Reviewerly/exHarmony huggingface dataset:
Features of teaching technical, physical and mathematical disciplines by means of distance learning in guarantine conditions

The article deals with the actual problem of introduction of distance learning during lockdown. Quarantine measures in educational institutions made it impossible for traditional forms of education and direct contact of lecturers with students at the classroom. Acceptance of this fact indicated the distance learning is the only way of in the current circumstances, and forced skeptics to change their views and beliefs about distance learning and to take possession of its technologies.&#x0D; The authors carried out a careful analysis of the legal documents that became the basis for the introduction of distance learning in educational institutions of Ukraine, in particular at universities. World education and science trends in distance learning are explored and other countries’ experiences of using technological innovations in educational services was examined.&#x0D; The review of the psycho-pedagogical literature on the organization of distance learning helped to distinguish such basic features: interactivity; using of new achievements of information technologies in the educational process; business communication skills during video communications, and during group video conferencing.&#x0D; The conducted survey of students showed that the advantages of distance learning are: the lack of a rigid time frame for mastering the learning material; the opportunity to independently organize your time intended for training; access to training materials and educational services at any time; the use of modern means of communication with teachers; the opportunity to get advice at a convenient time, quickly get an answer to questions through chat services of instant messengers or video communication technology; lack of transportation and accommodation costs and etc.&#x0D; The article provides examples of use by teachers of the National University of Civil Defence of Ukraine of such web resources as Zoom, Skype and etc. for educational online lectures, online practical classes, and technology of 3D-virtual tours to production enterprises during quarantine.
Predicting Type Annotations for Python using Embeddings from Graph Neural Networks

An intelligent tool for type annotations in Python would increase the productivity of developers.Python is a dynamic programming language, and predicting types using static analysis is difficult.Existing techniques for type prediction use deep learning models originated in the area of Natural Language Processing.These models depend on the quality of embeddings for source code tokens.We compared approaches for pretraining embeddings for source code.Specifically, we compared FastText embeddings to embeddings trained with Graph Neural Networks (GNN).Our experiments showed that GNN embeddings outperformed FastText embeddings on the task of type prediction.Moreover, they seem to encode complementary information since the prediction quality increases when both types of embeddings are used.1 https://github.com/VitalyRomanov/python-type-prediction
FAIRifying Clinical Studies Metadata: A Registry for the Biomedical Research

The data produced during a research project are too often collected for the sole purpose of the study, therefore hindering profitable reuse in similar contexts. The growing need to counteract this trend has recently led to the formalization of the FAIR principles that aim to make (meta)data Findable, Accessible, Interoperable and Reusable, for humans and machines. Since their introduction, efforts are ongoing to encourage FAIR principles adoption and to implement solutions based on them. This paper reports on the FAIR-compliant registry we developed to collect and serve metadata describing clinical trials. The design of the registry is based on the FAIR Data Point (FDP) specifications, the state-of-the-art reference for FAIRified metadata sharing. To map the metadata relevant to our use case, we have extended the DCAT-based semantic model of the FDP adopting well-established ontologies in the biomedical and clinical domain, like the Semanticscience Integrated Ontology (SIO). Current implementation is based on the Molgenis software and provides both a user interface and a REST API for metadata discovering. At present the registry is being loaded with the metadata of the 18 clinical studies included in the ‘I FAIR Program’, a project finalised to the dissemination of FAIR best practices among the clinical researchers in Sardinia (Italy). After a testing phase, the registry will be publicly available, while the new model and the source code will be released open source.
How Far Disaster Management Implemented Toward Flood Preparedness: A Lesson Learn from Youth Participation Assessment in Indonesia

Flood is a common and frequent natural disaster in many countries that causes huge economic losses and casualties every year. Youth participation in flood disaster management (FDM) has not been much explored, especially in the non-prone area but contributing to flooding resilience. Therefore, this study aims to identify youth participation in disaster management to help an improvement in preparedness action. The research was conducted using a qualitative model: case study research, involving 191 young people aged 14-35-years in 16 sub-districts in Semarang City. The data, including youth’s action, knowledge, and participation in FDM, was collected using Google Form, observation, and interview, then statistically analyzed using Mann-Whitney’s test and path analysis. The results show the respondents in flood-affected areas are more actively participating in flood disaster management action because of their experience in facing flooding. Also, the planning step is significantly influenced by the FDM implementation. The planning process is the main defining factor in disaster management successfulness and essentially affecting mitigation, rehabilitation, and evaluation steps. The level of youth participation is deemed necessary to be increased to develop a more comprehensive disaster management program according to regional needs. We suggest that FDM should be transformed into disaster awareness which is delivered through education, socialization, training, and/or flood disaster response simulations.
Interactive M-Learning Design Innovation using Android-Based Adobe Flash at WFH (Work From Home)

Work From Home (WFH) is an activity that is carried out at home with the flexibility to work both place and time with the help of telecommunications technology. The birth of WFH was caused by the impact of the Covid-19 pandemic, which forced all activities in various fields, including government, business, trade, education, and so on, to be carried out online through the use of technology. One thing that is affected is in the field of education, the teaching and learning process must be carried out online. The problem that occurs is the unpreparedness of the infrastructure and resources in the smooth running of the distance learning process. One of the steps to provide convenience in the distance learning process is to innovate in the field of technology by creating an Android-based M-Learning application with an interactive concept so that it not only provides simple learning but is also designed attractively and interactively so that student participants can interact directly with this M-Learning application. Interactive M-Learning applications are designed with UML (Unified Modeling Language) modeling and translated into applications using Adobe Flash and then implemented on Android mobile devices.­
Hybrid Reality-Based Education Expansion System for Non-Traditional Learning

Many educators utilize conventional coaching methods to coach and study behaviors in a classroom with face-to-face, verbal contact. But, the coaching with learning atmosphere has developed further than the classroom. The incorporation of technology at the coaching with learning procedure is the novel tendency at teaching, by a favorable result. Technologies present surroundings for learning behaviors to happen anytime also everywhere to advantages instructors with students universal. One of the skills to have been demonstrating feasibilities of the appliance at learning surroundings is Hybrid Reality (HR), which includes together Virtual Reality (VR) with Augmented Reality (AR). This work attempts to construct ahead the recent condition of hybrid reality also its appliance at learning. The initial section depicts the fundamental formation of hybrid reality also its various divisions. The subsequent sections provide the superior construction of a few innovative appliances that are implemented for the hybrid reality. Lastly, the paper shows the benefits of those applications over the traditional teaching methods and the essential user reactions. The outcomes have highly in assistance of taking mobile applications based on Hybrid Reality into a contemporary teaching scheme.
Dirichlet Process Mixture of Generalized Inverted Dirichlet Distributions for Positive Vector Data With Extended Variational Inference

A Bayesian nonparametric approach for estimation of a Dirichlet process (DP) mixture of generalized inverted Dirichlet distributions [i.e., an infinite generalized inverted Dirichlet mixture model (InGIDMM)] has been proposed. The generalized inverted Dirichlet distribution has been proven to be efficient in modeling the vectors that contain only positive elements. Under the classical variational inference (VI) framework, the key challenge in the Bayesian estimation of InGIDMM is that the expectation of the joint distribution of data and variables cannot be explicitly calculated. Therefore, numerical methods are usually applied to simulate the optimal posterior distributions. With the recently proposed extended VI (EVI) framework, we introduce lower bound approximations to the original variational objective function in the VI framework such that an analytically tractable solution can be derived. Hence, the problem in numerical simulation has been overcome. By applying the DP mixture technique, an InGIDMM can automatically determine the number of mixture components from the observed data. Moreover, the DP mixture model with an infinite number of mixture components also avoids the problems of underfitting and overfitting. The performance of the proposed approach is demonstrated with both synthesized data and real-life data applications.
THE USE OF E-LEARNING MANAGEMENT SYSTEMS TO SUPPORT ENGLISH LANGUAGE LEARNING AT ENGINEERING STUDY PROGRAMS: QUALITY AND LEARNERS' SATISFACTION

English language courses in their curricu including of foreign, English language are numerous starting from the fact that the English language is the facto language of science, following that the English language is important in business and engineering communication in modern business and engineering environment.The approaches to teaching and learning of English language are numerous and in this paper, it will be presented experience in using the Learning Management System, Moodle in teaching and learning of English language a with this platform and the concept of blended learning.
Dual-Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect-Based Sentiment Classification

With the development of 5G, the advancement of basic infrastructure has led to considerable development in related research and technology. It also promotes the development of various smart devices and social platforms. More and more people are now using smart devices to post their reviews right after something happens. In order to keep pace with this trend, we propose a method to analyze users’ sentiment by using their text data. When analyzing users’ text data, it is noted that a user’s review may contain many aspects. Traditional text classification methods used by smart devices, however, usually ignore the importance of multiple aspects of a review. Additionally, most algorithms usually ignore the network structure information between the words in a sentence and the sentence itself. To address these issues, we propose a novel dual-level attention-based heterogeneous graph convolutional network for aspect-based sentiment classification which minds more context information through information propagation along with graphs. Particularly, we first propose a flexible HIN (heterogeneous information network) framework to model the user-generated reviews. This framework can integrate various types of additional information and capture their relationships to alleviate semantic sparsity of some labeled data. This framework can also leverage the full advantage of the hidden network structure information through information propagation along with graphs. Then, we propose a dual-level attention-based heterogeneous graph convolutional network (DAHGCN), which includes node-level and type-level attentions. The attention mechanisms can analyze the importance of different adjacent nodes and the importance of different types of nodes for the current node. The experimental results on three real-world datasets demonstrated the effectiveness and reliability of our model.
Processing SPARQL Property Path Queries Online with Web Preemption

SPARQL property path queries provide a succinct way to write complex navigational queries over RDF knowledge graphs. However, the evaluation of these queries over online knowledge graphs such as DBPedia or Wikidata is often interrupted by quotas, returning no results or partial results. To ensure complete results, property path queries are evaluated client-side. Smart clients decompose property path queries into subqueries for which complete results are ensured. The granularity of the decomposition depends on the expressivity of the server. Whatever the decomposition, it could generate a high number of subqueries, a large data transfer, and finally delivers poor performance. In this paper, we extend a preemptable SPARQL server with a partial transitive closure operator (PTC) based on a depth limited search algorithm. We show that a smart client using the PTC operator is able to process SPARQL property path online and deliver complete results. Experimental results demonstrate that our approach outperforms existing smart client solutions in terms of HTTP calls, data transfer and query execution time.
Trans-Create – Co-Design with Persons with Severe Disabilities

Technology has potential for improving the lives of persons with severe disabilities. But it’s a challenge to create technology that improves lives from a person’s own perspective. Co-design methods have therefore been used in the design of Assistive Technology, to include users in the design process. But it’s a challenge to ensure the quality of participation with persons with significantly different prerequisites for communication than ourselves. It’s hard to know if what we design is good for them in the way they themselves define it, in a communication situation, which has to be significantly different than traditional co-design. In this paper, we present a new approach to co-design with persons with severe disabilities. We call this process “trans-create”, based on the creative translation we use when translating between cultures. We found that by using familiar artifacts that could be added and removed in the co-design process, we had a language for communication. By adding a personalisable digital layer to the artifacts, we could adapt, scale and redesign both tangible, visual and sound qualities in the situation dynamically. For example, by making it possible for the user to choose and activate a pink music cover card (RFID) that turns the lighting of the entire room pink and changes the music. This implies changing the distinction between designer and user, between the design process and the use process, and the view of what we create during a co-design process. That is why we have chosen to call this process “trans-create”, instead of co-create, what we create for “living works”, instead of design, a hybridisation between design and use, process and result.
A Personalized Recommendation for Web API Discovery in Social Web of Things

With the explosive growth of Web of Things (WoT) and social web, it is becoming hard for device owners and users to find suitable web Application Programming Interface (API) that meet their needs among a large amount of web APIs. Social-aware and collaborative filtering-based recommender systems are widely applied to recommend personalized web APIs to users and to face the problem of information overload. However, most of the current solutions suffer from the dilemma of accuracy- diversity where the prediction accuracy gains are typically accompanied by losses in the diversity of the recommended APIs due to the influence of popularity factor on the final score of APIs (e.g., high rated or high-invoked APIs). To address this problem, the purpose of this paper is developing an improved recommendation model called (Personalized Web API Recommendation) PWR, which enables to discover APIs and provide personalized suggestions for users without sacrificing the recommendation accuracy. To validate the performance of our model, seven variant algorithms of different approaches (popularity-based, user-based and item-based) are compared using MovieLens 20M dataset. The experiments show that our model improves the recommendation accuracy by 12% increase with the highest score among compared methods. Additionally it outperforms the compared models in diversity over all lengths of recommendation lists. It is envisaged that the proposed model is useful to accurately recommend personalized web API for users.
Assessing practitioner beliefs about software engineering

Software engineering is a highly dynamic discipline. Hence, as times change, so too might our beliefs about core processes in this field. This paper checks some five beliefs that originated in the past decades that comment on the relationships between (i) developer productivity; (ii) software quality and (iii) years of developer experience. Using data collected from 1,356 developers in the period 1995 to 2006, we found support for only one of the five beliefs titled “Quality entails productivity.” We found no clear support for four other beliefs based on programming languages and software developers. However, from the sporadic evidence of the four other beliefs, we learned that a narrow scope could delude practitioners in misinterpreting certain effects to hold in their day-to-day work. Lastly, through an aggregated view of assessing the five beliefs, we find programming languages act as a confounding factor for developer productivity and software quality. Thus the overall message of this work is that it is both important and possible to revisit old beliefs in software engineering. Researchers and practitioners should routinely retest old beliefs.
A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.
KGDB: Knowledge Graph Database System with Unified Model and Query Language

Knowledge graph is an important cornerstone of artificial intelligence, which currently has two main data models: RDF graph and property graph.There are several query languages on these two data models.The query language on RDF graph is SPARQL, and the query language on property graph is mainly Cypher.Over the last decade, various communities have developed different data management methods for RDF graphs and property graphs.Inconsistent data models and query languages hinder the wider application of knowledge graphs.KGDB is a knowledge graph database system with unified data model and query language.First, based on the relational model, a unified storage scheme is proposed, which supports the efficient storage of RDF graphs and property graphs, and meets the requirement of knowledge graph data storage and query load.Secondly, using the clustering method based on characteristic sets, KGDB can handle the issue of untyped triple storage.Finally, it realizes the interoperability of SPARQL and Cypher, which are two different knowledge graph query languages, and enables them to operate on the same knowledge graph.The extensive experiments on real-world datasets and synthetic datasets are carried out.The experimental results show that, compared with the existing knowledge graph database management systems, KGDB can not only provide more efficient storage management, but also has higher query efficiency.KGDB saves 30% of the storage space on average compared with gStore and Neo4j.The experimental results on basic graph pattern matching query show that, for the real-world dataset, the query efficiency of KGDB is generally higher than that of gStore and Neo4j, and can be improved by at most two orders of magnitude.
A New Computational Method for Arabic Calligraphy Style Representation and Classification

Despite the importance of recognizing Arabic calligraphy styles and their potential usefulness for many applications, a very limited number of Arabic calligraphy style recognition works have been established. Thus, we propose a new computational tool for Arabic calligraphy style recognition (ACSR). The present work aims to identify Arabic calligraphy style (ACS) from images where text images are captured by different tools from different resources. To this end, we were inspired by the indices used by human experts to distinguish different calligraphy styles. These indices were transformed into a descriptor that defines, for each calligraphy style, a set of specific features. Three scenarios have been considered in the experimental part to prove the effectiveness of the proposed tool. The results confirmed the outperformance of both individual and combine features coded by our descriptor. The proposed work demonstrated outstanding performance, even with few training samples, compared to other related works for Arabic calligraphy recognition.
Designing Human-Computer Conversational Systems using Needs Hierarchy

Souvick 'Vic' Ghosh is an Assistant Professor at the School of Information, SJSU.His scholarship involves the development of research models and methods that extend the traditional view of information seeking into voice-based and interactive environments.His work in these areas is informed by and contributes to the necessary research on Conversational Information Retrieval at the level of individuals interacting with a voice-based search system,