the baseline in region prediction. For Task B, we introduce a joint multi-task learning approach leveraging the synergies with Task A and incorporate a post-processing rectification module for improved geolocation accuracy, surpassing the baseline and achieving first place in the competition