Reviewerly/exHarmony huggingface dataset:
Autonomous Tracking Control for Four-Wheel Independent Steering Robot Based on Improved Pure Pursuit

Autonomous tracking control is one of the fundamental challenges in the field of robotic autonomous navigation, especially for future intelligent robots. In this paper, an improved pure pursuit control method is proposed for the path tracking control problem of a four-wheel independent steering robot. Based on the analysis of the four-wheel independent steering model, the kinematic model and the steering geometry model of the robot are established. Then the path tracking control is realized by considering the correlation between the look-ahead distance and the velocity, as well as the lateral error between the robot and the reference path. The experimental results demonstrate that the improved pure pursuit control method has the advantages of small steady-state error, fast response and strong robustness, which can effectively improve the accuracy of path tracking.
A Study of Learning Experience during Olfaction-enhanced Adaptive Rich Media Delivery

In recent years, the immersive technologies such as Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and multiple-sensorial media (mulsemedia) bring new multi-dimensional effects such as immersion, vibration, smell, airflow, etc. to gaming, video entertainment, e-learning and other aspects of human life. This paper introduces an adaptive mulsemedia-based delivery solution (DASHMS) which enhances classic multimedia delivery with effects such as touch, haptics and smell. It describes a study of learning experience with various stimuli when mulsemedia-based learning material is distributed in a real-life subjective experiment with 44 participants. The experiment evaluates user Quality of Experience (QoE) and the impact on learner education in the context of the European Horizon 2020 project NEWTON.
An Ontology for CoNLL-RDF: Formal Data Structures for TSV Formats in Language Technology

In language technology and language sciences, tab-separated values (TSV) represent a frequently used formalism to represent linguistically annotated natural language, often addressed as A large number of such formats do exist, but although they share a number of common features, they are not interoperable, as different pieces of information are encoded differently in these dialects.CoNLL-RDF refers to a programming library and the associated data model that has been introduced to facilitate processing and transforming such TSV formats in a serialization-independent way. CoNLL-RDF represents CoNLL data, by means of RDF graphs and SPARQL update operations, but so far, without machine-readable semantics, with annotation properties created dynamically on the basis of a user-defined mapping from columns to labels. Current applications of CoNLL-RDF include linking between corpora and dictionaries [Mambrini and Passarotti, 2019] and knowledge graphs [Tamper et al., 2018], syntactic parsing of historical languages [Chiarcos et al., 2018; Chiarcos et al., 2018], the consolidation of syntactic and semantic annotations [Chiarcos and Fath, 2019], a bridge between RDF corpora and a traditional corpus query language [Ionov et al., 2020], and language contact studies [Chiarcos et al., 2018].We describe a novel extension of CoNLL-RDF, introducing a formal data model, formalized as an ontology. The ontology is a basis for linking RDF corpora with other Semantic Web resources, but more importantly, its application for transformation between different TSV formats is a major step for providing interoperability between CoNLL formats.
PENERIMAAN MEMBACA DAN MENULIS DI TAMAN KANAK-KANAK

Kindergarten is a place to learn to play, so learning is through games. Where the child's world plays, by playing children will be happy, cheerful, and happy. But all that is no longer, the kindergarten was changed to a school whose lessons were like elementary school. Parents require teachers to teach reading and writing when the task of TK is to introduce a variety of knowledge, attitudes, and skills in preparation for elementary school.
Metrics for Student Source Code Analysis

Source code quality is undoubtedly very important, yet usually forgotten aspect of software development process. This study shows different approaches to analysis of student codes, mainly in Java. By comparing different widely-used tools and our solution, we are able to get precise results from such an analysis, and thus to improve the code quality. The analysis results can also be used to determine knowledge and skills of novice programmers (students). Analysis of source codes may be as simple as counting the occurrences of language constructs or as complex as design pattern detection. Different approaches and solutions, i.e. measurements of language construct occurrences, copy-pasted code and design pattern detection, or source code quality, are discussed and subjected to evaluation by being executing on source codes of students as novice programmers.
KESULITAN BELAJAR PADA SISWA SEKOLAH DASAR: STUDI KASUS DI SEKOLAH DASAR NEGERI AGOM

Learning difficulties are conditions where one student experiences difficulties in learning in the field of study, the factors that cause, and the nature of learning difficulties at SD Negeri Agom. This research uses a quantitative descriptive approach. The research subjects included students of SD Negeri Agom. Data collection techniques used are interview, observation, and documentation methods. The research instruments used were interview guidelines and observation guidelines. The results showed that 1) the level of learning difficulty of SD Negeri Agom students was in the medium category, 2) the field of study that was difficult for students to study was mathematics, 3) the factors causing learning difficulties, 4) while the nature of the learning difficulties was temporary. By knowing the types of learning difficulties, teachers can choose the right, effective and efficient types of interventions to provide in helping students overcome learning difficulties.
Developing Machine Learning Models for Predicting Intensive Care Unit Resource Use During the COVID-19 Pandemic: Experiences from a Bi-Regional Health Care System Covering 2.5 Million Citizens in Denmark

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Integrating Task-based Teaching and Testing in College English Context

The purpose of the current research is to improve the current traditional teaching style in college English classrooms and analyze the problems and phenomena that students exhibit in the current language teaching classrooms.Then, based on the analysis of these phenomena, the project tracked the original causes, conducted certain investigation, and created a new foreign language teaching model that integrates task-based teaching and testing based on the standard-referenced testing theory and the existing research findings.
Finding an Optimal Alphabet Ordering for Lyndon Factorization Is Hard.

This work establishes several strong hardness results on the problem of finding an ordering on a string’s alphabet that either minimizes or maximizes the number of factors in that string’s Lyndon factorization. In doing so, we demonstrate that these ordering problems are sufficiently complex to model a wide variety of ordering constraint satisfaction problems (OCSPs). Based on this, we prove that (i) the decision versions of both the minimization and maximization problems are NP-complete, (ii) for both the minimization and maximization problems there does not exist a constant approximation algorithm running in polynomial time under the Unique Game Conjecture and (iii) there does not exist an algorithm to solve the minimization problem in time poly(|T|) ⋅ 2^o(σlog σ) for a string T over an alphabet of size σ under the Exponential Time Hypothesis (essentially the brute force approach of trying every alphabet order is hard to improve significantly).
K-Anonymity technique for privacy protection: a proof of concept study

Privacy is a concept directly related to people&amp;apos;s interest in maintaining personal space without the interference of others. In this paper, we focus on study the k-anonymity technique since many generalization algorithms are based on this privacy model. Due to this, we develop a proof of concept that uses the k-anonymity technique for data anonymization to anonymize data raw and generate a new ﬁle with anonymized data. We present the system architecture and detailed an experiment using the adult data set which has sensitive information, where each record corresponds to the personal information for a person. Finally, we summarize our work and discuss future works.
COGNITIVE GAMES FOR IMPROVING LEARNING SKILLS

A. Mocanu1, I. Mocanu1, O. Cramariuc2 1University Politehnica of Bucharest (ROMANIA) 2IT Center for Science and Technology (ROMANIA)
Propagation of Random Errors by the Discrete Wavelet Transform Algorithm

The paper presents considerations concerning the transfer of random errors from the input to the output of the Discrete Wavelet Transform (DWT) algorithm. The concept of determining an uncertainty of its output data based on the probabilistic error description has been presented. The DWT is discussed as the product of the vector of input quantities and the matrix of algorithm coefficients. Calculations of the uncertainty of a single output result of the algorithm are described with assumption that the input quantities are burdened by random errors of known distributions. Theoretical considerations have been verified by simulation experiments using the Monte Carlo method. Determining the uncertainty at the DWT output is possible due to the specific properties of transferring random errors by linear and additive algorithms.
An Empirical Investigation the Use of Information, Communication Technologies to English Language Acquisition: a Case Study From the Jordan

Information, Communication Technologies (ICT) has become an important educational component in higher education. ICT it possible for students to learn, collaborate, and share ideas among each other with the aid of e-learning and development Students' abilities. However, the societies and countries at present, the English language is considered a key to the doors of development and use ICT. This paper aims at exploring the role of employing information and communication technologies in English language acquisition in higher education universities in Jordan; To serve this purpose, where a 12 questionnaire was distributed to teachers of the English language in Jordan from two schools. Findings indicated that information and communication technologies have a positive and important role in the acquisition of the English language among Jordanian students. Furthermore, results indicated that ICT can be one of the promising scholastic technologies to be employed in the learn the English language within Jordan
Validação automática dos currículos da plataforma lattes à biblioteca digital brasileira de teses e dissertações (BDTD)

One of the main problems in scientific repositories is to identify relationships and link metadata from different sources. This paper briefly describes the new results in an effort to build a software platform able to process metadata from different heterogeneous sources. The case study for this phase is the linking of the thesis and dissertation repositories network, the Brazilian Digital Library of Theses and Dissertations (BDTD), and the Lattes Platform of researchers' curricula. A strategy for generating hashes for records is applied, through transformation rules on title and year, capturing, in one step, the Lattes identifiers of theses and dissertations authors and supervisors.
VIRTUAL UNIVERSITY: EDUCATION AS A LIFESTYLE

The concept of the virtual university is relatively new and it is necessary to distinguish a number of contradictory points in its verification. Firstly, it is the quality of the education received. The problem of educational process content at the virtual university, organization of control and level of accomplished tasks, issues of intellectual abilities and critical thinking development, reflexivity of consciousness are the subject of heated discussions of both the supporters and opponents of the virtual university idea. Secondly, there is the question of the way of thinking. The virtual space involves a network mode of organization characterized by openness of the structure, poly variability of determination and expediency of modal logic. The phenomenon of virtual university is the result of responding to the needs of social transformation. In such model, the content of the social roles of the teacher and the student changes significantly. The competence of the modern teacher is not limited to knowledge. After all, the most experienced academician is not able to absorb all the information in a certain science, and obviously loses in the volume of assimilated information to any search engine.
MODELLING OF THE HUMANITARIAN LEARNING ENVIRONMENT WITHIN RUSSIAN UNIVERSITIES ON THE BASIS OF NON-FORMAL EDUCATION

A. Gazizova1, M. Siraeva2, V. Panfilova3, S. Skopkareva4 1Kazan National Research Technical University named after A.N. Tupolev (RUSSIAN FEDERATION) 2Udmurt State University (RUSSIAN FEDERATION) 3Kazan Federal University (RUSSIAN FEDERATION) 4School for Advanced Studies of Udmurt Republic (RUSSIAN FEDERATION)
FedTopK: Top-K Queries Optimization over Federated RDF Systems

Recently, how to evaluate SPARQL queries over federated RDF systems has become a hot research topic. However, most existing studies mainly focus on implementing and optimizing the basic queries over federated SPARQL systems, and few of them discuss top-k queries. To remedy this defect, this demo designs a system named FedTopK that can support top-k queries over federated RDF systems. FedTopK employs a cost-based optimal query plan generation algorithm and a query plan execution optimization strategy to minimize the top-k query cost. In addition, FedTopK uses a query decomposition optimization scheme which allow merge triple patterns with the same multi-sources into one subquery to reduce the remote access times. Experimental studies over real federated RDF datasets show that the demo is efficient.
Coronis: Towards integrated and open COVID-19 data

Motivated by the global unrest related to the COVID-19 pandemic, this demo paper presents a system for acquisition of COVID-related data from different, public sources, and interlinking under a common semantic data model at a fine level of granularity. The integrated data set contains data from several European countries, which come in different schemata, formats, granularity, and data integration acts as a facilitator towards querying data from different sources, joint data analysis, and identifying correlations at varying geographical level. Moreover, our work shows how such an integrated data set can be exploited to answer complex questions for the pandemic, also in combination with other data sets via federated queries. © 2021 Copyright held by the owner/author(s).
Chatboats for Releiving Stress

Happy soul is an android based chatbot that helps its users to stay calm and help them relieve stress by acting as a 24*7 companion. Chatbot refers to software that can generate applicable replies to any query. This paper will prove to be of great help to society as nowadays adolescents refrain themselves from showing true emotions to anyone. This project aims privacy and confidentiality of the users' emotion as the replies will be auto generated and the user will not have to reveal their cause of anxiety to anyone. Happy soul uses algorithms of machine learning and natural language processing to generate replies. An API of the processing can be made to be used by an android application. The application serves as front end to just take in the input and display the output. The backend processing uses concept of artificial neural network, artificial intelligence markup language and classification to process the input received from front end and produce a corresponding reply. Google API is used to fetch the replies for new unknown questions. These replies will then be stored in the database to facilitate replies to similar questions asked henceforth. Therefore, continuous learning can be established.
Responding to Media Inquiries About Remote Triggering Interactions

&amp;lt;p&amp;gt;In the aftermath of a significant earthquake, seismologists are frequently asked questions by the media and public regarding possible interactions with recent prior events, including events at great distances away, along with prospects of larger events yet to come, both locally or remotely.&amp;amp;#160; For regions with substantial earthquake catalogs that provide information on the regional Gutenberg-Richter magnitude-frequency relationship, Omori temporal aftershock statistical behavior, and aftershock productivity parameters, probabilistic responses can be provided for likelihood of nearby future events of larger magnitude (as well as expected behavior of the overall aftershock sequence). However, such procedures do not provide answers to inquiries about long-range interactions, either retrospectively for interaction with prior remote large events or prospectively for interaction with future remote large events. Dynamic triggering that may be involved in such long-range interactions occurs, often with significant temporal delay, but is not well-understood, making it difficult to respond to related inquiries. One approach to addressing such inquiries is to provide retrospective or prospective occurrence histories for large earthquakes based on global catalogs; while not providing quantitative understanding of any physical interaction, experience-based guidance on the (typically very low) chances of causal interactions can inform public understanding of likelihood of specific scenarios they are commonly very interested in.&amp;lt;/p&amp;gt;
Digital Contact Tracing in South Korea

South Korean citizens have exchanged privacy for permission to move about freely. The country has impressively managed its COVID-19 crisis with fewer fatalities than in other countries, and without a lockdown. This is largely thanks to extensive testing and to an elaborate and some would say invasive contact tracing system. Professor Jae Chun Choe describes how that system functions.
Embracing the value of research data: introducing the JCHLA/JABSC Data Sharing Policy

As health sciences researchers have been asked to share their data more frequently due to funder policies, journal requirements, or interest from their peers, health sciences librarians (HSLs) have simultaneously begun to provide support to researchers in this space through training, participating in RDM efforts on research grants, and developing comprehensive data services programs. If supporting researchers' data sharing efforts is a worthwhile investment for HSLs, it is crucial that we practice data sharing in our own research endeavours. sharing data is a positive step in the right direction, as it can increase the transparency, reliability, and reusability of HSL-related research outputs. Furthermore, having the ability to identify and connect with researchers in relation to the challenges associated with data sharing can help HSLs empathize with their communities and gain new perspectives on improving support in this area. To that end, the Journal of the Canadian Health Libraries Association / Journal de l’Association des bibliothèques de la santé du Canada (JCHLA / JABSC) has developed a Data Sharing Policy to improve the transparency and reusability of research data underlying the results of its publications. This paper will describe the approach taken to inform and develop this policy.
Community Detection in Complex Networks

As a ubiquitous feature of complex networks, community characteristic is common in real world networks especially in wireless sensor network. It is vital to detect community structure in complex networks and to take advantage of extracted information. On the basis, community detection has become a popular theme in the field of complex networks. It focuses on uncovering the affiliation of each node through topology analysis. In this chapter, we have presented the essential knowledge and advanced methods of community detection, including proximate support vector clustering (PSVC), deep auto-encoded extreme learning machine (DA-ELM), deep auto-coded clustering (DAC) and local aggregated differential evolution algorithm (LADE). These methods have been applied in tree-based WSNs and have been proved in terms of robustness and fragility.
Study on the Teaching of Outdoor Cycling Course

With the rapid economic development, people's living standards and quality of life have greatly improved, and bicycles have gradually shifted from the original means of transportation to the fields of fitness and entertainment.The continuous deepening of the concept of "low carbon, environmental protection and green travel" has made the role of bicycles increasingly prominent.Physical education in colleges and universities bears the important responsibility of extending school physical education to social sports.It is an important part of the system for students to receive physical education before entering the society.In the face of the gradual heating of the "cycling fever" in the society, in college physical education, bicycles can be increased.Relevant teaching content can not only expand the development space of physical education, but also provide technical reserves and ability enhancement for students' leisure, entertainment and fitness after integrating into the society in the future.
End to End Learning Human Pose Detection Using Convolutional Neural Networks

There have been many uses of human pose detection in recent days. In this paper, we propose a new algorithm for estimating human pose in real time by using pair of convolutional neural networks, along with a restrictor, trying to predict the human pose in next sequence, and 3D pose generator, from an image sequence, either fed in real time or prerecorded. This algorithm also focuses on trying to implement this in a lightweight pipeline, which can be used in lightweight systems such as a mobile platform.
A quaternion-group knowledge graph embedding model

Capturing the composite embedding representation of a multi-hop relation path is an extremely vital task in knowledge graph completion. Recently, rotation-based relation embedding models have been widely studied to embed composite relations into complex vector space. However, these models make some over-simplified assumptions on the composite relations, resulting the relations to be commutative. To tackle this problem, this paper proposes a novel knowledge graph embedding model, named QuatGE, which can provide sufficient modeling capabilities for complex composite relations. In particular, our method models each relation as a rotation operator in quaternion group-based space. The advantages of our model are twofold: (1) Since the quaternion group is a non-commutative group (i.e., non-Abelian group), the corresponding rotation matrices of composite relations can be non-commutative; (2) The model has a more expressive setting with stronger modeling capabilities, which is flexible to model and infer the complete relation patterns, including: symmetry/anti-symmetry, inversion and commutative/non-commutative composition. Experimental results on four benchmark datasets show that the proposed method outperforms the existing state-of-the-art models for link prediction, especially on composite relations.