Reviewerly/exHarmony huggingface dataset:
PRISMA2020: R package and ShinyApp for producing PRISMA 2020 compliant flow diagrams

You can use this package to produce a flow diagram that conforms to the PRISMA 2020 preprint standards using the PRISMA_flowdiagram() function. The data can be manually entered into the function, or loaded up using the template CSV file provided in 'INST/EXTDATA/'. The function, (if 'interactive = TRUE') produces an interactive HTML ouput with each box linking to a specific page (e.g. of search results or methods details), and hover-over tooltips for further information. The 'Previous' and 'Other' study arms of the flowchart can be toggled on and off and removed or added to the diagram by specifying this in the function inputs. A static version is produced otherwise.
Extending C++ for Heterogeneous Quantum-Classical Computing

We present qcor—a language extension to C++ and compiler implementation that enables heterogeneous quantum-classical programming, compilation, and execution in a single-source context. Our work provides a first-of-its-kind C++ compiler enabling high-level quantum kernel (function) expression in a quantum-language agnostic manner, as well as a hardware-agnostic, retargetable compiler workflow targeting a number of physical and virtual quantum computing backends. qcor leverages novel Clang plugin interfaces and builds upon the XACC system-level quantum programming framework to provide a state-of-the-art integration mechanism for quantum-classical compilation that leverages the best from the community at-large. qcor translates quantum kernels ultimately to the XACC intermediate representation, and provides user-extensible hooks for quantum compilation routines like circuit optimization, analysis, and placement. This work details the overall architecture and compiler workflow for qcor, and provides a number of illuminating programming examples demonstrating its utility for near-term variational tasks, quantum algorithm expression, and feed-forward error correction schemes.
Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?

Online shopping, already on a steady rise, was propelled even further with the advent of the COVID-19 pandemic. Of course, credit cards are a dominant way of doing business online. The credit card fraud detection problem has become relevant more than ever as the losses due to fraud accumulate. Most research on this topic takes an isolated, focused view of the problem, typically concentrating on tuning the data mining models. We noticed a significant gap between the academic research findings and the rightfully conservative businesses, which are careful when adopting new, especially black-box, models. In this paper, we took a broader perspective and considered this problem from both the academic and the business angle: we detected challenges in the fraud detection problem such as feature engineering and unbalanced datasets and distinguished between more and less lucrative areas to invest in when upgrading fraud detection systems. Our findings are based on the real-world data of CNP (card not present) fraud transactions, which are a dominant type of fraud transactions. Data were provided by our industrial partner, an international card-processing company. We tested different data mining models and approaches to the outlined challenges and compared them to their existing production systems to trace a cost-effective fraud detection system upgrade path.
Configurable Multi-directional Systolic Array Architecture for Convolutional Neural Networks

The systolic array architecture is one of the most popular choices for convolutional neural network hardware accelerators. The biggest advantage of the systolic array architecture is its simple and efficient design principle. Without complicated control and dataflow, hardware accelerators with the systolic array can calculate traditional convolution very efficiently. However, this advantage also brings new challenges to the systolic array. When computing special types of convolution, such as the small-scale convolution or depthwise convolution, the processing element (PE) utilization rate of the array decreases sharply. The main reason is that the simple architecture design limits the flexibility of the systolic array. In this article, we design a configurable multi-directional systolic array (CMSA) to address these issues. First, we added a data path to the systolic array. It allows users to split the systolic array through configuration to speed up the calculation of small-scale convolution. Second, we redesigned the PE unit so that the array has multiple data transmission modes and dataflow strategies. This allows users to switch the dataflow of the PE array to speed up the calculation of depthwise convolution. In addition, unlike other works, we only make a few changes and modifications to the existing systolic array architecture. It avoids additional hardware overheads and can be easily deployed in application scenarios that require small systolic arrays such as mobile terminals. Based on our evaluation, CMSA can increase the PE utilization rate by up to 1.6 times compared to the typical systolic array when running the last layers of ResNet-18. When running depthwise convolution in MobileNet, CMSA can increase the utilization rate by up to 14.8 times. At the same time, CMSA and the traditional systolic arrays are similar in area and energy consumption.
Unsupervised Estimation of Monocular Depth and VO in Dynamic Environments via Hybrid Masks

Deep learning-based methods mymargin have achieved remarkable performance in 3-D sensing since they perceive environments in a biologically inspired manner. Nevertheless, the existing approaches trained by monocular sequences are still prone to fail in dynamic environments. In this work, we mitigate the negative influence of dynamic environments on the joint estimation of depth and visual odometry (VO) through hybrid masks. Since both the VO estimation and view reconstruction process in the joint estimation framework is vulnerable to dynamic environments, we propose the cover mask and the filter mask to alleviate the adverse effects, respectively. As the depth and VO estimation are tightly coupled during training, the improved VO estimation promotes depth estimation as well. Besides, a depth-pose consistency loss is proposed to overcome the scale inconsistency between different training samples of monocular sequences. Experimental results show that both our depth prediction and globally consistent VO estimation are state of the art when evaluated on the KITTI benchmark. We evaluate our depth prediction model on the Make3D dataset to prove the transferability of our method as well.
Dataset construction challenges for digital forensics

As the digital forensic field develops, taking steps towards ensuring a level of reliability in the processes implemented by its practitioners, emphasis on the need for effective testing has increased. In order to test, test datasets are required, but creating these is not a straightforward task. A poorly constructed and documented test dataset undermines any testing which has taken place using it, eroding the reliability of any subsequent test results. In essence, given the time, effort and knowledge required to generate datasets, the field must guide those carrying out this task to ensure that it is done right at the first instance without wasting resources. Yet, there are currently few standards and best practices defined for dataset creation in digital forensics. This work defines three categories of dataset which typically exist in digital forensic - tool/process evaluation datasets, actions datasets and scenario-based datasets, where the minimum requirements for their creation are outlined and discussed to support those creating them and to help ensure that where datasets are created, they offer maximum value to the field.
Application of intelligent edge computing technology for video surveillance in human movement recognition and Taekwondo training

With the explosive growth of global data volume, the centralized cloud computing computing model cannot provide low-latency, high-efficiency video surveillance services. Therefore, a distributed computing model is proposed, which directly processes peripheral video data to reduce transmission pressure, the burden of the central cloud server and the processing delay of the video surveillance system. Combined with the federated learning algorithm, a light neural network model is proposed. The light neural network is used to generate calculation models in different scenarios, and the generated models are reasonably arranged in edge devices. With the continuous development of technology, human motion analysis has become a hot research field in recent years, and human motion recognition is developed on the basis of human motion analysis. With the development of society, the level of Chinese Taekwondo has improved compared to before. Therefore, Chinese Taekwondo classes have gradually developed, but behind this phenomenon there is a problem of uneven teaching quality. In order to help solve the teaching problems in the learning process of Taekwondo and further improve the level of Chinese Taekwondo, the author carried out specific research in conjunction with D.A. Cooper's empirical learning theory. Future work will continue to study federated learning algorithms, optimize general neural network weight update methods, further improve detection results, and introduce neural network compression acceleration algorithms to make the system meet real-time requirements. This research uses scientific and technological means as a guide to study technical actions and strategies, and apply these strategies to specific teaching experiments for testing.
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning

Training deep neural networks for automatic speech recognition (ASR) requires large amounts of transcribed speech. This becomes a bottleneck for training robust models for accented speech which typically contains high variability in pronunciation and other semantics, since obtaining large amounts of annotated accented data is both tedious and costly. Often, we only have access to large amounts of unannotated speech from different accents. In this work, we leverage this unannotated data to provide semantic regularization to an ASR model that has been trained only on one accent, to improve its performance for multiple accents. We propose Accent Pre-Training (Acc-PT), a semi-supervised training strategy that combines transfer learning and adversarial training. Our approach improves the performance of a state-of-the-art ASR model by 33% on average over the baseline across multiple accents, training only on annotated samples from one standard accent, and as little as 105 minutes of unannotated speech from a target accent.
Unsupervised feature learning-based encoder and adversarial networks

Abstract In this paper, we propose a novel deep learning-based feature learning architecture for object classification. Conventionally, deep learning methods are trained with supervised learning for object classification. But, this would require large amount of training data. Currently there are increasing trends to employ unsupervised learning for deep learning. By doing so, dependency on the availability of large training data could be reduced. One implementation of unsupervised deep learning is for feature learning where the network is designed to “learn” features automatically from data to obtain good representation that then could be used for classification. Autoencoder and generative adversarial networks (GAN) are examples of unsupervised deep learning methods. For GAN however, the trajectories of feature learning may go to unpredicted directions due to random initialization, making it unsuitable for feature learning. To overcome this, a hybrid of encoder and deep convolutional generative adversarial network (DCGAN) architectures, a variant of GAN, are proposed. Encoder is put on top of the Generator networks of GAN to avoid random initialisation. We called our method as EGAN. The output of EGAN is used as features for two deep convolutional neural networks (DCNNs): AlexNet and DenseNet. We evaluate the proposed methods on three types of dataset and the results indicate that better performances are achieved by our proposed method compared to using autoencoder and GAN.
VisualWordGrid: Information Extraction from Scanned Documents Using a Multimodal Approach

We introduce a novel approach for scanned document representation to perform field extraction. It allows the simultaneous encoding of the textual, visual and layout information in a 3-axis tensor used as an input to a segmentation model. We improve the recent Chargrid and Wordgrid [] models in several ways, first by taking into account the visual modality, then by boosting its robustness in regards to small datasets while keeping the inference time low. Our approach is tested on public and private document-image datasets, showing higher performances compared to the recent state-of-the-art methods.
Triple-Memory Networks: A Brain-Inspired Method for Continual Learning

Continual acquisition of novel experience without interfering with previously learned knowledge, i.e., continual learning, is critical for artificial neural networks, while limited by catastrophic forgetting. A neural network adjusts its parameters when learning a new task but then fails to conduct the old tasks well. By contrast, the biological brain can effectively address catastrophic forgetting through consolidating memories as more specific or more generalized forms to complement each other, which is achieved in the interplay of the hippocampus and neocortex, mediated by the prefrontal cortex. Inspired by such a brain strategy, we propose a novel approach named triple-memory networks (TMNs) for continual learning. TMNs model the interplay of the three brain regions as a triple-network architecture of generative adversarial networks (GANs). The input information is encoded as specific representations of data distributions in a generator, or generalized knowledge of solving tasks in a discriminator and a classifier, with implementing appropriate brain-inspired algorithms to alleviate catastrophic forgetting in each module. Particularly, the generator replays generated data of the learned tasks to the discriminator and the classifier, both of which are implemented with a weight consolidation regularizer to complement the lost information in the generation process. TMNs achieve the state-of-the-art performance of generative memory replay on a variety of class-incremental learning benchmarks on MNIST, SVHN, CIFAR-10, and ImageNet-50.
A convolutional neural network model based reversible data hiding scheme in encrypted images with block-wise Arnold transform

The research in the domain of reversible data hiding (RDH) is recently explored in all aspects due to its applications in cloud computing, forensics, and medical image communication. In this manuscript, we introduce a RDH scheme in encrypted images which can provide a high embedding rate without compromising the bit error rate during the message extraction and image recovery. The proposed scheme follows a block-wise data hiding process. If the processed block size is A×A pixels, and if A=2n+1, then the data hider can hide any number from the set {0, 1, …, (A+2n−1)} in that block. We have introduced an Arnold transform-based data hiding process in which each block will be undergoing a series of scrambling processes based on the bit sequence that we need to embed in the selected block. The message extraction and image restoration are carried out at the receiver side using a trained convolutional neural network (CNN) model.
SGA-Net: Self-Constructing Graph Attention Neural Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images is always a critical and challenging task. Graph neural networks, which can capture global contextual representations, can exploit long-range pixel dependency, thereby improving semantic segmentation performance. In this paper, a novel self-constructing graph attention neural network is proposed for such a purpose. Firstly, ResNet50 was employed as backbone of a feature extraction network to acquire feature maps of remote sensing images. Secondly, pixel-wise dependency graphs were constructed from the feature maps of images, and a graph attention network is designed to extract the correlations of pixels of the remote sensing images. Thirdly, the channel linear attention mechanism obtained the channel dependency of images, further improving the prediction of semantic segmentation. Lastly, we conducted comprehensive experiments and found that the proposed model consistently outperformed state-of-the-art methods on two widely used remote sensing image datasets.
Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification

With the advancements in processing units and easy availability of cloud-based GPU servers, many deep learning-based methods have been proposed for Aspect Level Sentiment Classification (ALSC) literature. With this increase in the number of deep learning methods proposed in ALSC literature, it has become difficult to ascertain the performance difference of one method over the other. To this end, our study provides a statistical comparison of the performance of 35 recent deep learning methods with respect to three performance metrics-Accuracy, Macro F1 score, and Time. The methods are evaluated for eight benchmark datasets. In this study, the statistical comparison is based on Friedman, Nemenyi, and Wilcoxon tests. As per the results of statistical tests, the top-ranking methods could not significantly outperform several other methods in terms of Accuracy and Macro F1 score and performed poorly on-time metric. However, the time taken by any method is crucial to analyze the overall performance. Thus, this study aids the selection of the Deep Learning method, which maximizes the accuracy and Macro F1 score and takes minimal time. Our study also establishes a framework for validating the performance of new and alternate methods in ALSC that can be helpful for researchers and practitioners working in this area.
The Construction of a Digital Resource Library of English for Higher Education Based on a Cloud Platform

This paper conducts in-depth research and analysis on the construction of an English digital resource library for higher education through the cloud platform. The cloud platform allows the English resource library to achieve integration, efficiency, scalability, and interactivity and then solves the problems of how to realize the co-construction and sharing of resources among multiple platforms in vocational institutions and improve the utilization rate of resources. The construction of the English resource library in higher vocational institutions cannot be separated from the collaboration between institutions and enterprises, and institutions must face up to the problems existing in the construction of the current resource library. Firstly, the requirements of various aspects of the higher vocational English digital resource library system are given from three aspects, functional requirements, performance requirements, and operational requirements, in strict accordance with the requirement analysis steps; secondly, the overall technical architecture and network topology architecture of the education cloud platform where the higher vocational English digital resource library system is located are analyzed and given in general to ensure the security, scalability, and ease of operation of this platform and then the resource collection and resource storage. The application of cloud computing technology can solve the problems of difficulty in obtaining teaching resources, insufficient storage space, limited communication, and slow circulation; it can realize the integration and integrated management of various resources through technologies such as networking, virtualization, and distributed storage. Finally, based on the optimization strategies proposed in this paper, the core modules of the higher vocational English digital resource library system are designed and implemented, and some important and representative interfaces and source codes are demonstrated. To ensure the operation performance, the situation of multiple users operating the higher-level English digital resource library system at the same time is simulated, and the overall performance of the higher-level English digital resource library system is evaluated for the common operations such as uploading resource files, downloading resource files, and querying resource files, which confirms the rationality of the design strategy in this paper and greatly improves the user experience and increases the utilization rate of teaching resources.
The Massive Implementation of ICT in Universities and Its Implications for Ensuring SDG 4: Challenges and Difficulties for Professors

Education has undergone the greatest revolution since the 19th century. The pandemic has massively launched teaching towards the use of ICTs and online education. The main objective of this research is to study the relationship between the adaptation of university faculty to the massive use of ICTs and educational digitization, and the promotion of SDG 4, which establishes Target 4.3, ensuring access to quality university education. To achieve this, a descriptive study methodology is used, in which the results of a validated survey, on a sample of 245 teachers from 32 public and private Spanish universities, were analyzed. The main results consist of the different emotional reactions among professors, finding that, on the one hand, ICTs allow for the development of new skills, the introduction of new methodological proposals, and the development of a positive attitude towards the use of digital tools in order to reach all of their students. However, on the other hand, the results also show that the use of ICTs caused an increase in anxiety, workloads, and the need for ongoing training. There is a need to continue improving the digital competencies of faculty professors in order to favor access to ICTs by university students and, thus, promote quality education worldwide.
Weakly Supervised Framework for Cancer Region Detection of Hepatocellular Carcinoma in Whole-Slide Pathologic Images Based on Multiscale Attention Convolutional Neural Network

Visual inspection of hepatocellular carcinoma cancer regions by experienced pathologists in whole-slide images (WSIs) is a challenging, labor-intensive, and time-consuming task because of the large scale and high resolution of WSIs. Therefore, a weakly supervised framework based on a multiscale attention convolutional neural network (MSAN-CNN) was introduced into this process. Herein, patch-based images with image-level normal/tumor annotation (rather than images with pixel-level annotation) were fed into a classification neural network. To further improve the performances of cancer region detection, multiscale attention was introduced into the classification neural network. A total of 100 cases were obtained from The Cancer Genome Atlas and divided into 70 training and 30 testing data sets that were fed into the MSAN-CNN framework. The experimental results showed that this framework significantly outperforms the single-scale detection method according to the area under the curve and accuracy, sensitivity, and specificity metrics. When compared with the diagnoses made by three pathologists, MSAN-CNN performed better than a junior- and an intermediate-level pathologist, and slightly worse than a senior pathologist. Furthermore, MSAN-CNN provided a very fast detection time compared with the pathologists. Therefore, a weakly supervised framework based on MSAN-CNN has great potential to assist pathologists in the fast and accurate detection of cancer regions of hepatocellular carcinoma on WSIs.