Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features.

A regression model with more parameters than data points in the training data is overparametrized and has the capability to interpolate the training data. Based on the classical bias-variance tradeoff expressions, it is commonly assumed that models which interpolate noisy training data are poor to generalize. In some cases, this is not true. The best models obtained are overparametrized and the testing error exhibits the double descent behavior as the model order increases. In this contribution, we provide some analysis to explain the double descent phenomenon, first reported in the machine learning literature. We focus on interpolating models derived from the minimum norm solution to the classical least-squares problem and also briefly discuss model fitting using ridge regression. We derive a result based on the behavior of the smallest singular value of the regression matrix that explains the peak location and the double descent shape of the testing error as a function of model order.

A common approach of system identification and machine learning is to generate a model by using training data to predict the test data instances as accurate as possible. Nonetheless, concerns about data privacy are increasingly raised, but not always addressed. We present a secure protocol for learning a linear model relying on recently described technique called real number secret sharing. We take as our starting point the PAC Bayesian bounds and deduce a closed form for the model parameters which depends on the data and the prior from the PAC Bayesian bounds. To obtain the model parameters one needs to solve a linear system. However, we consider the situation where several parties hold different data instances and they are not willing to give up the privacy of the data. Hence, we suggest to use real number secret sharing and multiparty computation to share the data and solve the linear regression in a secure way without violating the privacy of data. We suggest two methods; a secure inverse method and a secure Gaussian elimination method, and compare these methods at the end. The benefit of using secret sharing directly on real numbers is reflected in the simplicity of the protocols and the number of rounds needed. However, this comes with the drawback that a share might leak a small amount of information, but in our analysis we argue that the leakage is small.

Using the terminology defined by current legislation, the article outlines a systematic approach to handling hospital data anonymously in preparation for its use in Artificial Intelligence (AI) applications in healthcare. The development process consisted of 7 pragmatic steps, including defining scope, selecting knowledge, reviewing important terms, constructing classes that describe designs used in epidemiological studies, machine learning paradigms, types of data and attributes, risks that anonymized data may be exposed to, privacy attacks, techniques to mitigate re-identification, privacy models, and metrics for measuring the effects of anonymization. The article concludes by demonstrating the practical implementation of this ontology in hospital settings for the development and validation of AI.

Regime switching is ubiquitous in many complex dynamical systems with multiscale features, chaotic behavior, and extreme events. In this paper, a causation entropy boosting (CEBoosting) strategy is developed to facilitate the detection of regime switching and the discovery of the dynamics associated with the new regime via online model identification. The causation entropy, which can be efficiently calculated, provides a logic value of each candidate function in a pre-determined library. The reversal of one or a few such causation entropy indicators associated with the model calibrated for the current regime implies the detection of regime switching. Despite the short length of each batch formed by the sequential data, the accumulated value of causation entropy corresponding to a sequence of data batches leads to a robust indicator. With the detected rectification of the model structure, the subsequent parameter estimation becomes a quadratic optimization problem, which is solved using closed analytic formulae. Using the Lorenz 96 model, it is shown that the causation entropy indicator can be efficiently calculated, and the method applies to moderately large dimensional systems. The CEBoosting algorithm is also adaptive to the situation with partial observations. It is shown via a stochastic parameterized model that the CEBoosting strategy can be combined with data assimilation to identify regime switching triggered by the unobserved latent processes. In addition, the CEBoosting method is applied to a nonlinear paradigm model for topographic mean flow interaction, demonstrating the online detection of regime switching in the presence of strong intermittency and extreme events.

Solar energy has been developed as a better alternative to fossil fuels in the past few years. It is a renewable and infinite source of energy which does not have a bad impact on the environment. It is also cheap and easily accessible, making it a better alternative for both personal and commercial purposes. Solar Arrays are made when PV modules used in solar panels are connected together. Energy is produced when sunlight falls on Solar Panels which can be used instead of Fossil fuel's produced energy. For execution of a PV system under different situations, estimating the parameters in a PV model plays an important role because it enables us to optimise the design and performance of the system which leads to increased energy production and improved performance. If a PV system is not performing as expected, then identification of parameters of the PV model helps identify the root cause of the problem. This could be due to factors such as shading, module mismatch, or degradation over time. By accurately estimating the parameters, we can determine the best method to improve its performance.

Explanatory systems make machine learning models more transparent. However, they are often inconsistent. In order to quantify and isolate possible scenarios leading to this discrepancy, this paper compares two explanatory systems, SHAP and LIME, based on the correlation of their respective importance scores using 14 machine learning models (7 regression and 7 classification) and 4 tabular datasets (2 regression and 2 classification). We make two novel findings. Firstly, the magnitude of importance is not significant in explanation consistency. The correlations between SHAP and LIME importance scores for the most important features may or may not be more variable than the correlation between SHAP and LIME importance scores averaged across all features. Secondly, the similarity between SHAP and LIME importance scores cannot predict model accuracy. In the process of our research, we construct an open-source library, XAISuite, that unifies the process of training and explaining models. Finally, this paper contributes a generalized framework to better explain machine learning models and optimize their performance.

However, when the predictor variables are highly correlated then multicollinearity can become a problem. This can cause the coefficient estimates of the model to be unreliable and have high variance. One way to avoid this problem is to instead use principal components regression, which finds M linear combinations (known as "principal components") of the original p predictors and then uses least squares to fit a linear regression model using the principal components as predictors. This tutorial provides a step-by-step example of how to perform principal components regression in R. The easiest way to perform principal components regression in R is by using functions from the pls package. For this example, we'll use the built-in R dataset called mtcars which contains data about various types of cars: For this example we'll fit a principal components regression (PCR) model using hp as the response variable and the following variables as the predictor variables: The following code shows how to fit the PCR model to this data.

This paper describes our submission to the SemEval 2023 multilingual tweet intimacy analysis shared task. The goal of the task was to assess the level of intimacy of Twitter posts in ten languages. The proposed approach consists of several steps. First, we perform in-domain pre-training to create a language model adapted to Twitter data. In the next step, we train an ensemble of regression models to expand the training set with pseudo-labeled examples. The extended dataset is used to train the final solution. Our method was ranked first in five out of ten language subtasks, obtaining the highest average score across all languages.

Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations and discusses potential innovations. We searched five databases, SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from three primary perspectives, including data quality, modeling strategies, and FL frameworks. Out of the 1160 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis. The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research.