Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through  flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a  flexible approach for the construction and selection of highly  flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional  flexibility on the possible types of features to be considered. This  flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modified mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.  

Machine learning models, especially deep models, may unintentionally remember information about their training data. Malicious attackers can thus pilfer some property about training data by attacking the model via membership inference attack or model inversion attack. Some regulations, such as the EU's GDPR, have enacted "The Right to Be Forgotten" to protect users' data privacy, enhancing individuals' sovereignty over their data. Therefore, removing training data information from a trained model has become a critical issue. In this paper, we present a GAN-based algorithm to delete data in deep models, which significantly improves deleting speed compared to retraining from scratch, especially in complicated scenarios. We have experimented on five commonly used datasets, and the experimental results show the efficiency of our method.

Engagement in Human-Machine Interaction is the process by which entities participating in the interaction establish, maintain, and end their perceived connection. It is essential to monitor the engagement state of patients in various AI-based healthcare paradigms. This includes medical conditions that alter social behavior such as Autism Spectrum Disorder (ASD) or Attention-Deficit/Hyperactivity Disorder (ADHD). Engagement is a multifaceted construct which is composed of behavioral, emotional, and mental components. Previous research has neglected the multi-faceted nature of engagement. In this paper, a system is presented to distinguish these facets using contextual and relational features. This can facilitate further fine-grained analysis. Several machine learning classifiers including traditional and deep learning models are compared for this task. A highest accuracy of 74.57% with an F-Score and mean absolute error of 0.74 and 0.23 respectively was obtained on a balanced dataset of 22242 instances with neural network-based classification.

We propose a novel framework to classify large-scale time series data with long duration. Long time seriesclassification (L-TSC) is a challenging problem because the dataoften contains a large amount of irrelevant information to theclassification target. The irrelevant period degrades the classifica-tion performance while the relevance is unknown to the system.This paper proposes an uncertainty-aware multiple instancelearning (MIL) framework to identify the most relevant periodautomatically. The predictive uncertainty enables designing anattention mechanism that forces the MIL model to learn from thepossibly discriminant period. Moreover, the predicted uncertaintyyields a principled estimator to identify whether a prediction istrustworthy or not. We further incorporate another modality toaccommodate unreliable predictions by training a separate modelbased on its availability and conduct uncertainty aware fusion toproduce the final prediction. Systematic evaluation is conductedon the Automatic Identification System (AIS) data, which is col-lected to identify and track real-world vessels. Empirical resultsdemonstrate that the proposed method can effectively detect thetypes of vessels based on the trajectory and the uncertainty-awarefusion with other available data modality (Synthetic-ApertureRadar or SAR imagery is used in our experiments) can furtherimprove the detection accuracy.

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Data is what powers machine learning solutions. Quality datasets enable training models with the needed detection and classification accuracy, though sometimes the accumulation of sufficient and applicable training data that should be fed into the model is a complex challenge. For instance, to create data-intensive apps human annotators are required to label a huge number of samples, which results in complexity of management and high costs for businesses. In addition to that, there is the difficulty associated with data acquisition related to safety regulations, privacy, or ethical concerns. When we have a limited dataset including only a finite number of samples per class, few-shot learning may be useful.

In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and we address the issues with our understanding to build a bright and healthy environment.

The visualization and detection of anomalies (outliers) are of crucial importance to many fields, particularly cybersecurity. Several approaches have been proposed in these fields, yet to the best of our knowledge, none of them has fulfilled both objectives, simultaneously or cooperatively, in one coherent framework. The visualization methods of these approaches were introduced for explaining the output of a detection algorithm, not for data exploration that facilitates a standalone visual detection. This is our point of departure: UN-AVOIDS, an unsupervised and nonparametric approach for both visualization (a human process) and detection (an algorithmic process) of outliers, that assigns invariant anomalous scores (normalized to $[0,1]$), rather than hard binary-decision. The main aspect of novelty of UN-AVOIDS is that it transforms data into a new space, which is introduced in this paper as neighborhood cumulative density function (NCDF), in which both visualization and detection are carried out. In this space, outliers are remarkably visually distinguishable, and therefore the anomaly scores assigned by the detection algorithm achieved a high area under the ROC curve (AUC). We assessed UN-AVOIDS on both simulated and two recently published cybersecurity datasets, and compared it to three of the most successful anomaly detection methods: LOF, IF, and FABOD. In terms of AUC, UN-AVOIDS was almost an overall winner. The article concludes by providing a preview of new theoretical and practical avenues for UN-AVOIDS. Among them is designing a visualization aided anomaly detection (VAAD), a type of software that aids analysts by providing UN-AVOIDS' detection algorithm (running in a back engine), NCDF visualization space (rendered to plots), along with other conventional methods of visualization in the original feature space, all of which are linked in one interactive environment.

How much does a given trained model leak about each individual data record in its training set? Membership inference attacks are used as an auditing tool to quantify the private information that a model leaks about the individual data points in its training set. Membership inference attacks are influenced by different uncertainties that an attacker has to resolve about training data, the training algorithm, and the underlying data distribution. Thus attack success rates, of many attacks in the literature, do not precisely capture the information leakage of models about their data, as they also reflect other uncertainties that the attack algorithm has. In this paper, we explain the implicit assumptions and also the simplifications made in prior work using the framework of hypothesis testing. We also derive new attack algorithms from the framework that can achieve a high AUC score while also highlighting the different factors that affect their performance. Our algorithms capture a very precise approximation of privacy loss in models, and can be used as a tool to perform an accurate and informed estimation of privacy risk in machine learning models. We provide a thorough empirical evaluation of our attack strategies on various machine learning tasks and benchmark datasets.

Abstract-- The deep neural networks (DNNs)-based autonomous driving systems (ADSs) are expected to reduce road accidents and improve safety in the transportation domain as it removes the factor of human error from driving tasks. The DNN-based ADS sometimes may exhibit erroneous or unexpected behaviours due to unexpected driving conditions which may cause accidents. Therefore, safety assurance is vital to the ADS. However, DNN-based ADS is a highly complex system that puts forward a strong demand for robustness, more specifically, the ability to predict unexpected driving conditions to prevent potential inconsistent behaviour. It is not possible to generalize the DNN model's performance for all driving conditions. Therefore, the driving conditions that were not considered during the training of the ADS may lead to unpredictable consequences for the safety of autonomous vehicles. This study proposes an autoencoder and time series analysis-based anomaly detection system to prevent the safety-critical inconsistent behaviour of autonomous vehicles at runtime. Our approach called DeepGuard consists of two components. The first component-the inconsistent behaviour predictor, is based on an autoencoder and time series analysis to reconstruct the driving scenarios. Based on reconstruction error (e) and threshold (θ), it determines the normal and unexpected driving scenarios and predicts potential inconsistent behaviour. The second component provides on-the-fly safety guards, that is, it automatically activates healing strategies to prevent inconsistencies in the behaviour. We evaluated the performance of DeepGuard in predicting the injected anomalous driving scenarios using already available open-sourced DNN-based ADSs in the Udacity simulator. Our simulation results show that the best variant of DeepGuard can predict up to 93 % on the CHAUFFEUR ADS, 83 % on DAVE-2 ADS, and 80 % of inconsistent behaviour on the EPOCH ADS model, outperforming SELFORACLE and DeepRoad. Overall, DeepGuard can prevent up to 89% of all predicted inconsistent behaviours of ADS by executing predefined safety guards. I. INTRODUCTION Autonomous vehicles are one of the most promising applications of artificial intelligence. This would be a technological revolution in the transportation industry in the near future. Autonomous driving systems (ADSs) use sensors such as cameras, radar, Lidar, and GPS to automatically produce driving parameters such as vehicle velocity, throttle, brakes, steering angles, and directions. Advancements in deep learning have made progress in autonomous systems, such as autonomous vehicles and unmanned aerial vehicles.