Reviewerly/exHarmony huggingface dataset:
Digital Vaccination Records: Exploring Stakeholder Perceptions in Gauteng, South Africa

Electronic health (eHealth) is one of the focus areas of the South African Department of Health (DoH), with the ultimate goal being the development of an electronic health record (EHR) for every citizen. A commonly used subset of eHealth data, vaccination records, is still not yet fully digitised in South Africa. This study aimed to determine the perceptions of key stakeholders (doctors, nurses, parents, and school administrators) about a digital system for vaccination records for minors in South Africa’s Gauteng Province. Using a prototype online, cloud-based vaccine records management system created during the research, called e-Vaccination, quantitative and qualitative interaction-related data from 118 participants was collected using a five-point Likert-scale questionnaire. The questionnaire was based on Lund’s (2001) USE user perception framework, which considers usefulness, satisfaction, ease of use, and ease of learning. This study found that the participants supported the use of the digital vaccine records management system, with an emphasis on five identified factors: user friendliness, graphical design, practicality, user experience, and usability. Accordingly, this article recommends that policymakers and system designers carefully consider these factors in the design and development of South Africa’s digital vaccination records management system.
Prototype-Based Explanations for Graph Neural Networks (Student Abstract)

Aside the high performance of graph neural networks (GNNs), considerable attention has recently been paid to explanations of black-box deep learning models. Unlike most studies focusing on model explanations based on a specific graph instance, we propose Prototype-bAsed GNN-Explainer (PAGE), a novel model-level explanation method for graph-level classification that explains what the underlying model has learned by providing human-interpretable prototypes. Specifically, our method performs clustering on the embedding space of the underlying GNN model; extracts embeddings in each cluster; and discovers prototypes, which serve as model explanations, by estimating the maximum common subgraph (MCS) from the extracted embeddings. Experimental evaluation demonstrates that PAGE not only provides high-quality explanations but also outperforms the state-of-the-art model-level method in terms of consistency and faithfulness that are performance metrics for quantitative evaluations.
Big Data Analytics for Credit Card Fraud Detection Using Supervised Machine Learning Models

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model's resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.
MSML: Enhancing Occlusion-Robustness by Multi-Scale Segmentation-Based Mask Learning for Face Recognition

In unconstrained scenarios, face recognition remains challenging, particularly when faces are occluded. Existing methods generalize poorly due to the distribution distortion induced by unpredictable occlusions. To tackle this problem, we propose a hierarchical segmentation-based mask learning strategy for face recognition, enhancing occlusion-robustness by integrating segmentation representations of occlusion into face recognition in the latent space. We present a novel multi-scale segmentation-based mask learning (MSML) network, which consists of a face recognition branch (FRB), an occlusion segmentation branch (OSB), and hierarchical elaborate feature masking (FM) operators. With the guidance of hierarchical segmentation representations of occlusion learned by the OSB, the FM operators can generate multi-scale latent masks to eliminate mistaken responses introduced by occlusions and purify the contaminated facial features at multiple layers. In this way, the proposed MSML network can effectively identify and remove the occlusions from feature representations at multiple levels and aggregate features from visible facial areas. Experiments on face verification and recognition under synthetic or realistic occlusions demonstrate the effectiveness of our method compared to state-of-the-art methods.
StoryQ—an Online Environment for Machine Learning of Text Classification

The StoryQ environment provides an intuitive graphical user interface for middle and high school students to create features from unstructured text data and train and test classification models using logistic regression. StoryQ runs in a web browser, is free and requires no installation. AI concepts addressed include: features, weights, accuracy, training, bias, error analysis and cross validation. Using the software in conjunction with curriculum currently under development is expected to lead to student understanding of machine learning concepts and workflow; developing the ability to use domain knowledge and basic linguistics to identify, create, analyze, and evaluate features; becoming aware of and appreciating the roles and responsibilities of AI developers;. This paper will consist of an online demo with a brief video walkthrough.
Outline of a Theory of Reasons

Abstract This paper investigates the logic of reasons. Its aim is to provide an analysis of the sentences of the form ‘p is a reason for q’ that yields a coherent account of their logical properties. The idea that we will develop is that ‘p is a reason for q’ is acceptable just in case a suitably defined relation of incompatibility obtains between p and ¬q. As we will suggest, a theory of reasons based on this idea can solve three challenging puzzles that concern, respectively, contraposing reasons, conflicting reasons, and supererogatory reasons, and opens a new perspective on some classical issues concerning non-deductive inferences.
A model for developing cross-curricular digital competences of higher education students in non-core areas it areas in the process of additional education

Introduction. One of the urgent tasks in the implementation of the federal project "Workforce for the Digital Economy", which is part of the national digital economy programme, the transition to the digital economy is the development of professional competencies for workers in various sectors in the field of end-to-end digital technology. The digital competencies are developed in response to the requirements of society, the state, and the labour market for the individual in the digital society. Materials and Methods. The analysis, generalization and systematization of scientific research, and personal experience of formation and development of digital competences allowed us to identify their universality and the end-to-end principle of mastering them, which allowed us to draw a conclusion about the feasibility and necessity of developing a pedagogical model for forming end-to-end digital competences of higher education students in non-core IT disciplines. Results. The research defines the concept of «transversal digital competences» (TDC) at the stage of digital transformation of higher education; presents components, criteria, indicators and indices of formation of transversal digital competences of students; presents a pedagogical model for shaping transversal digital competences of students of non-profile IT fields in a classical university in the process of additional professional education. The stages of formation of end-to-end digital competences of students are considered in detail, the purpose of pedagogical model, tasks, principles, factors and organizational and pedagogical conditions of formation are defined, the results of research are presented. Discussion and Conclusions. This pedagogical model was developed in the process of studying the conditions for the formation of digital competences and skills of using digital technologies in students at Kemerovo State University. Partially the results of this study were presented in the report «Basic principles of formation of end-to-end digital competences in students in the process of mastering additional professional programmes» at the II All-Russian scientific-practical conference with international participation «Digital humanities and educational technologies - DHTE 2021» (organizer - FSBEI VPO "Moscow State Psychological and Pedagogical University").
A Two-Stage High-Order Modulation Recognition Based on Projected Accumulated Constellation Vector in Non-Cooperative B5G OSTBC-OFDM Systems

Blind modulation recognition (BMR) is a pivotal signal processing technology for cognitive receivers to reduce signaling overhead and improve transmission efficiency. Although BMR techniques have been extensively explored for single-carrier systems, only a few research have been published for OSTBC-OFDM systems. More troubling is that high-order modulation types will make the BMR harder because the constellation points are overcrowded. To remedy these flaws, we propose a two-stage high-order BMR approach that leverages low-complexity cumulants and projected accumulated constellation vector (P-ACV) to recognize high-order modulation types (e.g., 2048QAM). Firstly, zero-forcing blind equalization is employed to recover the damaged signal and enhance its feature representation. Next, the first-stage recognition module leverages the fourth-order cumulants to classify 13 modulation types into three groups, including PSK&PAM, square-shaped QAM and cross-shaped QAM groups. Finally, the second-stage recognition module distinguishes the inter-class signal in each group by P-ACV features. The temporal convolution network is built to tap the potentialities of P-ACV and speed up online inference. Numerical results demonstrate that our two-stage algorithm obtains a recognition accuracy of 97.37% when signal-to-noise-ratio (SNR) ≥ 12 dB and performs six times faster than the suboptimal method. Notably, it does not require prior knowledge, such as channel state information (CSI) and SNR conditions.
Monogenic features based single sample face recognition by kernel sparse representation on multiple Riemannian manifolds

This paper presents a novel method for single sample face recognition, characterized by the use of grayscale monogenic features of the face images, and the kernel sparse representation on multiple Riemannian manifolds. To indicate regional face discriminability, the multi-scale extended monogenic features are firstly locally extracted from different regions of an image, according to a specific face partition scheme, and then the intrinsic subspace of the feature vector set corresponding to each region is further extracted and modeled as a point of a Grassmann manifold. The congeneric local feature scheme is also exploited to entire image for the extraction of co-occurrence distributions of the grouped feature images of intersectional dimensions, based on a special binarization scheme applied to the resultant feature images. This derives the auxiliary marginal distribution-based descriptors residing on the closures of multiple multinomial manifolds. To train the kernel sparse representation classifier using the combined descriptors for a recognition task, the strategy of kernel alignment combining column L2-norm-based kernel matrix normalization are adopted for multiple kernel fusion, where the used kernels are all derived from Riemannian geometries of two types of manifolds. The superiority of our method is demonstrated through extensive experiments.
Hierarchical Image Generation via Transformer-Based Sequential Patch Selection

To synthesize images with preferred objects and interactions, a controllable way is to generate the image from a scene graph and a large pool of object crops, where the spatial arrangements of the objects in the image are defined by the scene graph while their appearances are determined by the retrieved crops from the pool. In this paper, we propose a novel framework with such a semi-parametric generation strategy. First, to encourage the retrieval of mutually compatible crops, we design a sequential selection strategy where the crop selection for each object is determined by the contents and locations of all object crops that have been chosen previously. Such process is implemented via a transformer trained with contrastive losses. Second, to generate the final image, our hierarchical generation strategy leverages hierarchical gated convolutions which are employed to synthesize areas not covered by any image crops, and a patch guided spatially adaptive normalization module which is proposed to guarantee the final generated images complying with the crop appearance and the scene graph. Evaluated on the challenging Visual Genome and COCO-Stuff dataset, our experimental results demonstrate the superiority of our proposed method over existing state-of-the-art methods.
Vine copula statistical disclosure control for mixed-type data

In this paper, we develop a new statistical disclosure control (SDC) method for mixed-type data based on vine copulas. The use of Gaussian and skew-t copulas has been demonstrated to be capable of incorporating information from the marginal distributions of mixed-type variables, whether they are discrete or continuous. In particular, our proposed SDC method using vine copulas generalizes a data perturbation method using an extended skew-t copula. Our vine-SDC method improves the SDC method using the extended skew-t copula by allowing the bivariate copulas in the vine decomposition to take various forms, thus offering a better fit for the joint distribution of the data and more flexibility in data perturbation. An additional advantage of our vine-SDC method is the significant improvement in computational efficiency compared with that using the extended skew-t copula. We discuss some statistical properties of vine copulas and the methodology of vine-SDC. A simulation and a study of real healthcare survey data are provided to explore the performance and strength of vine-SDC and compare it with a common copula-based SDC method.
Efficient Design of Low Bitwidth Convolutional Neural Networks on FPGA with Optimized Dot Product Units

Designing hardware accelerators to run the inference of convolutional neural networks (CNN) is under intensive research. Several different architectures have been proposed along with hardware-oriented optimizations of the neural network models. One of the most used optimizations is quantization since it reduces the memory requirements to store weights and layer maps, the memory bandwidth requirements and the hardware complexity. As a consequence, the inference throughput has improved and the computing cost has been reduced, allowing inference to be executed on embedded devices. In this work, we propose highly efficient dot-product arithmetic units for ternary and non-ternary convolutional neural networks on FPGA. The non-ternary dot-product unit uses a fused multiply-add that avoids expensive adder trees, while the ternary dot-product unit uses a dual product unit followed by an optimized conditional adder tree structure. In both cases, designs with and without embedded DSP are considered. The solution is configurable and can be adapted to the available number of resources of the FPGA to achieve the best efficiency. A CNN architecture was developed and characterized using the proposed dot product units. The results show a performance improvement of 1.8 × with a 2× more area efficiency for low bit-width quantizations when compared to previous works running large CNNs in FPGA.
Refined analysis and a hierarchical multi-task learning approach for loan fraud detection

Fraud problems in loan application assessment cause significant losses for finance companies worldwide, and much research has focused on machine learning methods to improve the efficacy of fraud detection in some financial domains. However, diverse information falsification in individual fraud remains one of the most challenging problems in loan applications. To this end, we conducted an empirical study to explore the relationships between various fraud types and analyzed the factors influencing information fabrication. Weak relationships exist among different falsification types, and some essential factors play the same roles in different fraud types. In contrast, others have various or opposing effects on these types of frauds. Based on this finding, we propose a novel hierarchical multi-task learning approach to refine fraud-detection systems. Specifically, we first developed a hierarchical fraud category method to break down this problem into several subtasks according to the information types falsified by customers, reducing fraud identification's difficulty. Second, a heterogeneous network with a meta-path-based random walk and heterogeneous skip-gram model can solve the representation learning problem owing to the sophisticated relationships among the applicants' information. Furthermore, the final subtasks can be predicted using a multi-task learning approach with two prediction layers. The first layer provides the probabilities of general fraud categories as auxiliary information for the second layer, which is for specific subtask prediction. Finally, we conducted extensive experiments based on a real-world dataset to demonstrate the effectiveness of the proposed approach.
Learning Pixel Affinity Pyramid for Arbitrary-Shaped Text Detection

Arbitrary-shaped text detection in natural images is a challenging task due to the complexity of the background and the diversity of text properties. The difficulty lies in two aspects: accurate separation of adjacent texts and sufficient text feature representation. To handle these problems, we consider text detection as instance segmentation and propose a novel text detection framework, which jointly learns semantic segmentation and a pixel affinity pyramid in a unified fully convolutional network. Specifically, the pixel affinity pyramid is proposed to encode multi-scale instance affiliation relationships of pixels, which is not only robust to varying shapes of text but also provides an accurate boundary description for separating closely located texts. In the inference phase, a simple but effective post-processing is presented to reconstruct text instances from the semantic segmentation results under the guidance of the learned pixel affinity pyramid, achieving good accuracy and efficiency. Furthermore, to enhance the representation of text features in the neural network, two modules — the Region Enhancement Module (REM) and Attentional Fusion Module (AFM) — are proposed. The REM models the semantic correlations of regional features to enhance the features from the text area, which effectively suppresses false-positive detection. The AFM adaptively fuses multi-scale textual information through an attention mechanism to obtain abundant text semantic features, which benefits multi-sized text detection. Extensive ablation experiments are conducted demonstrating the effectiveness of the REM and AFM. Evaluation results on standard benchmarks, including Total-Text, ICDAR2015, SCUT-CTW1500, and MSRA-TD500, show that our method surpasses most existing text detectors and achieves state-of-the-art performance, denoting its superior capability in detecting arbitrary-shaped texts.
Time-Optimal Trajectory Planning of Six-Axis Manipulators Based on the Improved Direct Collocation Method with FMU

The trajectory planning method with dynamics is the key to improving the motion performance of manipulators. The optimal control method (OCM) is a key technology to solve optimal problems with dynamics. There are direct and indirect methods in OCM; indirect methods are difficult to apply to engineering applications, and so direct methods are widely applied instead. The direct collocation method (DCM) is a technology in OCM to transform an optimal control problem (OCP) to a nonlinear problem (NLP), so that plenty of solvers can be used directly. However, the general DCM, for which it has been found that the explicit form of the right-hand-side (RHS) functions of state equations of the complex system in the OCP is hard to derive, is limited to solving the OCP of three-axis manipulators. This paper proposes an improved DCM to solve the OCP of six-axis manipulators, which can find the solution of the time-optimal trajectory for the motion of six-axis manipulators based on the improved DCM. The proposed method derives the RHS equations implicitly by introducing a Functional Mock-up Unit (FMU), which simplifies the representation of the RHS equations as a black-box model, so that the DCM can be applied to the OCP of six-axis manipulators. A simulation case of a three-axis manipulator accomplished in a related study works as a reference compared with our improved method to verify the solution consistence between the DCM using the explicit RHS equations or using the implicit RHS equations, and the loss of computational efficiency is acceptable. In the meantime, a simulation solution and an experiment of six-axis manipulators, which is a novel advancement, are presented to validate the proposed method.