On the use of Machine Learning methods in rock art research with application to automatic painted rock art identification

Rock art is globally recognized as significant, yet the resources allocated to the study and exploration of this important form of cultural heritage are often scarce. In areas where numerous rock art sites exist, much of the rock art is unidentified and therefore remains, unrecorded and unresearched. Manually identifying rock art is time-consuming, tedious, and expensive. Therefore, it is necessary to automate many processes in rock art research, which can be accomplished by Machine Learning. Artificial Intelligence (AI) and Machine Learning (ML) can greatly facilitate rock art research in many ways, such as through Object Recognition and Detection, Motif Extraction, Object Reconstruction, Image Knowledge Graphs, and Representations. This article is a reflective work on the future of ML for rock art research. As a proof-of-concept, it presents a machine learning method based on recent advances in deep learning to train a model to identify images with painted rock art (pictograms). The efficacy of the proposed method is shown using data collected from fieldwork in Australia. Furthermore, our proposed method can be used to train models that are specific to the rock art found in different regions. We provide the code and the trained models in the supplementary section

MEG: Masked Ensemble Tabular Data Generator

MEG: Masked Ensemble Tabular Data Generato

GANBLR++: Incorporating Capacity to Generate Numeric Attributes and Leveraging Unrestricted Bayesian Networks

Generative Adversarial Networks (GAN) models have led to a major breakthrough in data generation of various sorts. Over the years, we have seen several applications of GAN-based learning for tabular data generation as well. Very recently, GAN-based learning by incorporating Bayesian Networks (BN) as generator and discriminator – GANBLR, has shown to lead to state-of-the-art (SOTA) results for tabular data generation. Despite the impressive performance, GANBLR has an inherent weakness that it can only generate data with categorical attributes. Additionally, the model is trained and tested only with a restricted Bayesian Network. In this work, we have proposed an extension over GANBLR framework – GANBLR++, that has the capacity to generate numeric attributes, by leveraging Dirichlet Mixture Model. We also leverage unrestricted BN in GANBLR framework, and discuss how the use of unrestricted BN can lead to better quality data, as well as more interpretable model. We evaluate the effectiveness of GANBLR++ on wide range of datasets by demonstrating that it generates data of better quality as compared to existing SOTA models for tabular (numeric and categorical) data generation such as CTGAN, MedGAN and TableGAN

Improving neural network’s robustness on tabular data with D-layers

AbstractArtificial neural networks ($${{{\texttt {ANN}}}}$$ ANN ) are widely used machine learning models. Their widespread use has attracted a lot of interest in their robustness. Many studies show that ’s performance can be highly vulnerable to input manipulation such as adversarial attacks and covariate drift. Therefore, various techniques that focus on improving $${{{\texttt {ANN}}}}$$ ANN ’s robustness have been proposed in the last few years. However, most of these works have mostly focused on image data. In this paper, we investigate the role of discretization in improving $${{{\texttt {ANN}}}}$$ ANN ’s robustness on tabular datasets. Two custom $${{{\texttt {ANN}}}}$$ ANN layers–  and  (collectively called ) are proposed. The two layers integrate discretization during the training phase to improve $${{{\texttt {ANN}}}}$$ ANN ’s ability to defend against adversarial attacks. Additionally,  integrates dynamic discretization during testing phase as well, to provide a unified strategy to handle adversarial attacks and covariate drift. The experimental results on 24 publicly available datasets show that our proposed  add much-needed robustness to $${{{\texttt {ANN}}}}$$ ANN for tabular datasets

Interpretable tabular data generation

Generative adversarial network (GAN) models have been successfully utilized in a wide range of machine learning applications, and tabular data generation domain is not an exception. Notably, some state-of-the-art models of tabular data generation, such as CTGAN, TableGan, MedGAN, etc. are based on GAN models. Even though these models have resulted in superior performance in generating artificial data when trained on a range of datasets, there is a lot of room (and desire) for improvement. Not to mention that existing methods do have some weaknesses other than performance. For example, the current methods focus only on the performance of the model, and limited emphasis is given on the interpretation of the model. Secondly, the current models operate on raw features only, and hence they fail to exploit any prior knowledge on explicit feature interactions that can be utilized during data generation process. To alleviate the two above-mentioned limitations, in this work, we propose a novel tabular data generation model—GenerativeAdversarial Network modelling inspired fromNaiveBayes andLogisticRegression’s relationship (GANBLR), which not only address the interpretation limitation of existing tabular GAN-based models but provides capability to handle explicit feature interactions as well. Through extensive evaluations on wide range of datasets, we demonstrate GANBLR’s superior performance as well as better interpretable capability (explanation of feature importance in the synthetic generation process) as compared to existing state-of-the-art tabular data generation models

Robust quantification of prediction uncertainty by inducing heterogeneity in deep ensembles

Robust quantification of prediction uncertainty by inducing heterogeneity in deep ensemble

Discretization Inspired Defence Algorithm Against Adversarial Attacks on Tabular Data

Deep learning methods are usually trained via a gradient-descent based procedure, which can be efficient as it is not only end-to-end but also suitable for large quantities of data. However, gradient-based learning is vulnerable to adversarial attacks – which account for unperceivable changes in the input data to misguide a trained model. Though a plethora of work explored the adversarial learning (attacks and defences) in image datasets, the exploration of adversarial learning in tabular datasets has seen little attention. In this work, we study adversarial learning in tabular datasets. We investigate the role of discretization and demonstrate that discretizing numeric attributes offers a strong defence mechanism. The main contribution of this work is the proposition of two new defence algorithms for numeric tabular datasets, that utilize cut-points obtained from discretization, to forge a defence against various forms of adversarial attacks. We evaluate the effectiveness of our proposed method on a wide range of machine learning datasets and demonstrate that the proposed algorithms lead to a state-of-the-art defence strategy on tabular datasets

Robust visual question answering via semantic cross modal augmentation

Recent advances in vision-language models have resulted in improved accuracy in visual question answering (VQA) tasks. However, their robustness remains limited when faced with out-of-distribution data containing unanswerable questions. In this study, we first construct a simple randomised VQA dataset, incorporating unanswerable questions from the VQA v2 dataset, to evaluate the robustness of a state-of-the-art VQA model. Our findings reveal that the model struggles to predict the “unknown” answer or provides inaccurate responses with high confidence scores for irrelevant questions. To address this issue without retraining the large backbone models, we propose Cross Modal Augmentation (CMA), a model-agnostic, test-time-only, multi-modal semantic augmentation technique. CMA generates multiple semantically-consistent but heterogeneous instances from the visual and textual inputs, which are then fed to the model, and the predictions are combined to achieve a more robust output. We demonstrate that implementing CMA enables the VQA model to provide more reliable answers in scenarios involving unanswerable questions, and show that the approach is generalisable across different categories of pre-trained vision language models

Leveraging Generative Models for Combating Adversarial Attacks on Tabular Datasets

Leveraging Generative Models for Combating Adversarial Attacks on Tabular Dataset

Property graph representation learning for node classification

AbstractGraph representation learning (graph embedding) has led to breakthrough results in various machine learning graph-based applications such as node classification, link prediction and recommendation. Many real-world graphs can be characterized as the property graphs, because besides the structure information, there exists rich property information related to each node in the graphs. Many existing graph representation learning methods—e.g. random walk-based methods like and , focus only on the structure of graph for learning the node embedding. Although graph representation learning based on neural networks (e.g. typical methods such as ) uses the property of nodes as the initial features of nodes and then aggregates feature information of the neighbours, their limitation is that the neighbourhood of a node is considered to be uniform—i.e. there is no way to differentiate among neighbours of a node when learning a node embedding. Additionally, their definition of neighbourhood is local, i.e. only nodes connected to the current node are considered as neighbours. Hence, those methods fail to capture implicit/latent relationships among nodes, which are implicit in the given structure. In this study, our aim is to improve the performance of graph representation learning methods on property graphs. We present a new framework called  ()—a graph representation learning framework to address above-mentioned limitations. Our proposed framework relies on the notion of latent neighbourhood, as well as systematic sampling of neighbouring nodes to obtain better representation of the nodes. The experimental results on five publicly available graph datasets demonstrate that outperforms state-of-the-art baselines for the task of node classification. We further evaluate the superiority of our proposed formulation by defining a novel quantitative metric to measure the usefulness of the sampled neighbourhood in the graph