Reviewerly/exHarmony huggingface dataset:
Lessons Learned from Pandemics in the Context of Digital Transformation of Education

2020 brought the world huge challenges in almost every field when the new COVID-19 virus began to spread rapidly. Digitalization made it possible to use innovative solutions to ensure access to various services, to continue working, and to learn from a distance, but it made the situation different from the one we all were familiar with and raised questions on how to ensure qualitative education for all. The situation where the whole world had to move education to the digital environment at the same time posed many challenges for teachers, students, and parents. The aim of the present research was to find out how the COVID-19 crisis contributed to the digital transformation of education. Several complementary research methods were used to obtain results, one of which was used to analyze the functionality and degree of interactivity of learning platforms, providing an educational perspective to this research. The second method was focus group discussions with experts in technology-enhanced learning to gather ideas for overcoming the crisis and to outline future directions for the digital transformation of education in the context of remote learning. The third method was a survey of teachers who work with students of compulsory education. This material provides an overview of methods used by the WP6 working group “Education System Transformation: Consequences of the COVID-19 Crisis and Possible Solutions” (VPP-COVID-2020/1-0013), part of the National Research Program project.
Improving food shopping behaviour: A model-based review of mobile applications to assist with healthy and sustainable grocery shopping

Awareness of health and sustainability is increasing and can be supported by suitable smartphone applications. Most applications are designed based on commercial or ad-hoc considerations, although models for improving behavioural patterns and motivation have been studied in psychology and the social sciences. These models have so far not been widely applied to application design. Grocery shopping is one example where behaviour-supporting applications are being widely used. This paper aims at reviewing existing applications in this domain and determine how these support motivational aspects identified by current behavioural models. Existing behavioural theories are merged into a single redundancy free and comprehensive model. The model is used to categorise application features to assess the type and strength of support with respect to motivating healthy and sustainable grocery shopping behaviour. Results: The most popular Android applications in this area are evaluated, identifying potential strengths and weaknesses with respect to optimally supporting desired behaviour. The most popular applications do not support desired behaviour best. By indicating which aspects of the behavioural model are supported by a mobile application it is possible to identify features not included that could be supplemented or improved in future application development.
An Acceleration Strategy for Randomize-Then-Optimize Sampling Via Deep Neural Networks

Randomize-then-optimize (RTO) is widely used for sampling from posterior distributions in Bayesian inverse problems.However, RTO can be computationally intensive for complexity problems due to repetitive evaluations of the expensive forward model and its gradient.In this work, we present a novel goal-oriented deep neural networks (DNN) surrogate approach to substantially reduce the computation burden of RTO.In particular, we propose to drawn the training points for the DNN-surrogate from a local approximated posterior distribution -yielding a flexible and efficient sampling algorithm that converges to the direct RTO approach.We present a Bayesian inverse problem governed by elliptic PDEs to demonstrate the computational accuracy and efficiency of our DNN-RTO approach, which shows that DNN-RTO can significantly outperform the traditional RTO.
Deep Learning Based Handwritten Signature Recognition

A handwritten signature commonly practiced route for confirming the authenticity of legal documents. The verification of the signature is critical as it varies every time and may change with age, behavior, and environment. This paper presents a Deep learning model based on the CNN architecture to verify the signature. For experimental purpose, the feature extraction portion of the GoogleNet model has been used to transfer value calculation and the classification layer was retrained using back propagation with the concept of transfer learning. The classification layer of the Deep learning model was retrained with 25 classes of signature image dataset with each class consisting of 85 signatures. After training, the model was evaluated with a testing dataset of 15 signatures from each class. The mean testing precision of the neural network architecture with signature dataset was found to be 95.2 %.
Optical Design and Optimization with Genetic Algorithm for High-Resolution Optics Applied to Underwater Remote-Sensing

In fields such as biology, archeology, and industry, underwater photogrammetry can be achieved using consumer-grade equipment. However, camera operations underwater differ considerably from those on land because underwater photogrammetry involves different optical phenomena. On the basis of the requirements and specifications of the marine vessel Polaris, we developed a novel underwater camera with prime and zoom lenses and a high resolving power. The camera can be used in the spectrum in shallow water and the blue–green spectrum in deep water. In the past, ordinary cameras would be placed in waterproof airtight boxes for underwater photography. These cameras were not optimized to the underwater spectrum and environment, resulting in no breakthroughs in resolving power. Furthermore, the use of the blue spectrum greatly increases during underwater and particularly deep-water surveying. Chromatic aberration and focus-point displacement generated by the shift from the shallow-water spectrum to the blue–green spectrum in deep water makes universal underwater photography even more difficult. Our proposed optical design aimed to overcome such challenges for the development of a high-resolution underwater surveying camera. We designed a prime lens and a zoom lens. We adopted a waterproof dome window on the outer surface as the basic structure and optimized it in accordance with the conditions of different water depths and spectra to obtain distortion within ±2% and high-resolution underwater imaging quality. For the zoom lens design, we employed a genetic algorithm in Zemax to attenuate chromatic aberration as a kind of extended optimization. This novel optical design that can be used in all waters is expected to greatly reduce the volume and weight of conventional underwater cameras by more than 50% and 60%, respectively, and increase their resolving power by 30–40%.
Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment

In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).
Welcome to the First International Workshop on Requirements Engineering for Explainable Systems (RE4ES)

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Exploring Reciprocal Teaching Method on EFL Learners’ Reading Comprehension

This research was carried out as the learning activities in the reading classroom were not yet effective. As a result, the student's ability to comprehend reading texts was poor. This study aims to improve the EFL students’ reading comprehension by implementing the Reciprocal Teaching Method (RTM), especially in comprehending IELTS reading texts. Classroom action research was conducted by following four stages; planning, implementing, observing, and reflecting the actions. The Reciprocal Teaching Method (RTM) consists of four stages; predicting, questioning, clarifying, and summarizing to engage the learners in groupwork activities, boost motivation, as well as enhance their comprehension. 28 learners from the English department who enrolled in the Reading course were selected as participants. Data were gathered through observations, tests, interviews, as well as document reviews. The results revealed that the Reciprocal Teaching Method (RTM) boosts students' ability to comprehend the IELTS reading texts and engages them during the teaching and learning process.
Global Research Decentralized Autonomous Organization (GR-DAO): A DAO of Global Researchers

The latest trend in Blockchain formation is to utilize decentralized autonomous organizations (DAO) in many verticals. To date, little attention has been given to address the global research domain due to the difficulty in creating a comprehensive framework that can marry the cutting edge of academic grade scientific research with a decentralized governance body of researchers. A global research decentralized autonomous organization (GR-DAO) would have a profound impact on the research community academically, commercially, and the public good. In this paper, we propose the GR-DAO as a global community of researchers committed to collectively creating knowledge and sharing it with the world. Scientific research is the means for knowledge creation and learning. The GR-DAO provides the guidance, community and technological solutions for the evolution of a global research infrastructure and environment. Through its design, the GR-DAO embraces, enhances and extends the model of research, research on decentralization and DAO as a model for decentralised and autonomous organizing. This design, in turn, improves most of the uses for and applications of research for the greater good of society. The paper examines the core motivation, purpose and design of the GR-DAO, its strategy to embrace, enhance and extend the research ecosystem, and the GR-DAO design uses across the DAO ecosystem
Students’ Readiness for STEM Learning within the Context of National Education Reform

STEM (science, technology, engineering, and mathematics) education nowadays becomes more and more topical; however, students’ performance in these subjects is rather low and only a small part of them decide to study these sciences therefore it is important to rouse students’ interest in these subjects already at school. It is important to acquire not only the knowledge of the subject but also the transversal skills, thus, the organization of the teaching/learning process becomes more significant. Schools of Latvia start implementing the teaching/learning content and approach that correspond to the new standards of basic and general secondary education, which incorporates four innovative aspects in the science domain: the promotion of the content acquisition through teachers’ reciprocal collaboration; the use of ICT (Information Communication Technologies) as a platform for engineering technological solutions; learning through doing and engagement in discussions and other activities for making socially responsible decisions. The aim of the study is to find out students’ readiness to learn STEM in the context of innovative approaches of the national education reform. To reach the aim, the research question was set – to what extent and in which way are students ready to learn STEM? Using the QuestionPro e-platform, 257 students of Latvian general comprehensive schools were surveyed and the meta-analysis of factors of thematically respective selected questions was performed. The obtained empirical results were compared with the four aspects of the innovative approach pertaining to the education reform in the science domain. The study resulted in isolating main four factors: the course of the teaching/learning process, the feedback, the use of ICT and technologies little used in the acquisition STEM. The found factors comprised all the innovations of the science domain put forward by the national education reform, which means that students already at the pre-reform stage are ready to acquire STEM subjects in an innovative way.
What do mobile AR game players complain about?: A qualitative analysis of mobile AR game reviews

Augmented Reality (AR) combines real and virtual objects, provides opportunities for real-time interaction and provides accurate registration of 3D virtual and real objects.Mobile AR creates unique opportunities for gameplay unrestricted by the screen size of mobile devices with interactions possible between players, game objects and the real world.Since the launch of Pokémon Go in 2016, AR gaming has gone mainstream, several commercial mobile AR games have been launched, and researchers have conducted several studies on the motivations, intentions, and experiences associated with playing AR games.However, most studies in this area have focused on Pokémon Go, and have not included issues reported by players.This paper presents a study conducted with the aim of understanding issues, specifically those associated with the use of AR in games, that mobile AR game players face.User reviews for 10 popular commercial mobile AR games that utilise AR in all significant gameplay activities were gathered and analysed using Thematic Analysis to identify 11 themes of issues.This study adds to the body of knowledge and understanding of issues facing mobile AR game players and includes commercial mobile AR games other than Pokémon Go in the research on AR games.
Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks

One of the central aspects of science is systematic problem-solving. Therefore, problem and solution statements are an integral component of the scientific discourse. The scientific analysis would be more successful if the problem–solution claims in scientific texts were automatically classified. It would help in knowledge mining, idea generation, and information classification from scientific texts. It would also help to compare scientific papers and automatically generate review articles in a given field. However, computational research on problem–solution patterns has been scarce. The linguistic analysis, instructional-design research, theory, and empirical methods have not paid enough attention to the study of problem–solution patterns. This paper tries to solve this issue by applying the computational techniques of machine learning classifiers and neural networks to a set of features to intelligently classify a problem phrase from a non-problem phrase and a solution phrase from a non-solution phrase. Our analysis shows that deep learning networks outperform machine learning classifiers. Our best model was able to classify a problem phrase from a non-problem phrase with an accuracy of 90.0% and a solution phrase from a non-solution phrase with an accuracy of 86.0%.
Blockchain Traceability to Ensure the Veracity of Diplomas

Higher education institutions are considered one of the most important pillars and the greatest starting points from which the wheels of development and civilized advancement are launched, as well as their important role in instilling the values of society and preserving its moral and value system.In performing the role entrusted to it in achieving sustainable development and the comprehensive renaissance of society in various field and sectors.Thus; the accreditation systems are frequently used to verify which institutions are recognized and authorized to giveeducational or professional skills.However, these systems are not always efficient in countries where recognized higher education establishments cannot rally the demand for certified professionals required by the employment market.This generates a fertile argument for the "certificate factories" to sell false diplomas to unskilled people who are trying to catchbenefit of this deficit.In this regard, the digitization of diploma granting's and verification's processes, is becomingincreasingly important in order to guarantee the identity of diplomas, and that companies recruit the right qualified people.For that reason, an efficient management system for the control of diploma creation processes is immediatelymandatory.The Blockchain methodology provides efficient ways to examine the data information management systems.It is designed to make confidence techniques that can revolutionize information management methods.The major purpose of this paper is to develop a "BlockDipls" system based on Blockchain technology.This BlockDipls system is planned to support diploma traceability and smart contract functions, and can be used to address the problems of diploma falsification and diploma record fraud.
Olivine composition and reflectance spectroscopy relationship revisited from advanced MGM deconvolution based on synthetic and natural samples

The behavior of the three primary olivine absorptions near 1 μm (referred to as M1-1, M2, M1-2) is revisited by means of an advanced version of MGM analysis. The (M1-1, M2, M1-2) band center positions are used simultaneously to produce a constrained prediction across an extended range Fo#[0–90] of olivine solid solution composition. The established trend lines based on synthetic samples from the Suny and Bristol suites appear robust when implemented on natural samples, and represent an improvement over previous solutions. The methodology, based on a wavelength deconvolution along the entire visible and near-infrared range (540-2600 nm), and the trend lines derived from the current solution, perform well, even when considering the complex situation of chromite-bearing lunar olivines. Furthermore, through the use of separate and quadratic estimates to the trends, an assessment is offered to qualify the Fo# determination made on unknown samples and/or remote sensing observations.
Integrated Document-based Electronic Health Records Persistence Framework

Electronic health record systems work beyond just recording patients` health data.They have multiple secondary functionalities, such as data reporting and clinical decision support.As each of these systems` workloads has contradictory different needs, managing a multipurpose electronic health record is a challenge.This paper proposes a unified healthcare data framework that can simplify health information system infrastructure.It investigates the suitability of the documentbased NoSQL persistence mechanism, storing electronic health records data as a design choice for managing varied complexity ad hoc queries used in operational business intelligence.The performance of the most popular two document-based NoSQL back-ends, Couchbase Server and MongoDB, is compared according to the size of the database and query execution time.Results showed that while MongoDB can execute simple singledocument queries nearly in milliseconds.It does not provide satisfactory response time for unplanned complex queries spanning multiple documents.By utilizing its analytics services and multi-dimensional scaling architecture, Couchbase Server multi-node cluster outperforms the response times of MongoDB for both simple and complex healthcare data access patterns.The primary advantage of the proposed tightly coupled EHRs processing framework is its flexibility to manage workload according to changing requirements.
PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation

We introduce a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is 2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15.We conduct experiments comparing strong neural baselines and well-known automatic translation engines on our dataset and find that in both automatic and human evaluations: the best performance is obtained by fine-tuning the pretrained sequence-to-sequence denoising autoencoder mBART.To our best knowledge, this is the first large-scale Vietnamese-English machine translation study.We hope our publicly available dataset and study can serve as a starting point for future research and applications on Vietnamese-English machine translation.We release our dataset at:
Digital Youth and Their Acquisition of Values When Using the Internet

The ability to respond to complex demands in a hypermediated society such as the current one, in young people who navigate the digital world, demands a combination of practical skills, knowledge, values, and social components mobilized together to influence the ways of communicating, acquiring values, and training. Taking this context into account, the following research aims to discover if there is a relationship between the use of the Internet and the social values perceived by university students highlighting the variables of gender, age, and academic year. Regarding the methodology, the type of study is non-experimental with a correlational design, with a sample of 305 university students from the faculties of human and social sciences, as well as education in the Spanish and Chilean context. To collect data on the variable presence of social values, the “Social Values Questionnaire” was developed, and to collect data on variable Internet use, the “Internet Use Questionnaire” was constructed, considering reliability criteria. The results revealed that most university students that use the Internet can identify social values and use them when they communicate with classmates, friends, and family, although with certain differences when looking at the variables analyzed. In conclusion, it is important to promote the ethical and responsible use of the Internet and social networks among young university students because it promotes the development of personal skills and social values.