Reviewerly/exHarmony huggingface dataset:
Breakdancing Movement Based on Image Recognition Promotes Preschool Children’s Executive Function and Intervention Plan

With the continuous development of science and technology, people can apply more and more technology to the cultivation of children's abilities. In the process of cultivating children's ability, the most fancy is the study of executive function, and this is the research topic of this article. In the past, training methods such as music, mindfulness, and exercise have been used in the study of children's executive abilities to promote the development of preschool children's executive functions. While various approaches have had some effect, researchers have been exploring more comprehensive approaches to effective training. This article is aimed at studying how to use image recognition technology to conduct an intervention analysis of breakdancing in promoting the executive function of preschool children. For this reason, this paper proposes image recognition technology based on deep learning neural network and conducts research, analysis, and improvement on related technologies obtained from deep learning. This makes it more suitable for the research topic of this article and design-related experiments and analysis to explore its related performance. The experimental results in this paper show that the improved image recognition technology has improved accuracy by 31.2%. And the performance of its algorithm is also improved by 21%, which can be very effective in monitoring preschool children during breakdancing.
University Campus: Interrelations of Educational Technologies and Models of Architectural Space Formation

This article studies the main cultural and social processes influencing the formation of modern educational space. We analyze the traditional models of the educational cycle and propose a new one. The latter, unifying research, education and knowledge realization activities, reflects the interaction of education, research, experimental production and information processing as architectural space functions. There is revealed the system of trends affecting the architecture of scientific and educational facilities when forming high-quality spatial solutions of university campus facilities. The structure of the university complexes’ «intellectual capital» is supplemented by spatial, infrastructural capital and architectural image of the complex. A theoretical model of the functional and spatial structure represents the scientific and educational center as a modern technological architectural and spatial unit of campus. The author also gives some examples on forming scientific and educational complexes at various levels of designing architectural and spatial objects based on a modern model of a new educational cycle. This paper might be of use for specialists in the fields of education and architecture, as well as for scientific and educational facilities designers.
Discriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition

• To the best of our knowledge, we are the first to address the issue of weakly supervised low-resolution fine-grained image recognition in an end-to-end manner. By enhancing the network’s perception of discriminative features, the necessary critical details are recovered for fine-grained recognition, so as to improve the performance of weakly supervised low-resolution fine-grained image recognition. • We propose a minimum spanning tree aggregation module to aggregate context information for each pixel by utilizing the structural characteristic of minimum spanning tree, which can help the fine-grained discriminative information restoration sub-network keep a watchful eye on discriminative fine-grained details. • We introduce a semantic relation distillation loss to help the recognition sub-network calibrate the relationship between features, which further prompts the fine-grained detail restoration sub-network to generate the unambiguous details of super-resolution images and recognition sub-network to be aware of discriminative features. • Extensive experiments are carried out on four challenging datasets (CUB-200-2011, Stanford Cars, FGVC-Aircraft and RP-281) to demonstrate the effectiveness of our framework. The existing methods of fine-grained image recognition mainly devote to learning subtle yet discriminative features from the high-resolution input. However, their performance deteriorates significantly when they are used for low quality images because a lot of discriminative details of images are missing. We propose a discriminative information restoration and extraction network, termed as DRE-Net, to address the problem of low-resolution fine-grained image recognition, which has widespread application potential, such as shelf auditing and surveillance scenarios. DRE-Net is the first framework for weakly supervised low-resolution fine-grained image recognition and consists of two sub-networks: (1) fine-grained discriminative information restoration sub-network (FDR) and (2) recognition sub-network with the semantic relation distillation loss (SRD-loss). The first module utilizes the structural characteristic of minimum spanning tree (MST) to establish context information for each pixel by employing the spatial structures between each pixel and other pixels, which can help FDR focus on and restore the critical texture details. The second module employs the SRD-loss to calibrate recognition sub-network by transferring the correct relationships between every two pixels on the feature map. Meanwhile the SRD-loss can further prompt the FDR to recover reliable and accurate fine-grained details and guide the recognition sub-network to perceive the discriminative features from the correct relationships. Extensive experiments on three benchmark datasets and one retail product dataset demonstrate the effectiveness of our proposed framework.
A Patient-Centric Interoperable, Quorum-based Healthcare System for Sharing Clinical Data

In the current Electronic Medical Record (EMR) systems, the healthcare organizations have the ownership of patient&#x0027;s EMR. Patients have only limited information of the EMR in the form of discharge summary and reports. This was viewed a problem in eHealth consultations during a pandemic like COVID-19, when doctors does not have access to patient data. Patient is the owner of the data and patient should have control over his medical data and should be able to share the data according to his requirement. So currently work is being undertaken to develop patient-centric EMR system. One major challenge here is to ensure the privacy and access management of data being accessed and shared. This paper aims to solve these challenges by using a permissioned blockchain network based FHIR solution for secure interoperability. The proposed system was evaluated by developing a prototype on Quorum Blockchain. The throughput and latency characteristics of the system was analyzed with different workloads and results was promising.
A novel zero-watermarking scheme based on NSCT-SVD and blockchain for video copyright

Abstract With the advent of information era, the issues on video copyright continue to emerge. In the grey industrial chain of piracy, the film industry is suffering a major disaster area. The current protection schemes for video copyright are inefficient and costly so that video industry still has to face the infringement. To address poor robustness, weak imperceptibility and difficulty in traceability of the current protection schemes for video copyright, this paper proposes a novel zero-watermarking scheme based on NSCT-SVD and blockchain for video copyright. In addition, we research the feasibility and reason of combining zero-watermarking with blockchain. The designed zero-watermarking algorithm is based on NSCT-SVD, Arnold Transform and key-frames extraction algorithm which is based on distance threshold clustering. The results show the extracted features from video are stable and secure than DWT-SVD based and the past non-blocking. The performance of the blockchain scheme designed for video copyright practices well. Comparatively, the discussion and results showed that the proposed scheme has been verified more credible and efficient in authoritative traceability compared to trusted third party based. The registration information cannot be tampered and be more suitable for copyright protection.
Protection of User’s Data in IOT

There’s no denying that the market for Internet of Things (IoT) personal gadgets is exploding as new items like smartwatches, contact lenses, fitness bands, and even microchips beneath the skin hit the market. Because of the recent expansion of ICT, there have been several changes for economic and technological development. Unfortunately, they also unlocked Pandora’s door of cyberattacks, allowing criminals to steal users’ data and identities, as well as monitor their professional and personal life. In addition, as IoT connects a wide range of devices for a two-way flow of information, it poses a significant risk of misuse by third parties since it gathers and saves vast amounts of personal data. Data sharing, on the other hand, raises concerns about data protection since the combination of several data points might lead to the prediction of previously unknown private details. An overall framework for dealing with the problem of privacy protection against unintentional exposure of personal managers to govern and enhance user data exchange and inference avoidance measures are also part of the framework. For the framework to be useful, it must use both research areas in a user-centric way that gives users more control over their data and educates them on the implications of data sharing choices.
Promoting Ethics And Morality In Education For Equality, Diversity And Inclusivity

Ethics is properly defined as the discipline concerned with what is good and wrong in terms of moral duty and obligation. Ethics education is necessary in the curriculum to help students deal with ethical dilemmas in professional practice and life. The ethical codes and student’s perceptions of these codes have an impact on how they perceive, maintain and act in accordance with the ethical standards set at their workplace or school. Moral values are a set of valuable principles that assist us in becoming better human beings. These values include a variety of aspects such as morality, respect for others, helping others, loving others and so on. Social, aesthetic, cognitive, spiritual, economic, religious, health, political, environmental and cultural values are among the several forms of moral values. Information about these values enables individuals to make a meaningful contribution to the community and nation's well-being, as well as to develop into moral and ethical human beings. Human values and human rights, professional integrity, respect and equality, privacy, building of trusting relationships, creating and participating in a culture of positive cooperation, respecting the competence of other professions, taking initiative, promoting an open culture and displaying loyalty towards goals and objectives are some of the factors that lead to the development of professional ethics. Moral standards can be improved through education, especially among students. Education, in my opinion, is the most effective means of instilling moral ideals in individuals. Student’s emotional intelligence will be influenced by their moral ideals. As a result, moral values and ethics education is critical in our schooling. Moral and ethical values education will result in a society that is more egalitarian, diverse and inclusive.
Enhancing the Security of Medical Images in Telemedicine Using Region-based Crypto-Watermarking Approach

Along with maintaining the confidentiality and privacy of patient information, the preservation and authentication of internal physiological features of tissues and organs present in any type of medical images is equally important while transmitting medical images in Telemedicine. To achieve it, this paper presents a region-based approach for Medical Image by integrating cryptography in the digital watermarking. To preserve the internal physiological features, medical image is separated into two regions &#x2013; Region of Interest (ROI) and Not-a-Region of Interest (NROI). ROI contains the important internal physiological features of human body. Therefore, no information is embedded in ROI to preserve it. Cryptography is integrated only for generation and exchange of the secret key of ROI using Elliptic curve cryptography. Patient information and secret key is embedded into NROI using wavelet-based hybrid watermarking technique. The proposed work is tested on varieties of medical image dataset of MRI, CT scan and X-ray against various intentional and unintentional attacks and evaluated with performance metrics PSNR (Peak signal to Noise Ratio), SSIM (Structural Similarity Index), NC (Normalized Correlation) and BER (Bit Error Ratio). Secret key is matched at receiving end to authenticate the contents. The proposed approach not only enhances authenticity of medical images but also preserves the internal physiological features along with maintaining the confidentiality of patient information.
Some Translation Studies informed suggestions for further balancing methodologies for machine translation quality evaluation

Abstract This article intends to contribute to the current debate on the quality of neural machine translation (NMT) vs. (professional) human translation quality, where recently claims concerning (super)human performance of NMT systems have emerged. The article will critically analyse some current machine translation (MT) quality evaluation methodologies employed in studies claiming such performance of their MT systems. This analysis aims to identify areas where these methodologies are potentially biased in favour of MT and hence may overvalue MT performance while undervaluing human translation performance. Then, the article provides some Translation Studies informed suggestions for improving or debiasing these methodologies in order to arrive at a more balanced picture of MT vs. (professional) human translation quality.
Visualization of medical concepts represented using word embeddings: a scoping review

Analyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language processing (NLP), to learn dense and low-dimensional word representations from large unlabeled corpora that capture the implicit semantics of words. Models like Word2Vec, GloVe or FastText have been broadly applied and reviewed in the bioinformatics and healthcare fields, most often to embed clinical notes or activity and diagnostic codes. Visualization of the learned embeddings has been used in a subset of these works, whether for exploratory or evaluation purposes. However, visualization practices tend to be heterogeneous, and lack overall guidelines.This scoping review aims to describe the methods and strategies used to visualize medical concepts represented using word embedding methods. We aim to understand the objectives of the visualizations and their limits.This scoping review summarizes different methods used to visualize word embeddings in healthcare. We followed the methodology proposed by Arksey and O'Malley (Int J Soc Res Methodol 8:19-32, 2005) and by Levac et al. (Implement Sci 5:69, 2010) to better analyze the data and provide a synthesis of the literature on the matter.We first obtained 471 unique articles from a search conducted in PubMed, MedRxiv and arXiv databases. 30 of these were effectively reviewed, based on our inclusion and exclusion criteria. 23 articles were excluded in the full review stage, resulting in the analysis of 7 papers that fully correspond to our inclusion criteria. Included papers pursued a variety of objectives and used distinct methods to evaluate their embeddings and to visualize them. Visualization also served heterogeneous purposes, being alternatively used as a way to explore the embeddings, to evaluate them or to merely illustrate properties otherwise formally assessed.Visualization helps to explore embedding results (further dimensionality reduction, synthetic representation). However, it does not exhaust the information conveyed by the embeddings nor constitute a self-sustaining evaluation method of their pertinence.
Classification and intuitionistic fuzzy set based software bug triaging techniques

Software development is a modular approach involving multiple developers and multi-tasking teams working at different locations. A particular term in a software bug can belong to multiple modules and multiple developers’ profiles. Also, many people who report software bugs are unfamiliar with the exact technical terminology of software development, which causes the software bug to be unlabeled, vague, and noisy. Hence, analyzing, understanding, and assigning the newly reported bugs to the most appropriate developer is a challenging task for the triager. Intuitionistic Fuzzy Sets (IFS) consider the non-membership and hesitant values along with the membership values of the software bug terms mapped to the developers and thus provide a powerful tool for better analysis in cases where the same term can belong to multiple categories. Two IFS similarity measure-based techniques, namely, the Intuitionistic Fuzzy Similarity Model for Developer Term Relation (IFSDTR) and the Intuitionistic Fuzzy Similarity Model for Developer Category Relation (IFSDCR), are proposed in this work. In IFSDTR, a developer-term vocabulary is constructed based on the previous bug-fixing experience of software developers by considering the most frequent terms in the IFS representation of bugs they fixed earlier. In IFSDCR, software bugs are categorized into multiple categories and a developer-category relation is constructed. When a new bug is reported, the IFS similarity measure is calculated with the developer-term and developer-category relationship, and a fuzzy α-cut is applied to find a group of expert developers to fix it. The proposed techniques are evaluated on the available data set and compared with existing approaches to bug triaging. On the Eclipse, Mozilla, and NetBeans data sets, the IFSDTR techniques yield an accuracy of 0.90, 0.89, and 0.87, respectively, whereas the IFSDCR yields a greater accuracy of 0.93, 0.90, and 0.88 for the Eclipse, Mozilla, and NetBeans data sets, respectively. Similarly, in all other performance measures, the proposed approaches outperform the state-of-the-art approaches.
Neural logic rule layers

Deep neural networks (DNN) are mainly black boxes, generally suffering from bad interpretability of their behavior and the results obtained. Hence, a human can not easily derive the relations modeled by the network. A reasonable way to provide interpretability for humans are logical rules. In this paper we propose neural logic rule layers (NLRL), which are able to represent arbitrary logic rules in terms of their conjunctive and disjunctive normal forms. Stacking various layers, we are theoretically able to represent arbitrary complex rules by the resulting neural network architecture. The NLRL are end-to-end trainable allowing to learn logic rules directly on the given data without needing any background information about the origin. We show in experiments, that NLRL-enhanced neural networks can model arithmetic and logical operations over the input values. Furthermore, we apply NLRL to image classification tasks and show that interpretability is provided without sacrificing classification performance by exchanging the fully-connected head of the network. We also apply NLRL to a real world industrial control problem where the task is to model the discrete control behaviour of a programmable logic controller (PLC), following a basic step sequence.
AILS: A budget-constrained adaptive iterated local search for workflow scheduling in cloud environment

With the rapid development of cloud computing, scheduling the complex scientific workflow on the cloud becomes an extraordinarily challenging problem. Especially, the workflow scheduling in cloud environment often need to meet several constraints such as budget. To reduce the execution time subject to a specific budget, this paper proposes a novel adaptive iterated local search framework (AILS). In the proposed AILS, a greedy resource provisioning scheme and HEFT are combined to generate the initial solution. Based on a budget-aware diversification strategy and level-based order perturbation strategy, a perturbation mechanism is applied to perform exploration. In addition, a novel intensification strategy is utilized to execute exploitation. Furthermore, an adaptive penalty function is employed to guide the search near the bound of feasible solution space. Based on the components of AILS, the Markov chain is employed to analyze the convergence of AILS. A comprehensive comparison is constructed to evaluate the proposed AILS, the simulating results indicate that the average relative percentage deviation of AILS is 3.6% lower than that of GRP-HEFT. Compared with the meta-heuristics, the AILS is effective and competitive for the considered problem. • An AILS framework was proposed for budget constrained cloud workflow scheduling problem. • A perturbation mechanism was designed to control the resource pool and task permutation. • The intensification strategy was employed to assign the tasks to the leased resources. • The Markov chain was employed to analyze the convergence of AILS. • The well-known scientific workflows were utilized to evaluate the performance of AILS.
Enhancing drug–drug interaction prediction by three-way decision and knowledge graph embedding

Drug–Drug interaction (DDI) prediction is essential in pharmaceutical research and clinical application. Existing computational methods mainly extract data from multiple resources and treat it as binary classification. However, this cannot unambiguously tell the boundary between positive and negative samples owing to the incompleteness and uncertainty of derived data. A granular computing method called three-way decision is proved to be effective in making uncertain decision, but it relies on supplementary information to make delay decision. Recently, biomedical knowledge graph has been regarded as an important source to obtain abundant supplementary information about drugs. This paper proposes a three-way decision-based method called 3WDDI, in combination with knowledge graph embedding as supplementary features to enhance DDI prediction. The drug pairs are divided into positive, negative and boundary regions by Convolutional Neural Network (CNN) according to drug chemical structure feature. Further, delay decision is made for objects in the boundary region by integrating knowledge graph embedding feature to promote the accuracy of decision-making. The empirical results show that 3WDDI yields up to 0.8922, 0.9614, 0.9582, 0.8930 for Accuracy, AUPR, AUC and F1-score, respectively, and outperforms several baseline models.
Text CAPTCHA Traversal with ConvNets: Impact of Color Channels

Most of the existing studies have investigated fixed-length CAPTCHA recognition. In this paper the authors propose applying convolutional neural networks trained with a special loss function to recognize dynamic character sequences. The paper studies the influence of individual color channels and their linear combination on the models' final quality. Each color channel importance was estimated using trained weighting coefficients in linear combination of color pixel values. The results obtained allowed us to reduce the resource requirements for model training without loss of recognition quality, and at the same time to accelerate training. Model error analysis allowed us to make proposals for improving CAPTCHA designs and ways to counteract automatic recognition are outlined.