Data-driven unsupervised clustering of online learner behaviour

The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here we introduce a mathematical framework for the analysis of time series of online learner engagement, which allows the identification of clusters of learners with similar online temporal behaviour directly from the raw data without prescribing a priori subjective reference behaviours. The method uses a dynamic time warping kernel to create a pairwise similarity between time series of learner actions, and combines it with an unsupervised multiscale graph clustering algorithm to identify groups of learners with similar temporal behaviour. To showcase our approach, we analyse task completion data from a cohort of learners taking an online post-graduate degree at Imperial Business School. Our analysis reveals clusters of learners with statistically distinct patterns of engagement, from distributed to massed learning, with different levels of regularity, adherence to pre-planned course structure and task completion. The approach also reveals outlier learners with highly sporadic behaviour. A posteriori comparison against student performance shows that, whereas high performing learners are spread across clusters with diverse temporal engagement, low performers are located significantly in the massed learning cluster, and our unsupervised clustering identifies low performers more accurately than common machine learning classification methods trained on temporal statistics of the data. Finally, we test the applicability of the method by analysing two additional datasets: a different cohort of the same course, and time series of different format from another university

Multi-stage machine learning model for hierarchical tie valence prediction

This is the final version. Available from the Association for Computing Machinery via the DOI in this record. Data availability: Due to our non-disclosure agreement with the organization and our Institutional Review Board data management protocol, the raw data cannot be shared.Individuals interacting in organizational settings involving varying levels of formal hierarchy naturally form a complex network of social ties having different tie valences (e.g., positive and negative connections). Social ties critically affect employees' satisfaction, behaviors, cognition, and outcomes - yet identifying them solely through survey data is challenging because of the large size of some organizations or the often hidden nature of these ties and their valences. We present a novel deep learning model encompassing NLP and graph neural network techniques that identifies positive and negative ties in a hierarchical network. The proposed model uses human resource attributes as node information and web-logged work conversation data as link information. Our findings suggest that the presence of conversation data improves the tie valence classification by 8.91% compared to employing user attributes alone. This gain came from accurately distinguishing positive ties, particularly for male, non-minority, and older employee groups. We also show a substantial difference in conversation patterns for positive and negative ties with positive ties being associated with more messages exchanged on weekends, and lower use of words related to anger and sadness. These findings have broad implications for facilitating collaboration and managing conflict within organizational and other social networks.Institute for Basic Sciences (IBS), Republic of KoreaNational Research Foundation of Korea (NRF

SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces

Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective functions can be queried in parallel. However, current methods do not scale to large batch sizes -- a frequent desideratum in practice (e.g. drug discovery or simulation-based inference). We present a novel algorithm, SOBER, which permits scalable and diversified batch global optimisation and quadrature with arbitrary acquisition functions and kernels over discrete and mixed spaces. The key to our approach is to reformulate batch selection for global optimisation as a quadrature problem, which relaxes acquisition function maximisation (non-convex) to kernel recombination (convex). Bridging global optimisation and quadrature can efficiently solve both tasks by balancing the merits of exploitative Bayesian optimisation and explorative Bayesian quadrature. We show that SOBER outperforms 11 competitive baselines on 12 synthetic and diverse real-world tasks.Comment: 34 pages, 12 figure

Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy

Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation and similar domains. Despite the significant successes achieved to date, applying GANs to real-world problems still poses significant challenges, three of which we focus on here. These are: (1) the generation of high quality images, (2) diversity of image generation, and (3) stable training. Focusing on the degree to which popular GAN technologies have made progress against these challenges, we provide a detailed review of the state of the art in GAN-related research in the published scientific literature. We further structure this review through a convenient taxonomy we have adopted based on variations in GAN architectures and loss functions. While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision. Accordingly, we review and critically discuss the most popular architecture-variant, and loss-variant GANs, for tackling these challenges. Our objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress towards important computer vision application requirements. As we do this we also discuss the most compelling applications in computer vision in which GANs have demonstrated considerable success along with some suggestions for future research directions. Code related to GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.Comment: Accepted by ACM Computing Surveys, 23 November 202

Development and evaluation of machine learning algorithms for biomedical applications

Gene network inference and drug response prediction are two important problems in computational biomedicine. The former helps scientists better understand the functional elements and regulatory circuits of cells. The latter helps a physician gain full understanding of the effective treatment on patients. Both problems have been widely studied, though current solutions are far from perfect. More research is needed to improve the accuracy of existing approaches. This dissertation develops machine learning and data mining algorithms, and applies these algorithms to solve the two important biomedical problems. Specifically, to tackle the gene network inference problem, the dissertation proposes (i) new techniques for selecting topological features suitable for link prediction in gene networks; a graph sparsification method for network sampling; (iii) combined supervised and unsupervised methods to infer gene networks; and (iv) sampling and boosting techniques for reverse engineering gene networks. For drug sensitivity prediction problem, the dissertation presents (i) an instance selection technique and hybrid method for drug sensitivity prediction; (ii) a link prediction approach to drug sensitivity prediction; a noise-filtering method for drug sensitivity prediction; and (iv) transfer learning approaches for enhancing the performance of drug sensitivity prediction. Substantial experiments are conducted to evaluate the effectiveness and efficiency of the proposed algorithms. Experimental results demonstrate the feasibility of the algorithms and their superiority over the existing approaches

Forestogram: Biclustering Visualization Framework with Applications in Public Transport and Bioinformatics

RÉSUMÉ : Dans de nombreux problèmes d’analyse de données, les données sont exprimées dans une matrice avec les sujets en ligne et les attributs en colonne. Les méthodes de segmentations traditionnelles visent à regrouper les sujets (lignes), selon des critères de similitude entre ces sujets. Le but est de constituer des groupes de sujets (lignes) qui partagent un certain degré de ressemblance. Les groupes obtenus permettent de garantir que les sujets partagent des similitudes dans leurs attributs (colonnes), il n’y a cependant aucune garantie sur ce qui se passe au niveau des attributs (les colonnes). Dans certaines applications, un regroupement simultané des lignes et des colonnes appelé biclustering de la matrice de données peut être souhaité. Pour cela, nous concevons et développons un nouveau cadre appelé Forestogram, qui permet le calcul de ce regroupement simultané des lignes et des colonnes (biclusters)dans un mode hiérarchique. Le regroupement simultané des lignes et des colonnes de manière hiérarchique peut aider les praticiens à mieux comprendre comment les groupes évoluent avec des propriétés théoriques intéressantes. Forestogram, le nouvel outil de calcul et de visualisation proposé, pourrait être considéré comme une extension 3D du dendrogramme, avec une fusion orthogonale étendue. Chaque bicluster est constitué d’un groupe de lignes (ou de sujets) qui déplie un schéma fortement corrélé avec le groupe de colonnes (ou attributs) correspondantes. Cependant, au lieu d’effectuer un clustering bidirectionnel indépendamment de chaque côté, nous proposons un algorithme de biclustering hiérarchique qui prend les lignes et les colonnes en même temps pour déterminer les biclusters. De plus, nous développons un critère d’information basé sur un modèle qui fournit un nombre estimé de biclusters à travers un ensemble de configurations hiérarchiques au sein du forestogramme sous des hypothèses légères. Nous étudions le cadre suggéré dans deux perspectives appliquées différentes, l’une dans le domaine du transport en commun, l’autre dans le domaine de la bioinformatique. En premier lieu, nous étudions le comportement des usagers dans le transport en commun à partir de deux informations distinctes, les données temporelles et les coordonnées spatiales recueillies à partir des données de transaction de la carte à puce des usagers. Dans de nombreuses villes, les sociétés de transport en commun du monde entier utilisent un système de carte à puce pour gérer la perception des tarifs. L’analyse de cette information fournit un aperçu complet de l’influence de l’utilisateur dans le réseau de transport en commun interactif. À cet égard, l’analyse des données temporelles, décrivant l’heure d’entrée dans le réseau de transport en commun est considérée comme la composante la plus importante des données recueillies à partir des cartes à puce. Les techniques classiques de segmentation, basées sur la distance, ne sont pas appropriées pour analyser les données temporelles. Une nouvelle projection intuitive est suggérée pour conserver le modèle de données horodatées. Ceci est introduit dans la méthode suggérée pour découvrir le modèle temporel comportemental des utilisateurs. Cette projection conserve la distance temporelle entre toute paire arbitraire de données horodatées avec une visualisation significative. Par conséquent, cette information est introduite dans un algorithme de classification hiérarchique en tant que méthode de segmentation de données pour découvrir le modèle des utilisateurs. Ensuite, l’heure d’utilisation est prise en compte comme une variable latente pour rendre la métrique euclidienne appropriée dans l’extraction du motif spatial à travers notre forestogramme. Comme deuxième application, le forestogramme est testé sur un ensemble de données multiomiques combinées à partir de différentes mesures biologiques pour étudier comment l’état de santé des patientes et les modalités biologiques correspondantes évoluent hiérarchiquement au cours du terme de la grossesse, dans chaque bicluster. Le maintien de la grossesse repose sur un équilibre finement équilibré entre la tolérance à l’allogreffe foetale et la protection mécanismes contre les agents pathogènes envahissants. Malgré l’impact bien établi du développement pendant les premiers mois de la grossesse sur les résultats à long terme, les interactions entre les divers mécanismes biologiques qui régissent la progression de la grossesse n’ont pas été étudiées en détail. Démontrer la chronologie de ces adaptations à la grossesse à terme fournit le cadre pour de futures études examinant les déviations impliquées dans les pathologies liées à la grossesse, y compris la naissance prématurée et la prééclampsie. Nous effectuons une analyse multi-physique de 51 échantillons de 17 femmes enceintes, livrant à terme. Les ensembles de données comprennent des mesures de l’immunome, du transcriptome, du microbiome, du protéome et du métabolome d’échantillons obtenus simultanément chez les mêmes patients. La modélisation prédictive multivariée utilisant l’algorithme Elastic Net est utilisée pour mesurer la capacité de chaque ensemble de données à prédire l’âge gestationnel. En utilisant la généralisation empilée, ces ensembles de données sont combinés en un seul modèle. Ce modèle augmente non seulement significativement le pouvoir prédictif en combinant tous les ensembles de données, mais révèle également de nouvelles interactions entre différentes modalités biologiques. En outre, notre forestogramme suggéré est une autre ligne directrice avec l’âge gestationnel au moment de l’échantillonnage qui fournit un modèle non supervisé pour montrer combien d’informations supervisées sont nécessaires pour chaque trimestre pour caractériser les changements induits par la grossesse dans Microbiome, Transcriptome, Génome, Exposome et Immunome réponses efficacement.----------ABSTRACT : In many statistical modeling problems data are expressed in a matrix with subjects in row and attributes in column. In this regard, simultaneous grouping of rows and columns known as biclustering of the data matrix is desired. We design and develop a new framework called Forestogram, with the aim of fast computational and hierarchical illustration of biclusters. Often in practical data analysis, we deal with a two-dimensional object known as the data matrix, where observations are expressed as samples (or subjects) in rows, and attributes (or features) in columns. Thus, simultaneous grouping of rows and columns in a hierarchical manner helps practitioners better understanding how clusters evolve. Forestogram, a novel computational and visualization tool, could be thought of as a 3D expansion of dendrogram, with extended orthogonal merge. Each bicluster consists of group of rows (or samples) that unfolds a highly-correlated schema with their corresponding group of columns (or attributes). However, instead of performing two-way clustering independently on each side, we propose a hierarchical biclustering algorithm which takes rows and columns at the same time to determine the biclusters. Furthermore, we develop a model-based information criterion which provides an estimated number of biclusters through a set of hierarchical configurations within the forestogram under mild assumptions. We study the suggested framework in two different applied perspectives, one in public transit domain, another one in bioinformatics field. First, we investigate the users’ behavior in public transit based on two distinct information, temporal data and spatial coordinates gathered from smart card. In many cities, worldwide public transit companies use smart card system to manage fare collection. Analysis of this information provides a comprehensive insight of user’s influence in the interactive public transit network. In this regard, analysis of temporal data, describing the time of entering to the public transit network is considered as the most substantial component of the data gathered from the smart cards. Classical distance-based techniques are not always suitable to analyze this time series data. A novel projection with intuitive visual map from higher dimension into a three-dimensional clock-like space is suggested to reveal the underlying temporal pattern of public transit users. This projection retains the temporal distance between any arbitrary pair of time-stamped data with meaningful visualization. Consequently, this information is fed into a hierarchical clustering algorithm as a method of data segmentation to discover the pattern of users. Then, the time of the usage is taken as a latent variable into account to make the Euclidean metric appropriate for extracting the spatial pattern through our forestogram. As a second application, forestogram is tested on a multiomics dataset combined from different biological measurements to study how patients and corresponding biological modalities evolve hierarchically in each bicluster over the term of pregnancy. The maintenance of pregnancy relies on a finely-tuned balance between tolerance to the fetal allograft and protective mechanisms against invading pathogens. Despite the well-established impact of development during the early months of pregnancy on long-term outcomes, the interactions between various biological mechanisms that govern the progression of pregnancy have not been studied in details. Demonstrating the chronology of these adaptations to term pregnancy provides the framework for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. We perform a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets include measurements from the immunome, transcriptome, microbiome, proteome, and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net algorithm is used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets are combined into a single model. This model not only significantly increases the predictive power by combining all datasets, but also reveals novel interactions between different biological modalities. Furthermore, our suggested forestogram is another guideline along with the gestational age at time of sampling that provides an unsupervised model to show how much supervised information is necessary for each trimester to characterize the pregnancy-induced changes in Microbiome, Transcriptome, Genome, Exposome, and Immunome responses effectively

Local selection of features and its applications to image search and annotation

In multimedia applications, direct representations of data objects typically involve hundreds or thousands of features. Given a query object, the similarity between the query object and a database object can be computed as the distance between their feature vectors. The neighborhood of the query object consists of those database objects that are close to the query object. The semantic quality of the neighborhood, which can be measured as the proportion of neighboring objects that share the same class label as the query object, is crucial for many applications, such as content-based image retrieval and automated image annotation. However, due to the existence of noisy or irrelevant features, errors introduced into similarity measurements are detrimental to the neighborhood quality of data objects. One way to alleviate the negative impact of noisy features is to use feature selection techniques in data preprocessing. From the original vector space, feature selection techniques select a subset of features, which can be used subsequently in supervised or unsupervised learning algorithms for better performance. However, their performance on improving the quality of data neighborhoods is rarely evaluated in the literature. In addition, most traditional feature selection techniques are global, in the sense that they compute a single set of features across the entire database. As a consequence, the possibility that the feature importance may vary across different data objects or classes of objects is neglected. To compute a better neighborhood structure for objects in high-dimensional feature spaces, this dissertation proposes several techniques for selecting features that are important to the local neighborhood of individual objects. These techniques are then applied to image applications such as content-based image retrieval and image label propagation. Firstly, an iterative K-NN graph construction method for image databases is proposed. A local variant of the Laplacian Score is designed for the selection of features for individual images. Noisy features are detected and sparsified iteratively from the original standardized feature vectors. This technique is incorporated into an approximate K-NN graph construction method so as to improve the semantic quality of the graph. Secondly, in a content-based image retrieval system, a generalized version of the Laplacian Score is used to compute different feature subspaces for images in the database. For online search, a query image is ranked in the feature spaces of database images. Those database images for which the query image is ranked highly are selected as the query results. Finally, a supervised method for the local selection of image features is proposed, for refining the similarity graph used in an image label propagation framework. By using only the selected features to compute the edges leading from labeled image nodes to unlabeled image nodes, better annotation accuracy can be achieved. Experimental results on several datasets are provided in this dissertation, to demonstrate the effectiveness of the proposed techniques for the local selection of features, and for the image applications under consideration