Latent deep sequential learning of behavioural sequences

The growing use of asynchronous online education (MOOCs and e-courses) in recent years has resulted in increased economic and scientific productivity, which has worsened during the coronavirus epidemic. The widespread usage of OLEs has increased enrolment, including previously excluded students, resulting in a far higher dropout rate than in conventional classrooms. Dropouts are a significant problem, especially considering the rising proliferation of online courses, from individual MOOCs to whole academic programmes due to the pandemic. Increased efficiency in dropout prevention techniques is vital for institutions, students, and faculty members and must be prioritised. In response to the resurgence of interest in the student dropout prediction (SDP) issue, there has been a significant rise in contributions to the literature on this topic. An in-depth review of the current state of the art literature on SDP is provided, with a special emphasis on Machine Learning prediction approaches; however, this is not the only focus of the thesis. We propose a complete hierarchical categorisation of the current literature that correlates to the process of design decisions in the SDP, and we demonstrate how it may be implemented. In order to enable comparative analysis, we develop a formal notation for universally defining the multiple dropout models examined by scholars in the area, including online degrees and their attributes. We look at several other important factors that have received less attention in the literature, such as evaluation metrics, acquired data, and privacy concerns. We emphasise deep sequential machine learning approaches and are considered to be one of the most successful solutions available in this field of study. Most importantly, we present a novel technique - namely GRU-AE - for tackling the SDP problem using hidden spatial information and time-related data from student trajectories. Our method is capable of dealing with data imbalances and time-series sparsity challenges. The proposed technique outperforms current methods in various situations, including the complex scenario of full-length courses (such as online degrees). This situation was thought to be less common before the outbreak, but it is now deemed important. Finally, we extend our findings to different contexts with a similar characterisation (temporal sequences of behavioural labels). Specifically, we show that our technique can be used in real-world circumstances where the unbalanced nature of the data can be mitigated by using class balancement technique (i.e. ADASYN), e.g., survival prediction in critical care telehealth systems where balancement technique alleviates the problem of inter-activity reliance and sparsity, resulting in an overall improvement in performance

Robust Stochastic Graph Generator for Counterfactual Explanations

Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI systems. While extensively researched in domains such as medical imaging and autonomous vehicles, Graph Counterfactual Explanation (GCE) methods have been comparatively under-explored. GCEs generate a new graph similar to the original one, with a different outcome grounded on the underlying predictive model. Among these GCE techniques, those rooted in generative mechanisms have received relatively limited investigation despite demonstrating impressive accomplishments in other domains, such as artistic styles and natural language modelling. The preference for generative explainers stems from their capacity to generate counterfactual instances during inference, leveraging autonomously acquired perturbations of the input graph. Motivated by the rationales above, our study introduces RSGG-CE, a novel Robust Stochastic Graph Generator for Counterfactual Explanations able to produce counterfactual examples from the learned latent space considering a partially ordered generation sequence. Furthermore, we undertake quantitative and qualitative analyses to compare RSGG-CE's performance against SoA generative explainers, highlighting its increased ability to engendering plausible counterfactual candidates.Comment: Accepted in AAAI'2

Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes

We introduce a novel semi-supervised Graph Counterfactual Explainer (GCE) methodology, Dynamic GRAph Counterfactual Explainer (DyGRACE). It leverages initial knowledge about the data distribution to search for valid counterfactuals while avoiding using information from potentially outdated decision functions in subsequent time steps. Employing two graph autoencoders (GAEs), DyGRACE learns the representation of each class in a binary classification scenario. The GAEs minimise the reconstruction error between the original graph and its learned representation during training. The method involves (i) optimising a parametric density function (implemented as a logistic regression function) to identify counterfactuals by maximising the factual autoencoder's reconstruction error, (ii) minimising the counterfactual autoencoder's error, and (iii) maximising the similarity between the factual and counterfactual graphs. This semi-supervised approach is independent of an underlying black-box oracle. A logistic regression model is trained on a set of graph pairs to learn weights that aid in finding counterfactuals. At inference, for each unseen graph, the logistic regressor identifies the best counterfactual candidate using these learned weights, while the GAEs can be iteratively updated to represent the continual adaptation of the learned graph representation over iterations. DyGRACE is quite effective and can act as a drift detector, identifying distributional drift based on differences in reconstruction errors between iterations. It avoids reliance on the oracle's predictions in successive iterations, thereby increasing the efficiency of counterfactual discovery. DyGRACE, with its capacity for contrastive learning and drift detection, will offer new avenues for semi-supervised learning and explanation generation

A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation

In recent years, Graph Neural Networks have reported outstanding performance in tasks like community detection, molecule classification and link prediction. However, the black-box nature of these models prevents their application in domains like health and finance, where understanding the models' decisions is essential. Counterfactual Explanations (CE) provide these understandings through examples. Moreover, the literature on CE is flourishing with novel explanation methods which are tailored to graph learning. In this survey, we analyse the existing Graph Counterfactual Explanation methods, by providing the reader with an organisation of the literature according to a uniform formal notation for definitions, datasets, and metrics, thus, simplifying potential comparisons w.r.t to the method advantages and disadvantages. We discussed seven methods and sixteen synthetic and real datasets providing details on the possible generation strategies. We highlight the most common evaluation strategies and formalise nine of the metrics used in the literature. We first introduce the evaluation framework GRETEL and how it is possible to extend and use it while providing a further dimension of comparison encompassing reproducibility aspects. Finally, we provide a discussion on how counterfactual explanation interplays with privacy and fairness, before delving into open challenges and future works.Comment: arXiv admin note: text overlap with arXiv:2107.04086 by other author

Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection

Anomalies are rare and anomaly detection is often therefore framed as One-Class Classification (OCC), i.e. trained solely on normalcy. Leading OCC techniques constrain the latent representations of normal motions to limited volumes and detect as abnormal anything outside, which accounts satisfactorily for the openset'ness of anomalies. But normalcy shares the same openset'ness property, since humans can perform the same action in several ways, which the leading techniques neglect. We propose a novel generative model for video anomaly detection (VAD), which assumes that both normality and abnormality are multimodal. We consider skeletal representations and leverage state-of-the-art diffusion probabilistic models to generate multimodal future human poses. We contribute a novel conditioning on the past motion of people, and exploit the improved mode coverage capabilities of diffusion processes to generate different-but-plausible future motions. Upon the statistical aggregation of future modes, anomaly is detected when the generated set of motions is not pertinent to the actual future. We validate our model on 4 established benchmarks: UBnormal, HR-UBnormal, HR-STC, and HR-Avenue, with extensive experiments surpassing state-of-the-art results.Comment: Accepted at ICCV202

Latent and sequential prediction of the novel coronavirus epidemiological spread

In this paper we present CoRoNNa a deep sequential framework for epidemic prediction that leverages a flexible combination of sequential and convolutional components to analyse the transmission of COVID-19 and, perhaps, other undiscovered viruses. Importantly, our methodology is generic and may be tailored to specific analysis goals. We exploit CoRoNNa to analyse the impact of various mobility containment policies on the pandemic using cumulative viral dissemination statistics with local demographic and movement data from several nations. Our experiments show that data on mobility has a significant, but delayed, impact on viral propagation. When compared to alternative frameworks that combine multivariate lagged predictors and basic LSTM models, CoRoNNa outperforms them. On the contrary, no technique based solely on lagged viral dissemination statistics can forecast daily cases

CoRoNNa: A Deep Sequential Framework to Predict Epidemic Spread

We propose CORONNA, a deep framework for epidemic prediction to analyse the spread of COVID-19 and, potentially, of other unknown viruses, based on a flexible integration of sequential and convolutional components. Importantly, our framework is general and can be specialised according to different analysis objectives. In this paper, the specific purpose is to optimise CORONNA for analysing the impact of different mobility containment policies on the epidemic. To this end, we integrate cumulative viral diffusion statistics and local demographic and mobility information of several countries. Our analysis confirms that mobility data have a strong, but delayed, effect on the viral spread. In this context, CORONNA has superior performances when compared with other frameworks that incorporate multivariate lagged predictors, and with simple LSTM models. On the contrary, no method is able to predict daily cases based only on lagged viral diffusion statistics

Towards Non-Adversarial Algorithmic Recourse

The streams of research on adversarial examples and counterfactual explanations have largely been growing independently. This has led to several recent works trying to elucidate their similarities and differences. Most prominently, it has been argued that adversarial examples, as opposed to counterfactual explanations, have a unique characteristic in that they lead to a misclassification compared to the ground truth. However, the computational goals and methodologies employed in existing counterfactual explanation and adversarial example generation methods often lack alignment with this requirement. Using formal definitions of adversarial examples and counterfactual explanations, we introduce non-adversarial algorithmic recourse and outline why in high-stakes situations, it is imperative to obtain counterfactual explanations that do not exhibit adversarial characteristics. We subsequently investigate how different components in the objective functions, e.g., the machine learning model or cost function used to measure distance, determine whether the outcome can be considered an adversarial example or not. Our experiments on common datasets highlight that these design choices are often more critical in deciding whether recourse is non-adversarial than whether recourse or attack algorithms are used. Furthermore, we show that choosing a robust and accurate machine learning model results in less adversarial recourse desired in practice

Hidden space deep sequential risk prediction on student trajectories

Online learning environments (OLEs) have seen a continuous increase over the past decade and a sudden surge in the last year, due to the coronavirus outbreak. The widespread use of OLEs has led to an increasing number of enrolments, even from students who had previously left education systems, but it also resulted in a much higher dropout rate than in traditional classrooms. This is a crucial problem since online courses have rapidly expanded from individual MOOCs to entire study programmes, also due to the pandemic. Early detection of students in difficulty is a challenging problem that can be mitigated with the support of state-of-the-art methods for data analytics and machine learning. In this study, we propose a novel strategy that exploits both hidden space information and time-related data from student trajectories and copes with unbalanced data and time-series sparsity issues to solve the student dropout prediction problem. The proposed approach outperforms state-of-the-art methods, particularly in the complex case of full-length curricula (such as online degrees), a scenario that was thought to be less common before the pandemic, but is now particularly relevant

House in the (biometric) cloud: a possible application

This article presents a novel approach to extend cloud computing from company services to consumer biometrics. The proposed system recognizes the person at the door, allowing entrance or denying it according to the recognition result. Very little processing is required locally, and biometrics is implemented as a service