Reviewerly/exHarmony huggingface dataset:
From Constituency to UD-Style Dependency: Building the First Conversion Tool of Turkish

This paper deliberates on the process of building the first constituency-to-dependency conversion tool of Turkish 1 .The starting point of this work is a previous study in which 10,000 phrase structure trees were manually transformed into Turkish from the original Penn Treebank corpus.Within the scope of this project, these Turkish phrase structure trees were automatically converted into UD-style dependency structures, using both a rule-based algorithm and a machine learning algorithm specific to the requirements of the Turkish language.The results of both algorithms were compared and the machine learning approach proved to be more accurate than the rule-based algorithm.The output was revised by a team of linguists.The refined versions were taken as gold standard annotations for the evaluation of the algorithms.In addition to its contribution to the UD Project with a large dataset of 10,000 Turkish dependency trees, this project also fulfills the important gap of a Turkish conversion tool, enabling the quick compilation of dependency corpora which can be used for the training of better dependency parsers.
Performance of Rapid Diagnosis Technique (RDT) in Screening Malaria Among Patients of Selected Health Facilities in Ardo-Kola Local Government Area, Taraba State

Malaria infection is one of the most common disease of public health importance afflicting millions of people in sub-Saharan Africa.Malaria diagnosis and surveillance rely predominantly on rapid diagnostic tests (RDTs).This study investigated the performance of Rapid Diagnostic Test (RDT) against microscopy of stained blood for Plasmodium falciparum of patients attending some Primary Health Centres in Ardo-Kola LGA, Taraba State.Five hundred and eighty five (585) blood samples were collected and examined for Plasmodium falciparum on the microscope.Rapid Diagnostic Tests were also used for examination.The overall result showed a prevalence of 446 (76.23%).The infection found female, 199 (82.23%) to be more exposed to malaria than male, 247 (72.01%) with no significant difference (χ 2 =4.381;P≥0.05).With regards to age, females aged ≥51 years were more infected than other age groups, while male aged 21-30 years were more infected with malaria than the other age groups.There was no significant difference with malaria infection between the age groups (χ 2 =2.207;P≥0.05).The performance of RDTs against microscopy showed that RDT used had a sensitivity of 66.91% and specificity of 58.52%.The positive predictive value of 33.45% and negative predictive value of 85.02% were found for the RDT used.The present study demonstrated that RDTs can act as diagnostic tool to manage malaria in resource poor settings with limited, access to expert microscopy as they are easy to use and perform better than microscopy.It is recommended that malaria Rapid Diagnosis Tests can be used in endemic areas in Nigeria.
A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach

In this paper, we propose a novel fact checking and verification system to check claims against Wikipedia content.Our system retrieves relevant Wikipedia pages using Anserini, uses BERT-large-cased question answering model to select correct evidence, and verifies claims using XLNET natural language inference model by comparing it with the evidence.Table cell evidence is obtained through looking for entity-matching cell values and TAPAS table question answering model.The pipeline utilizes zero-shot capabilities of existing models and all the models used in the pipeline requires no additional training.Our system got a FEVEROUS score of 0.06 and a label accuracy of 0.39 in FEVEROUS challenge.
Co-Teaching Student-Model through Submission Results of Shared Task

Shared tasks have a long history and have become the mainstream of NLP research.Most of the shared tasks require participants to submit only system outputs and descriptions.It is uncommon for the shared task to request submission of the system itself because of the license issues and implementation differences.Therefore, many systems are abandoned without being used in real applications or contributing to better systems.In this research, we propose a scheme to utilize all those systems which participated in the shared tasks.We use all participated system outputs as task teachers in this scheme and develop a new model as a student aiming to learn the characteristics of each system.We call this scheme "Co-Teaching."This scheme creates a unified system that performs better than the task's single best system.It only requires the system outputs, and slightly extra effort is needed for the participants and organizers.We apply this scheme to the "SHINRA2019-JP" shared task, which has nine participants with various output accuracies, confirming that the unified system outperforms the best system.Moreover, the code used in our experiments has been released. 1
A Study on the Improvement of Air Force Spare Parts Demand Prediction Accuracy Using Machine Learning

The following research is conducted to study how much demand prediction accuracy is improved when machine learning is implemented on ROKAF spare parts demand prediction calculations. Based on the data from the last three years of ROKAF’s three major aircraft models, the study compares the demand prediction accuracy values computed through the time series technique and machine learning models. Generally utilized machine learning models, such as Decision Tree(DT), Linear Discriminant Analysis (LDA), Logistic Regression(LR), K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM), were implemented. Experiment results show that all machine learning models yield higher accuracy of the demand prediction than the time series technique. SVM, DT, K-NN and LR machine learning models yield excellent results, with accuracy values higher than 85%, while the LDA model shows relatively poor performance.
IMPLEMENTATION OF INCLUSIVE PEDAGOGY IN THE TEACHING OF CHEMISTRY

The aim of the article is to draw attention to the context that inclusive understanding of pedagogy brings to the education of natural sciences with an emphasis on the teaching of chemistry and to open a debate for cooperation in the fields of social and special pedagogy and the methodology of teaching chemistry. In the article, inclusion is understood as a process of creating and improving the learning conditions of all students, taking into account the broader dimensions of heterogeneity, diversity, and language, as well as cognitive and physical barriers that may occur during the teaching of chemistry. The main focus is centered on the concept of science literacy for all, out of which comes the requirement to link inclusive pedagogical principles and the goals of natural sciences education. We bring to attention the need to increase the sensitivity of chemistry teachers to differences that may have a negative impact on learning outcomes, as well as the need for targeted teacher training for leading inclusive classes and for assessing their level of competence required for inclusive teaching.
Scalable Streaming Graph and Time Series Analysis Using Partitioning and Machine Learning

Recent years have witnessed a massive increase in the amount of data generated by the Internet of Things (IoT) and social media. Processing huge amounts of this data poses non-trivial challenges in ...
Rhetorical Structure and Linguistic Features of Abstracts: A Comparative Study Of Local, National And International Journals In English Education Written by Indonesian Authors

The abstract is an important part of a research article; it is a research summary that must be attached, presented together with the research article. Therefore, the quality of an abstract is explicitly significant. This article aims to determine the similarities and differences of the rhetorical moves and linguistic features of abstracts of local, national, and international journals by Indonesian authors in the field of English language teaching. The method used is descriptive quantitative and qualitative methods or mix-methods by analyzing the communicative objectives and linguistic features of the abstract parts of research articles. The results show that the majority of Ijal abstracts have five moves while the majority of Edu-Ling abstracts have only three moves. In general, active sentences are used more dominantly used than passive sentences in all moves of all three groups of abstracts, and present tense is far more frequently used in Ijal and Teflin abstracts but past tense is more frequently used in Edu-Ling abstracts while that-complement clause in Move-4 of Ijal abstracts is the most frequently used among the three groups of abstracts. It can also be concluded that Teflin article abstracts are more similar to those of Ijal articles than to Edu-Ling articles in all aspects. This is probably because Teflin authors are more experienced in writing journal articles than those of Edu-Ling articles.
A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems.

Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request. We propose a scalable and automatic approach for improving NLU in a large-scale conversational AI system by leveraging implicit user feedback, with an insight that user interaction data and dialog context have rich information embedded from which user satisfaction and intention can be inferred. In particular, we propose a domain-agnostic framework for curating new supervision data for improving NLU from live production traffic. With an extensive set of experiments, we show the results of applying the framework and improving NLU for a large-scale production system across 10 domains.
Student Perspectives on Changing Requirements for Human Factors Engineering Education

In today’s digital economy, the Internet of Things (IoT) has connected devices, humans, and everyday objects to each other in ways that were unimaginable before. Vast amounts of data are collected everywhere and disrupting how we design systems and products. Data science and emerging technologies offer challenges and opportunities for early-career human factors professionals who are looking to grow their careers and their human factors practice. In this paper, we report on a survey to assess the perspectives of students currently studying human factors. The survey items examined shortfalls in current human factors education with re-spect to relevance to industry trends. The survey results show that students see a need to include more relevant subjects in data science, as well as opportunities to learn trending industry problems, hands-on experience with real-life projects, prior to graduation.
Character-based ThaiWord Segmentation with Multiple Attentions

Character-based word-segmentation models have been extensively applied to agglutinative languages, including Thai, due to their high performance.These models estimate word boundaries from a character sequence.However, a character unit in sequences has no essential meaning, compared with word, subword, and character cluster units.We propose a Thai word-segmentation model that uses various types of information, including words, subwords, and character clusters, from a character sequence.Our model applies multiple attentions to refine segmentation inferences by estimating the significant relationships among characters and various unit types.The experimental results indicate that our model can outperform other state-of-the-art Thai wordsegmentation models.
PECULIARITIES OF EDUCATIONAL MOBILITY IN THE AGE OF INFORMATIZATION

The author presents the features of educational mobility in the age of informatization, as well as a description of theoretical approaches to defining the concept and essence of academic mobility in terms of organizing the movement of students and teachers to other universities to achieve educational goals in the context of personal characteristics and needs.Global informatization of society is one of the dominant trends of civilization in the XXI century.The development of new technologies, modernization of production, automation of existing processes, and informatization of corporate data every day require more and more staff with knowledge of advanced technologies.Educational mobility in the age of informatization provides students and staff with more opportunities to have international experience, as well as to acquire promising and other relevant skills around the world.In addition, educational mobility allows higher education institutions from participating countries to establish long-term sustainable international cooperation with partner institutions from partner countries.The peculiarity of educational mobility in the age of informatization is: the use of computer tools as auxiliary procedures for more effective solutions of the system of didactic tasks; the use of computer technology in the educational process allows to solve other tasks in terms of content and form, to conduct a laboratory experiment, etc.; students can study and gain international experience at any convenient time; with the help of informatization of education, a student can get an international experience in another country while staying in his own country.Educational mobility is an opportunity for students traveling abroad to study and develop valuable skills and gain new experience.The essence of informatization of education is the structuring of professional knowledge in given subject areas and ensuring free access to databases.The learning process should be aimed not only at the ability to work with certain software but also at the technology of working with various information: audio, video, graphics, text, tabular.
M-Learning and Learning Autonomy - Needs Analysis and Suggested Model

In this 4.0 era, where technology has been a part of our daily lives, the integration of smart devices into education is an indispensable movement that we are striving for. Hence, the application of mobile devices into learning, M-learning is becoming a trendy approach and a quality assurance’s measurement for almost every academic institution. Technology makes education available for students almost anywhere, anytime, and offers unlimited access to learning resources. Furthermore, M-learning is also reported to have a positive impact on students’ learning autonomy. Hence, this paper aims at validating possibilities to amplify this aspect of M-learning and suggesting an appropriate M-learning model at Nguyen Tat Thanh university (NTTU). The paper investigates students’ needs, requirements, ability to adapt to the new trend of M-learning plus the external elements affecting their learning autonomy to come up with the learning model. Surveys were collected from students in the Faculty of Foreign Languages and analyzed using a qualitative method to provide educators and teachers at NTTU with insightful information and careful preparation before launching new approaches to M-learning.
Diving into Text Representation Learning with Deep Hashing

With the development of deep neural network, especially the emergence of large scale pre-trained language model, methods based on deep learning have substantially advanced the state of the art across a variety of subtasks in natural language processing. Nevertheless, in order to get better performance, the increasing model parameters and exponential growth of training time become problems that can not be ignored. In this paper, inspired by the application of deep hashing in large-scale image retrieval, text representation learning based on deep hashing technology has been extensively explored. And we use three common subtasks in natural language processing to evaluate the proposed method. Experimental results show that deep hashing can greatly reduce the physical space cost by text representation under the condition of limited performance loss, which is of great significance in large-scale text representation, and is worthy of further exploration.
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding

When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU – a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
The movement-stories approach to increasing physical activity in kindergartens during the pandemic

Research shows that the pandemic has increased sedentariness and decreased physical activity even in pre-schoolers.To deal with this period, the association Laboratorio 0246 proposed to some Italian kindergarten teachers an active training based on "movement-stories".39 teachers of kindergartens in 6 Italian regions were involved.All teachers completed the course a questionnaire, reporting difficulties related to space, time, bureaucratic demands, and highlighting problems in socializing with children due to restrictions.Most teachers reported that activities with storytelling in motion appeal to children, can be implemented in any indoor or outdoor setting, are easy to implement, and promote active children.There is a need for teacher training, including technical training about physical activity.Without the specific knowledge base, it is difficult for teachers to understand the real needs related to physical activity and to use methodology and didactics.The research-training-action pathway demonstrates to be well suited to address this situation.
ACCO

We propose ACCO: the first maliciously secure multiparty computation engine in the honest majority setting, which also supports secure and efficient comparison and integer truncation. Our system is also the first to achieve information theoretic security.
Student Scientific Publication at The Department of International Relations, UPN 'Veteran' Yogyakarta: An Action Research

This study aims to find the right strategy for increasing scientific publications for students of the Department of International Relations, UPN 'Veteran' Yogyakarta, Indonesia. Thus, it is essential to consider the low number of scientific publications produced by students considering that scientific publications are an obligation for academics. The Director-General of Higher Education Letter No. 152/E/T/2012 concerning the duty of scientific publications for students and Regulation of the National Accreditation Board for Higher Education Number 5 of 2019 concerning Study Program Accreditation Instruments requires publication and citation student scientific articles. The research uses action research to find strategies for increasing scientific publications where there are three research steps: identifying problems, making and testing procedures, and evaluating. The second step is to try the strategy based on problem identification. Problem identification is made through an online open questionnaire. The third step is the evaluation of the action research. Experiments conducted to influence student behavior in scientific writing showed that socialization and coaching clinics were needed to increase knowledge about scientific writing and encourage ideas. While writing assistance can produce student scientific publications because of its nature and able to foster comfort. This empirical evidence provides insights for the manager of student scientific journals and department managers to provide formulations for improving students' quality and quantity of scientific publications.&#x0D; &#x0D; Received: 11 August 2021 / Accepted: 3 October 2021 / Published: 5 November 2021
Sistem Informasi Penggajian dengan Pencatatan Presensi Menggunakan Fingerprint Pada MTS Nu Jogoloyo Berbasis Multiuser

Current technology has developed rapidly, computers are one of them. Almost all types of jobs require a computer to complete the task. In the field of computer education it is also very helpful, especially in terms of payroll services for teachers and employees who work as educators.&#x0D; MTs NU Jogoloyo Wonosalam Demak as an institution that has a large number of teachers and employees, besides that every day attendance officers are absent which results in difficulties in making payroll reports, especially recording and reporting still using manual methods so it takes longer time to record and deliver. report. In addition, sometimes there is a misunderstanding between attendance officers and teachers and employees regarding remuneration at the end of the month.&#x0D; In solving this payroll problem, the writer uses the Research and Development (R &amp; D) method, while making applications can use high-level programming languages ​​such as Microsoft Visual Basic Ver 6.0. and SQL Server 2000 for data base management.&#x0D; Payroll applications are needed to simplify transactions and reporting on a monthly basis. With the use of this new payroll system software, it is hoped that it can produce quality information, improve service to employees and can assist in the decision-making process
LOSS FUNCTIONS AND DESCENT METHOD

In this paper, we showed that it is possible to use gradient descent method to get minimal error values of loss functions close to their Bayesian estimators. We calculated Bayesian estimators mathematically for different loss functions and tested them using gradient descent algorithm. This algorithm, working on Normal and Poisson distributions showed that it is possible to find minimal error values without having Bayesian estimators. Using Python, we tested the theory on loss functions with known Bayesian estimators as well as another loss functions, getting results proving the theory.
Simulator use on competence

Utilization of a bridge simulator that provides an accurate picture of conditions such as on the bridge of a ship to increase the competence that is incorporated in the navigation function. This survey research takes samples from the population and uses a questionnaire as the primary data collection instrument. The sample used in this study was cadets majoring in marine engineering at the Makassar Shipping Science Polytechnic campus. Collecting data using the questionnaire method, and for data analysis by simplifying all data and then presenting it in a systematic arrangement, after that interpreting or interpreting the data obtained. By practicing using a simulator, cadets can practice and understand the function of the material whose scope is about Radar (radio detection &amp; ranging), AIS (Automatic Identification System), GPS, and ECDIS.