A New XAI-based Evaluation of Generative Adversarial Networks for IMU Data Augmentation

Data augmentation is a widespread innovative technique in Artificial Intelligence: it aims at creating new synthetic data given an existing real baseline, thus allowing to overcome the issues arising from the lack of labelled data for proper training of classification algorithms. Our paper focuses on how a common data augmentation methodology, the Generative Adversarial Networks (GANs), which is widespread for images and timeseries data, can be also applied to generate multivariate data. We propose a novel scheme for GANs evaluation, based on the performance of an explainable AI (XAI) algorithm and an innovative definition of rule similarity. In particular, we will consider an application dealing with the augmentation of Inertial Movement Units (IMU) data for physical fatigue monitoring in two age subgroups (under and over 40 years old) of the original data. We will show how our innovative rule similarity metric can drive the selection of the best fake dataset among a set of different candidates, corresponding to different GAN training runs

Portfolio optimization and risk management through Hierarchical Risk Parity and Logic Learning Machine: a case study applied to the Turkish stock market

This study explores an innovative approach to portfolio optimization, bridging traditional Modern Portfolio Theory (MPT) with advanced Machine Learning techniques. We start by recognizing the significance of Markowitz's model in MPT and quickly proceed to focus on the Hierarchical Risk Parity (HRP) method. HRP overcomes some of the limitations of Markowitz's model, particularly in managing complex asset correlations, by offering a more refined risk management strategy that ensures balanced risk distribution across the portfolio. The paper then introduces an innovative Machine Learning approach that employs the Logic Learning Machine (LLM) method to enhance the explainability of the Hierarchical Risk Parity strategy. Such integration is considered the core research part of the study, given that its application makes the output of the model more accessible and transparent. A case study based on the Turkish stock market has been provided as an example. The combination of traditional financial theories with modern Machine Learning tools marks a significant advancement in investment management and portfolio optimization, emphasizing the importance of clarity and ease of understanding in complex financial portfolio models

Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group

Identifying and treating lipid abnormalities is crucial for preventing cardiovascular disease in diabetic patients, yet only two-thirds of patients reach recommended cholesterol levels. Elucidating the factors associated with lipid goal attainment represents an unmet clinical need. To address this knowledge gap, we conducted a real-world analysis of the lipid profiles of 11.252 patients from the Annals of the Italian Association of Medical Diabetologists (AMD) database from 2005 to 2019. We used a Logic Learning Machine (LLM) to extract and classify the most relevant variables predicting the achievement of a low-density lipoprotein cholesterol (LDL-C) value lower than 100 mg/dL (2.60 mmol/L) within two years of the start of lipid-lowering therapy. Our analysis showed that 61.4% of the patients achieved the treatment goal. The LLM model demonstrated good predictive performance, with a precision of 0.78, accuracy of 0.69, recall of 0.70, F1 Score of 0.74, and ROC-AUC of 0.79. The most significant predictors of achieving the treatment goal were LDL-C values at the start of lipid-lowering therapy and their reduction after six months. Other predictors of a greater likelihood of reaching the target included high-density lipoprotein cholesterol, albuminuria, and body mass index at baseline, as well as younger age, male sex, more follow-up visits, no therapy discontinuation, higher Q-score, lower blood glucose and HbA1c levels, and the use of anti-hypertensive medication. At baseline, for each LDL-C range analysed, the LLM model also provided the minimum reduction that needs to be achieved by the next six-month visit to increase the likelihood of reaching the therapeutic goal within two years. These findings could serve as a useful tool to inform therapeutic decisions and to encourage further in-depth analysis and testing

Structure-based object representation and classification in mobile robotics through a Microsoft Kinect

A new approach enabling a mobile robot to recognize and classify furniture-like objects composed of assembled parts using a Microsoft Kinect is presented. Starting from considerations about the structure of furniture-like objects, i.e., objects which can play a role in the course of a mobile robot mission, the 3D point cloud returned by the Kinect is first segmented into a set of "almost convex" clusters. Objects are then represented by means of a graph expressing mutual relationships between such clusters. Off-line, snapshots of the same object taken from different positions are processed and merged, in order to produce multiple-view models that are used to populate a database. On-line, as soon as a new object is observed, a run-time window of subsequent snapshots is used to search for a correspondence in the database. Experiments validating the approach with a set of objects (i.e., chairs, tables, but also other robots) are reported and discussed in detail

Micro air vehicle localization and position tracking from textured 3D cadastral models

In this paper, we address the problem of localizing a camera-equipped Micro Aerial Vehicle (MAV) flying in urban streets at low altitudes. An appearance-based global positioning system to localize MAVs with respect to the surrounding buildings is introduced. We rely on an air-ground image matching algorithm to search the airborne image of the MAV within a ground-level Street View image database and to detect image matching points. Based on the image matching points, we infer the global position of the MAV by back-projecting the corresponding image points onto a cadastral 3D city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry, whenever a good match is detected between the airborne MAV and the street-level images. The proposed approach is tested on a dataset captured with a small quadroctopter flying in the streets of Zurich

The clinical meaning of the area under a receiver operating characteristic curve for the evaluation of the performance of disease markers

OBJECTIVES The area under a receiver operating characteristic (ROC) curve (AUC) is a popular measure of pure diagnostic accuracy that is independent from the proportion of diseased subjects in the analysed sample. However, its actual usefulness in the clinical context has been questioned, because it does not seem to be directly related to the actual performance of a diagnostic marker in identifying diseased and non-diseased subjects in real clinical settings. This study evaluates the relationship between the AUC and the proportion of correct classifications (global diagnostic accuracy, GDA) in relation to the shape of the corresponding ROC curves. METHODS We demonstrate that AUC represents an upward-biased measure of GDA at an optimal accuracy cut-off for balanced groups. The magnitude of bias depends on the shape of the ROC plot and on the proportion of diseased and non-diseased subjects. In proper curves, the bias is independent from the diseased/non-diseased ratio and can be easily estimated and removed. Moreover, a comparison between 2 partial AUCs can be replaced by a more powerful test for the corresponding whole AUCs. RESULTS Applications to 3 real datasets are provided: a marker for a hormone deficit in children, 2 tumour markers for malignant mesothelioma, and 2 gene expression profiles in ovarian cancer patients. CONCLUSIONS The AUC is a measure of accuracy with potential clinical relevance for the evaluation of disease markers. The clinical meaning of ROC parameters should always be evaluated with an analysis of the shape of the corresponding ROC curve

Optimizing Water Distribution through Explainable AI and Rule-Based Control

Optimizing water distribution both from an energy-saving perspective and from a quality of service perspective is a challenging task since it involves a complex system with many nodes, many hidden variables and many operational constraints. For this reason, water distribution systems need to handle a delicate trade-off between the effectiveness and computational time of the solution. In this paper, we propose a new computationally efficient method, named rule-based control, to optimize water distribution networks without the need for a rigorous formulation of the optimization problem. As a matter of fact, since it is based on a machine learning approach, the proposed method employs only a set of historical data, where the configuration can be labeled according to a quality criterion. Since it is a data-driven approach, it could be applied to any complex network where historical labeled data are available. In particular, rule-based control exploits a rule-based classification method that allows us to retrieve the rules leading to good or bad performances of the system, even without any information about its physical laws. The evaluation of the results on some simulated scenarios shows that the proposed approach is able to reduce energy consumption while ensuring a good quality of the service. The proposed approach is currently used in the water distribution system of the Milan (Italy) water main

Air-ground Matching: Appearance-based GPS-denied Urban Localization of Micro Aerial Vehicles

In this paper, we address the problem of globally localizing and tracking the pose of a camera-equipped micro aerial vehicle (MAV) flying in urban streets at low altitudes without GPS. An image-based global positioning system is introduced to localize the MAV with respect to the surrounding buildings. We propose a novel air-ground image-matching algorithm to search the airborne image of the MAV within a ground-level, geotagged image database. Based on the detected matching image features, we infer the global position of the MAV by back-projecting the corresponding image points onto a cadastral three-dimensional city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry whenever a good match is detected between the airborne and the ground-level images. The proposed approach is tested on a 2 km trajectory with a small quadrocopter flying in the streets of Zurich. Our vision-based global localization can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus outperforming conventional visual place-recognition approaches. The dataset is made publicly available to the research community. To the best of our knowledge, this is the first work that studies and demonstrates global localization and position tracking of a drone in urban streets with a single onboard camera

Using Stratified Sampling to Improve LIME Image Explanations

We investigate the use of a stratified sampling approach for LIME Image, a popular model-agnostic explainable AI method for computer vision tasks, in order to reduce the artifacts generated by typical Monte Carlo sampling. Such artifacts are due to the undersampling of the dependent variable in the synthetic neighborhood around the image being explained, which may result in inadequate explanations due to the impossibility of fitting a linear regressor on the sampled data. We then highlight a connection with the Shapley theory, where similar arguments about undersampling and sample relevance were suggested in the past. We derive all the formulas and adjustment factors required for an unbiased stratified sampling estimator. Experiments show the efficacy of the proposed approach

SRM and Performance Evaluation Models for Logistics Optimization in Consumer Supply Chain

This paper focuses on the evaluation of transport optimization techniques in a consumer-packaged goods supply chain. In particular, it evaluates how the parameters of the optimization, such as the inbound and outbound capacity of nodes, affect the effectiveness of the distribution of goods, measured in terms both of the ability to fulfill the demand in time and of reducing the cost (and environmental impact) of logistics operations. Synthetic data regarding a realistic supply chain network have been generated and the Rulex Platform’s Transport Optimizer has been applied to different scenarios where some parameters of the network have been changed. This allows supply chain experts to create a what-if analysis about how the supply chain would react if something changed in the network. Moreover, from a strategic perspective, the analysis of the behavior of the network in different situations could lead to redesigning the whole supply chain to reduce its impact while keeping the quality of service. An in-depth analysis is performed in this paper on the synthetic data and on a selected set of parameters of the network