Reviewerly/exHarmony huggingface dataset:
A Fast Image Template Matching Algorithm Based on Normalized Cross Correlation

Abstract Image matching algorithms based on image gray value are commonly used, which can achieve high matching accuracy. Among them, the Normalized Cross Correlation (NCC) method has high accuracy and strong adaptability, however it has the disadvantages of high computational complexity and slow calculation speed. By using dynamic programming strategy, a fast template matching algorithm based on NCC is proposed in this paper. At the same time, by calculating the cumulative sum of pixel values and the sum of squares of pixel values, and using the down sampling process, the calculation time and complexity are greatly reduced. Experimental results show that the proposed method has the same matching accuracy as the existing algorithms, and further improves the image matching efficiency.
Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources

In order to facilitate natural language understanding, the key is to engage commonsense or background knowledge.However, how to engage commonsense effectively in question answering systems is still under exploration in both research academia and industry.In this paper, we propose a novel question-answering method by integrating multiple knowledge sources, i.e.Con-ceptNet, Wikipedia, and the Cambridge Dictionary, to boost the performance.More concretely, we first introduce a novel graph-based iterative knowledge retrieval module, which iteratively retrieves concepts and entities related to the given question and its choices from multiple knowledge sources.Afterward, we use a pre-trained language model to encode the question, retrieved knowledge and choices, and propose an answer choice-aware attention mechanism to fuse all hidden representations of the previous modules.Finally, the linear classifier for specific tasks is used to predict the answer.Experimental results on the CommonsenseQA dataset show that our method significantly outperforms other competitive methods and achieves the new state-ofthe-art.In addition, further ablation studies demonstrate the effectiveness of our graph-based iterative knowledge retrieval module and the answer choice-aware attention module in retrieving and synthesizing background knowledge from multiple knowledge sources.
‘You should see a doctor’, said the robot: Reflections on a digital diagnostic device in a pandemic age

Aims: In three days at the beginning of the COVID-19 pandemic, the Copenhagen Emergency Medical Services developed a digital diagnostic device. The purpose was to assess and triage potential COVID-19 symptoms and to reduce the number of calls to public health-care helplines. The device was used almost 150,000 times in a few weeks and was described by politicians and administrators as a solution and success. However, high usage cannot serve as the sole criterion of success. What might be adequate criteria? And should digital triage for citizens by default be considered low risk? Methods: This paper reflects on the uncertain aspects of the performance, risks and issues of accountability pertaining to the digital diagnostic device in order to draw lessons for future improvements. The analysis is based on the principles of evidence-based medicine (EBM), the EU and US regulations of medical devices and the taxonomy of uncertainty in health care by Han et al. Results: Lessons for future digital devices are (a) the need for clear criteria of success, (b) the importance of awareness of other severe diseases when triaging, (c) the priority of designing the device to collect data for evaluation and (d) clear allocation of responsibilities. Conclusions: A device meant to substitute triage for citizens according to its own criteria of success should not by default be considered as low risk. In a pandemic age dependent on digitalisation, it is therefore important not to abandon the ethos of EBM, but instead to prepare the ground for new ways of building evidence of effect.
Knowledge graph embeddings for dealing with concept drift in machine learning

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. As data is evolving on a temporal basis, its underlying knowledge is subject to many challenges. Concept drift,1 as one of core challenge from the stream learning community, is described as changes of statistical properties of the data over time, causing most of machine learning models to be less accurate as changes over time are in unforeseen ways. This is particularly problematic as the evolution of data could derive to dramatic change in knowledge. We address this problem by studying the semantic representation of data streams in the Semantic Web, i.e., ontology streams. Such streams are ordered sequences of data annotated with ontological vocabulary. In particular we exploit three levels of knowledge encoded in ontology streams to deal with concept drifts: i) existence of novel knowledge gained from stream dynamics, ii) significance of knowledge change and evolution, and iii) (in)consistency of knowledge evolution. Such knowledge is encoded as knowledge graph embeddings through a combination of novel representations: entailment vectors, entailment weights, and a consistency vector. We illustrate our approach on classification tasks of supervised learning. Key contributions of the study include: (i) an effective knowledge graph embedding approach for stream ontologies, and (ii) a generic consistent prediction framework with integrated knowledge graph embeddings for dealing with concept drifts. The experiments have shown that our approach provides accurate predictions towards air quality in Beijing and bus delay in Dublin with real world ontology streams.
Phonologically-Meaningful Subunits for Deep Learning-Based Sign Language Recognition

The large majority of sign language recognition systems based on deep learning adopt a word model approach. Here we present a system that works with subunits, rather than word models. We propose a pipelined approach to deep learning that uses a factorisation algorithm to derive hand motion features, embedded within a low-rank trajectory space. Recurrent neural networks are then trained on these embedded features for subunit recognition, followed by a second-stage neural network for sign recognition. Our evaluation shows that our proposed solution compares well in accuracy against the state of the art, providing added benefits of better interpretability and phonologically-meaningful subunits that can operate across different signers and sign languages.
Advanced Outlier Detection Using Unsupervised Learning for Screening Potential Customer Returns

Due to the extreme scarcity of customer failure data, it is challenging to reliably screen out those rare defects within a high-dimensional input feature space formed by the relevant parametric test measurements. In this paper, we study several unsupervised learning techniques based on six industrial test datasets, and propose to train a more robust unsupervised learning model by self-labeling the training data via a set of transformations. Using the labeled data we train a multi-class classifier through supervised training. The goodness of the multiclass classification decisions with respect to an unseen input data is used as a normality score to defect anomalies. Furthermore, we propose to use reversible information lossless transformations to retain the data information and boost the performance and robustness of the proposed self-labeling approach.
Crypto-Assets: Implications for Financial Stability, Monetary Policy, and Payments and Market Infrastructures

This paper summarises the outcomes of the analysis of the ECB Crypto-Assets Task Force. First, it proposes a characterisation of crypto-assets in the absence of a common definition and as a basis for the consistent analysis of this phenomenon. Second, it analyses recent developments in the crypto-assets market and unfolding links with financial markets and the economy. Finally, it assesses the potential impact of crypto-assets on monetary policy, payments and market infrastructures, and financial stability. The analysis shows that, in the current market, crypto-assets’ risks or potential implications are limited and/or manageable on the basis of the existing regulatory and oversight frameworks. However, this assessment is subject to change and should not prevent the ECB from continuing to monitor crypto-assets, raise awareness and develop preparedness.
Human Activity Recognition Based on Parallel Approximation Kernel K-Means Algorithm

Recently, owing to the capability of mobile and wearable devices to sense daily human activity, human activity recognition (HAR) datasets have become a large-scale data resource. Due to the heterogeneity and nonlinearly separable nature of the data recorded by these sensors, the datasets generated require special techniques to accurately predict human activity and mitigate the considerable heterogeneity. Consequently, classic clustering algorithms do not work well with these data. Hence, kernelization, which converts the data into a new feature vector representation, is performed on nonlinearly separable data. This study aims to present a robust method to perform HAR data clustering to mitigate heterogeneity in data with minimal resource consumption. Therefore, we propose a parallel approximated clustering approach to handle the computational cost of big data by addressing noise, heterogeneity, and nonlinearity in data using data reduction, filtering, and approximated clustering methods on parallel computing environments that have not been previously addressed. Our key contribution is to treat HAR as big data implemented by approximation kernel K-means approaches and fill the gap between the HAR clustering cost and parallel computing fields. We implemented our approach on Google cloud on a parallel spark cluster, which helped us to process large-scale HAR data across multiple machines of clusters. The normalized mutual information is used as validation metric to assess the quality of the clustering algorithm. Additionally, the precision, recall, f-score metrics values are obtained somehow to compare the results with a classification technique. The experimental results of our clustering approach prove its effectiveness compared with a classification technique and can efficiently detect physical activity and mitigate the heterogeneity of the datasets.
Mathematical Reasoning via Self-supervised Skip-tree Training

We demonstrate that self-supervised language modeling applied to mathematical formulas enables logical reasoning. To measure the logical reasoning abilities of language models, we formulate several evaluation (downstream) tasks, such as inferring types, suggesting missing assumptions and completing equalities. For training language models for formal mathematics, we propose a novel skip-tree task. We find that models trained on the skip-tree task show surprisingly strong mathematical reasoning abilities, and outperform models trained on standard skip-sequence tasks. We also analyze the models' ability to formulate new conjectures by measuring how often the predictions are provable and useful in other proofs.
Hierarchical-Bayesian-Based Sparse Stochastic Configuration Networks for Construction of Prediction Intervals

To address the architecture complexity and ill-posed problems of neural networks when dealing with high-dimensional data, this article presents a Bayesian-learning-based sparse stochastic configuration network (SCN) (BSSCN). The BSSCN inherits the basic idea of training an SCN in the Bayesian framework but replaces the common Gaussian distribution with a Laplace one as the prior distribution of the output weights of SCN. Meanwhile, a lower bound of the Laplace sparse prior distribution using a two-level hierarchical prior is adopted based on which an approximate Gaussian posterior with sparse property is obtained. It leads to the facilitation of training the BSSCN, and the analytical solution for output weights of BSSCN can be obtained. Furthermore, the hyperparameter estimation process is derived by maximizing the corresponding lower bound of the marginal likelihood function based on the expectation-maximization algorithm. In addition, considering the uncertainties caused by both noises in the real-world data and model mismatch, a bootstrap ensemble strategy using BSSCN is designed to construct the prediction intervals (PIs) of the target variables. The experimental results on three benchmark data sets and two real-world high-dimensional data sets demonstrate the effectiveness of the proposed method in terms of both prediction accuracy and quality of the constructed PIs.
Embedding Human Knowledge into Deep Neural Network via Attention Map

In this work, we aim to realize a method for embedding human knowledge into deep neural networks. While the conventional method to embed human knowledge has been applied for non-deep machine learning, it is challenging to apply it for deep learning models due to the enormous number of model parameters. To tackle this problem, we focus on the attention mechanism of an attention branch network (ABN). In this paper, we propose a fine-tuning method that utilizes a single-channel attention map which is manually edited by a human expert. Our fine-tuning method can train a network so that the output attention map corresponds to the edited ones. As a result, the fine-tuned network can output an attention map that takes into account human knowledge. Experimental results with ImageNet, CUB-200-2010, and IDRiD demonstrate that it is possible to obtain a clear attention map for a visual explanation and improve the classification performance. Our findings can be a novel framework for optimizing networks through human intuitive editing via a visual interface and suggest new possibilities for human-machine cooperation in addition to the improvement of visual explanations.
A Hybridized Deep Learning Method for Bengali Image Captioning

An omnipresent challenging research topic in computer vision is the generation of captions from an input image.Previously, numerous experiments have been conducted on image captioning in English but the generation of the caption from the image in Bengali is still sparse and in need of more refining.Only a few papers till now have worked on image captioning in Bengali.Hence, we proffer a standard strategy for Bengali image caption generation on two different sizes of the Flickr8k dataset and BanglaLekha dataset which is the only publicly available Bengali dataset for image captioning.Afterward, the Bengali captions of our model were compared with Bengali captions generated by other researchers using different architectures.Additionally, we employed a hybrid approach based on InceptionResnetV2 or Xception as Convolution Neural Network and Bidirectional Long Short-Term Memory or Bidirectional Gated Recurrent Unit on two Bengali datasets.Furthermore, a different combination of word embedding was also adapted.Lastly, the performance was evaluated using Bilingual Evaluation Understudy and proved that the proposed model indeed performed better for the Bengali dataset consisting of 4000 images and the BanglaLekha dataset.
Implementation of Industry 4.0 Using E-learning and M-learning Approaches in Technically-Oriented Education

Within the states of the European Union, the traditional system of education in the concept of student-teacher-department dominates in the 21st century. Although technically oriented teaching is transferred to the digital world a bit more demanding compared to teaching in the humanities, there is a period of significant modernization and digitization in the field of education of technical subjects using a wide range of Learning management system approaches. This article is focused on the implementation of industry 4.0 using E-learning and M-learning approaches in technically-oriented education. The introductory part provides an overview of the issues of the Learning Management System by the concept of Industry 4.0. Based on research studies, the next part of the article provides a brief overview of the most well-known LMS systems with a subsequent description of the most suitable system. The main part of the article describes the implementation of Elearning and M-learning approaches on a specific example from the educational practice implemented at the Faculty of Manufacturing Technologies with a seat in Prešov.
Assessment of high school teachers on their digital competences

&#x0D; &#x0D; &#x0D; &#x0D; Digital Technologies’ potential has brought new challenges to teachers, making it essential to acquire digital competences that will allow them to effectively use those technologies. The aim of this research is to assess the Portuguese high school teachers’ digital competence level. The quantitative methodology used emphasises the teachers’ perception of their digital competences in three focal dimensions: teachers’ professional and pedagogic competences and learners’ competences. The findings show that teachers’ digital competence level is moderate; the dimensions with the lowest values are “teachers’ pedagogic competences” and “learners’ competences”. Subdimensions Assessment, Empowering Learners and Facilitating Learners’ Digital Competence are the weakest.&#x0D; &#x0D; &#x0D; &#x0D;
CNN based Optical Character Recognition and Applications

The procedure of translating images of handwritten, typewritten, or typed text into a format recognized by computers is called Optical Character Recognition (OCR). Editing, indexing, searching, and storage space reduction are the uses of Optical Character Recognition. This is done by scanning the picture of the text character-by-character first, then processing the scanned image and eventually converting the image of the character into character codes, such as ASCII. To translate text in an image into text format, the Optical Character Recognition system is used. There are three key aspects of OCR approach: pre-processing, character recognition, character segmentation and presentation of data. Convolutional Neural Network is a deep learning method which is used for character recognition. In this paper, CNN layers, architecture and implementation of CNN architecture are discussed. Here the CNN (VGG-16) model is trained over Telugu character data set which covers maximum of 1600 characters and its accuracy is measured.
PMT-Net: Progressive Multi-Task Network for one-shot Person Re-Identification

Most existing person Re-Identification (Re-ID) algorithms require abundant labeled data from paired non-overlapping camera views in the fully supervised scenario. However, the fully supervised Re-ID suffers from the limited availability of labeled training samples due to the sharply increased cost of manual efforts. To tackle this problem, a novel Progressive Multi-Task Network (PMT-Net) for person Re-ID is proposed. PMT-Net initializes a model using only one labeled sample for each identity, and it iteratively optimizes the model by sampling the most reliable pseudo labels dynamically from unlabeled samples. Firstly, pedestrian attributes recognition is incorporated as an auxiliary task to learn discriminative features. Then, based on the discriminative features, the identity label for unlabeled samples is estimated by the distance between the labeled samples and unlabeled samples in feature space. In addition, to enhance the accuracy of label estimation for the unlabeled samples, a semi-supervised clustering method, named Distance Ranked Weight Clustering (DRW-Clustering) is designed. The clustering method weights partial unlabeled samples by the indexed ordinal of distance sorting, so that it can find the real cluster center quickly and effectively. Extensive comparative evaluation experiments are conducted on Market1501 and DukeMTMC-reID datasets, and the experimental results indicate that the proposed method achieves performance competitive or better than that of the state-of-the-art for one-shot person Re-ID.
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction

Current state-of-the-art systems for joint entity relation extraction (Luan et al., 2019;Wadden et al., 2019) usually adopt the multi-task learning framework.However, annotations for these additional tasks such as coreference resolution and event extraction are always equally hard (or even harder) to obtain.In this work, we propose a pre-training method ENPAR to improve the joint extraction performance.EN-PAR requires only the additional entity annotations that are much easier to collect.Unlike most existing works that only consider incorporating entity information into the sentence encoder, we further utilize the entity pair information.Specifically, we devise four novel objectives, i.e., masked entity typing, masked entity prediction, adversarial context discrimination, and permutation prediction, to pretrain an entity encoder and an entity pair encoder.Comprehensive experiments show that the proposed pre-training method achieves significant improvement over BERT on ACE05, SciERC, and NYT, and outperforms current state-of-the-art on ACE05.
Cross-Graph Attention Enhanced Multi-Modal Correlation Learning for Fine-Grained Image-Text Retrieval

Fine-grained Image-text retrieval is challenging but vital technology in the field of multimedia analysis. Existing methods mainly focus on learning the common embedding space of images (or patches) and sentences (or words), whereby their mapping features in such embedding space can be directly measured. Nevertheless, most existing image-text retrieval works rarely consider the shared semantic concepts that potentially correlated the heterogeneous modalities, which can enhance the discriminative power of learning such embedding space. Toward this end, we propose a Cross-Graph Attention model (CGAM) to explicitly learn the shared semantic concepts, which can be well utilized to guide the feature learning process of each modality and promote the common embedding learning. More specifically, we build semantic-embedded graph for each modality, and smooth the discrepancy between two modalities via cross-graph attention model to obtain shared semantic-enhanced features. Meanwhile, we reconstruct image and text features via the shared semantic concepts and original embedding representations, and leverage multi-head mechanism for similarity calculation. Accordingly, the semantic-enhanced cross-modal embedding between image and text is discriminatively obtained to benefit the fine-grained retrieval with high retrieval performance. Extensive experiments evaluated on benchmark datasets show the performance improvements in comparison with state-of-the-arts.