Reviewerly/exHarmony huggingface dataset:
From FAIR Leading Practices to FAIR Implementation and Back: An Inclusive Approach to FAIR at Leiden University Libraries

Leiden University (LU) adopted a data management regulation in 2016. The regulation embraces the Findable, Accessible, Interoperable and Reusable (FAIR) principles. To implement the regulation a programme was established. The focus of the programme was initially to raise awareness and to set up services to make data Findable and Accessible and to train researchers on data management planning. In 2019, the programme entered its second phase, with an increased focus on Interoperable and Reusable data, and on implementing the machine-actionable aspects of FAIR data. This step is non-trivial, however, because of the fast-developing FAIR research data international research field that requires fast adoption of leading practices by support professionals with the adequate skills. In this paper we describe how LU aims to close the feedback loop between international bottom-up organisations such as GOFAIR, the Research Data Alliance and CODATA on the one hand and university staff engaged in developing and implementing emerging FAIR leading practices on the other. During processes such as these, it is of crucial importance to focus primarily on the needs of researchers. We describe how LU builds up its support for FAIR data before, during and after research through its involvement in leading practices, training and consultancy and end with recommendations for other universities wanting to implement the FAIR principles.
Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems

Incorporating knowledge bases (KB) into endto-end task-oriented dialogue systems is challenging, since it requires to properly represent the entity of KB, which is associated with its KB context and dialogue context.The existing works represent the entity with only perceiving a part of its KB context, which can lead to the less effective representation due to the information loss, and adversely favor KB reasoning and response generation.To tackle this issue, we explore to fully contextualize the entity representation by dynamically perceiving all the relevant entities and dialogue history.To achieve this, we propose a COntextaware Memory Enhanced Transformer framework (COMET), which treats the KB as a sequence and leverages a novel Memory Mask to enforce the entity to only focus on its relevant entities and dialogue history, while avoiding the distraction from the irrelevant entities.Through extensive experiments, we show that our COMET framework can achieve superior performance over the state of the arts.
A New Blockchain Digital MRV (Measurement, Reporting, and Verification) Architecture for Existing Building Energy Performance

The widespread and massive effects of climate are now inevitable, and action must be taken by all sectors to mitigate their contributions to its impacts. The building sector accounts for about 40% of global energy consumption and 30% of GHG emissions. This sector must reduce its energy consumption by at least 50% by shifting to hyper-efficient systems and renewable energy to meet the climate change mitigation goal. Certified green buildings are responsible for about 40% of the office markets in the U. S. and utilize the types of strategies that should be implemented by all new construction and major renovations. Certified green buildings, although highly effective in reducing greenhouse gas emissions, are generally considered to be an expensive solution to this problem. There is excellent potential for the construction sector to employ a blockchain framework of measurement, report, and verification (MRV) towards building energy performance (BEP) to enable certified green buildings to earn carbon credits based on their exceptional energy performance. Blockchain technology eliminates the need for intermediaries to validate the data and builds up a reliable, immutable, traceable energy monitoring system. The goal of this paper is to present a new blockchain digital MRV architecture for existing BEP and a prototype of its application.
The Availability of the 21<sup>st</sup> Century Skills in the Sport and Health for All Curriculum of the Undergraduate University Students in Al-Balqaa Applied University, Jordan

The current study aimed at exploring the availability of the 21 st century skills in the sport and health for all curriculum designed and introduced for the undergraduate university students in Al-Balqaa University, Jordan.The study adopted the descriptive analytical design.The study analyzed the objectives, activities and the assessment questions of the sport and health for all curriculums through using a researcher-developed checklist that included indicators for three major skills: the learning and innovations skills, information and technology skills, and life and work skills.The results of the study revealed that learning and innovation skills (59.73%) are available to a moderate degree, the life and work skills (38.38%) are available to a low degree, whereas the information and technology skills are available to a very low degree (1.87%).The results of the current study revealed that the mean score for the availability of the 21 st century skills in the sport and health for all curriculum designed for the undergraduate students in Al-Balqaa university was 33.33%, which is a low degree of availability.The study concluded that there was a significant shortage of including the 21 st century skills in the sport and health for all curriculums designed and introduced for the undergraduate university students in Al-Balqaa University, Jordan.The study recommended performing a thorough revision of the curriculum content and improving the content through integrating more of the 21 st century skills in order to enable the students cope with the challenges of the 21 st century.
New voting functions for neural network algorithms

Neural Network and Convolutional Neural Network algorithms are among the best performing machine learning algorithms.However, the performance of the algorithms may vary between multiple runs because of the stochastic nature of these algorithms.This stochastic behavior can result in weaker accuracy for a single run, and in many cases, it is hard to tell whether we should repeat the learning giving a chance to have a better result.Among the useful techniques to solve this problem, we can use the committee machine and the ensemble methods, which in many cases give better than average or even better than the best individual result.We defined new voting function variants for ensemble learner committee machine algorithms which can be used as competitors of the well-known voting functions.Some belong to the locally weighted average voting functions, others are meta voting functions calculated from the output of the previous voting functions functions called with the results of the individual learners.The performance evaluation of these methods was done from numerous learning sessions.
Smart Cities and Smart Devices: The Back Door to Privacy and Data Breaches

Christos Beretas* Author Affiliations PhD Candidate (full scholarship) in Cyber Security at Innovative Knowledge Institute, France Received: May 21, 2020 | Published: June 01, 2020 Corresponding author: Christos Beretas, PhD Candidate (full scholarship) in Cyber Security at Innovative Knowledge Institute, France DOI: 10.26717/BJSTR.2020.28.004588
Intrapersonal dependencies in multimodal behavior

Human interlocutors automatically adapt verbal and non-verbal signals so that different behaviors become synchronized over time. Multimodal communication comes naturally to humans, while this is not the case for Embodied Conversational Agents (ECAs). Knowing which behavioral channels synchronize within and across speakers and how they align seems critical in the development of ECAs. Yet, there exists little data-driven research that provides guidelines for the synchronization of different channels within an interlocutor. This study focuses on intrapersonal dependencies of multimodal behavior by using cross-recurrence analysis on a multimodal communication dataset to better understand the temporal relationships between language and gestural behavior channels. By shedding light on the intrapersonal synchronization of communicative channels in humans, we provide an initial manual for modality synchronisation in ECAs.
Siamese X-Vector Reconstruction for Domain Adapted Speaker Recognition

With the rise of voice-activated applications, the need for speaker recognition is rapidly increasing. The x-vector, an embedding approach based on a deep neural network (DNN), is considered the state-of-the-art when proper end-to-end training is not feasible. However, the accuracy significantly decreases when recording conditions (noise, sample rate, etc.) are mismatched, either between the x-vector training data and the target data or between enrollment and test data. We introduce the Siamese x-vector Reconstruction (SVR) for domain adaptation. We reconstruct the embedding of a higher quality signal from a lower quality counterpart using a lean auxiliary Siamese DNN. We evaluate our method on several mismatch scenarios and demonstrate significant improvement over the baseline.
Folding Clothes Tool Using Arduino Uno Microcontroller And Gear Servo

Folding clothes is one of the homework that is often done for housewives, from a survey conducted on 200 housewives in Pekanbaru, the problem is that many housewives choose to fold family clothes by hand, this takes time and the energy to do the job. Based on these problems, the authors designed a microcontroller with a servo motor as a practical medium in folding the clothes applied to the clothes folding tool, where the author uses the Hardware Programming Methodology in order to carry out this research smoothly, the materials needed in this research are Arduino Uno, Gear. Servo, Power Supply, and software components. The result of this research is the creation of an automatic clothes folding device that is useful for simplifying household chores. From the test results, it can be concluded that this tool can fold clothes with a duration of 2 seconds.
Suppression method for strong interference from stray light in 3D imaging system of structured light

It is critical to accurately obtain a phase position in a digital fringe projection system. Due to stray light in a complex environment, when projecting an image of a code fringe, the projected light intensity can be influenced. As a result, the intensity of fringe light captured with the camera may be defective, resulting in the decline of measurement precision. Hence, we propose a strong suppression technology for stray light based on polarization. Through processing fringe images in the frequency domain, this technology can effectively suppress interference of stray light on the three-dimensional (3D) imaging system. This method is verified in the experiment. The results show that the method can accurately capture the 3D profile of real-world targets under the interference of stray light.
Praktik Pembelajaran Keterampilan Berbicara Di Masa Pandemi Covid-19

This study aims to describe the practice of language learning, namely the speaking ability of students during the pandemic period with a learning system that dares to follow what they face. The subjects in this study were 13 students of PGMI Insuri Ponorogo. The practice of learning Speaking skills is carried out with four speaking practices: reading stories, reading announcements, making speeches, and discussing. Of the four practices, knew that if you do not practice speaking, namely reading stories, reading announcements, and making speeches, it only folds four aspects of delivery, namely (1) vocal clarity; (2) correct intonation; (3) accuracy of word choice (diction); and (4) sentence structure (speech). There is no interaction with other speakers, no need to convey or express opinions, and not required to show data outside of the text to support their speaking skills. In contrast to the ability to discuss which requires six aspects, namely (1) fluency in delivering responses/responses; (2) vocal clarity; (3) correct intonation; (4) accuracy of word choice (diction); (5) sentence structure (speech); and (6) accuracy in disclosing data in practice. In the discussion practice, the students' ability to speak is also better without reading texts. However, it cannot involve one aspect of learning services from voice message media.. be involved, namely eye contact with listeners. One aspect of this also later becomes one of the pathways related to bold learning that only uses voice message media in learning. Not recognizing expressions and gestures when executing the practice also prevents listeners from entering into reports.
Automatic image segmentation for material microstructure characterization by optical microscopy

This work shows the utility to have a microstructure characterization to analysis the properties of materials. For this, digital image segmentation is used on microscopic images of materials to extract the number of phases and their proportion present in the material to obtain a quantitative description of material properties and to better control product quality. In this way, we present here an automated method for segmenting the phases present in microscopic scanning images of metallographic samples using a multiphase level set with Mumford Shah formulation. Experience shows that the proposed model successfully detects phase regions for a variety of real micrographic images and provides the required accuracy and robustness to the process..
Model-Finding for Externally Verifying FOL Ontologies: A Study of Spatial Ontologies

Use and reuse of an ontology requires prior ontology verification which encompasses, at least, proving that the ontology is internally consistent and consistent with representative datasets. First-order logic (FOL) model finders are among the only available tools to aid us in this undertaking, but proving consistency of FOL ontologies is theoretically intractable while also rarely succeeding in practice, with FOL model finders scaling even worse than FOL theorem provers. This issue is further exacerbated when verifying FOL ontologies against datasets, which requires constructing models with larger domain sizes. This paper presents a first systematic study of the general feasibility of SAT-based model finding with FOL ontologies. We use select spatial ontologies and carefully controlled synthetic datasets to identify key measures that determine the size and difficulty of the resulting SAT problems. We experimentally show that these measures are closely correlated with the runtimes of Vampire and Paradox, two state-of-the-art model finders. We propose a definition elimination technique and demonstrate that it can be a highly effective measure for reducing the problem size and improving the runtime and scalability of model finding.
A Note on the Nonparametric Estimation of the Conditional Mode by Wavelet Methods

The purpose of this note is to introduce and investigate the nonparametric estimation of the conditional mode using wavelet methods. We propose a new linear wavelet estimator for this problem. The estimator is constructed by combining a specific ratio technique and an established wavelet estimation method. We obtain rates of almost sure convergence over compact subsets of Rd. A general estimator beyond the wavelet methodology is also proposed, discussing adaptivity within this statistical framework.
Measuring Feature Importance of Convolutional Neural Networks

Convolutional neural networks have demonstrated powerful abilities to achieve state-of-the-art results in many classification tasks, while the interpretability and reliability of these complicated models are also a non-negligible problem. Understanding how these networks arrive at their final decisions becomes more and more indispensable, so this article puts forward an interpretive method to obtain feature importance, which indicates to what extent an input feature can discriminate different classes. The proposed method utilizes the attribution maps of multiple-class predictions and can decomposes the feature importance into individual and co-variation effects. Some properties of the method are justified theoretically. Furthermore, a visualization method is proposed to sketch the silhouette of the target object. And to improve computational efficiency, some practical tricks are applied. For the purpose of evaluating this method, some comparative experiments are performed; the results testify that the proposed method can identify important features and improve the visualization effects.
A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

Cyberbullying is a social problem in which bullies' actions are more harmful than in traditional forms of bullying as they have the power to repeatedly humiliate the victim in front of an entire community through social media.Nowadays, multiple works aim at detecting acts of cyberbullying via the analysis of texts in social media publications written in one or more languages; however, few investigations target the cyberbullying detection in the Spanish language.In this work, we aim to compare four traditional supervised machine learning methods performances in detecting cyberbullying via the identification of four cyberbullying-related categories on Twitter posts written in the Peruvian Spanish language.Specifically, we trained and tested the Naive Bayes, Multinomial Logistic Regression, Support Vector Machines, and Random Forest classifiers upon a manually annotated dataset with the help of human participants.The results indicate that the best performing classifier for the cyberbullying detection task was the Support Vector Machine classifier.
Multilevel Identification Friend or Foe of Objects and Analysis of the Applicability of Post-Quantum Cryptographic Algorithms for Information Security

Introduction. Widespread use of unmanned aerial vehicles in the civilian and military spheres requires the development of new algorithms for identification friend or foe of targets, as used in the Armed Forces of Ukraine (AFU) devices of the "Parol" system are designed to service approximately 110 objects military equipment. AFU automation systems allow the use of additional sources of information about various objects from civil or special data transmission networks, which can be the basis for building a networked multi-level system of state recognition. Predictions of the development of quantum computers foresee the possibility of breaking modern algorithms for information security in polynomial time in the next 5-10 years, which requires the development and implementation of new encryption algorithms and revision of modern parameters. The purpose of the article is to develop a new algorithm for state recognition of objects, which can be scaled to process the required number of manned and unmanned aerial vehicles. Potential threats to classical cryptographic protection algorithms for data networks, which will result in the execution of algorithms such as Grover and Shore on quantum computers, were also discussed. Results. The article proposes a new multilevel algorithm of state recognition based on modern cryptographic methods of information protection, which allows to perform reliable automated identification of objects, scale systems using data on potential targets from other sources through secure special networks. Grover's search algorithm does not give a strong increase in key search performance for symmetric encryption algorithms, so there is no need to increase the key lengths for this type of information security algorithms. Post-quantum asymmetric encryption algorithms require additional study and comprehensive testing of information security or increasing the key lengths of cryptographic algorithms, which corresponds to the number of qubits, i.e. more than twice. The most promising is the family of asymmetric post-quantum cryptographic algorithms based on supersingular isogenic elliptic curves. Conclusions. The developed algorithm of identification friend or foe of objects is more secure compared to existing algorithms and is focused on the use of modern on-board computers and programmable radio modems. Shore's algorithm and the like will be a significant threat to modern asymmetric cryptography algorithms when the number of qubits of quantum computers exceeds the number of bits in public keys more than twice. Keywords: identification friend or foe, symmetric encryption, asymmetric cryptography, quantum computer, post-quantum cryptography.
Сapitalist Paradigm Of Contemporary Art As An Ontological Problem

Contemporary art has become autonomous in relation to the viewer and his taste preferences, that is, in relation to the public taste."Elite artist" offers, sometimes even imposes on the viewer what he has.Or are the curators of the so-called "world art" allowing him to impose it?... To deal with this issue, the authors of the study take a historical and philosophical excursion into the essence of the issue.It is shown that in the conditions of the capitalist paradigm of art, carriers of traditional, artistic-figurative art do not have the ability to fully integrate into the system of the so-called "world art".To convey sincerity of feelings in art, it is necessary to selflessly serve beauty.The embodiment of this sincerity from a potential opportunity into a practically realized reality is only possible with a soul-open and sensitive to the world around, and its changes in the personality of the artist.However, in the capitalist paradigm of art at the present stage of its development, there is no place for the value-semantic strata of human existence, the spiritual and spiritual phenomena of his being.Since contemporary art is often based on principled, consciously cultivated tastelessness, absurdity, and formlessness, the complex unity of the artistic, social, and humanistic functions of art, which are its most important ontological links, is being destroyed.Due to the elimination of the essential, aesthetic function of art and the expulsion of the image from the picture, the ontological problem of the latter is exposed.
DATA SCIENCE AND DEEP LEARNING APPLICATIONS IN THE E-COMMERCE INDUSTRY: A SURVEY

The E-Commerce industry is a new way of shopping.Most manufacturers prefer to sell their products on E-commerce websites because of low maintenance and high returns.This paper highlights the application of data science and deep learning methodologies to drive the E-commerce business.The first part of the paper explains the role of data science to identify potential customers, items recommendations and fraud detections.Different stages of building E-commerce websites and the role of proper handling of data is then highlighted.The second part of the paper is about the use of deep learning approaches like Recurrent neural networks in analysing textual information is presented with a demonstration of three architectures.At last, the importance of convolutional neural networks in the E-commerce industry is presented.Fashion related images like dresses, shoes and bags are classified using two separate convolution neural network architectures.