Reviewerly/exHarmony huggingface dataset:
VLSP 2021-ViMRC Challenge: Vietnamese Machine Reading Comprehension

One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswerable for which the correct answer is not stated in the given textual data. To address the weakness, we provide the research community with a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a benchmark dataset for the challenge on Vietnamese MRC at the Eighth Workshop on Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77 participant teams from 34 universities and other organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 77.24% in F1-score and 67.43% in Exact Match on the private test set. The Vietnamese MRC systems proposed by the top 3 teams use XLM-RoBERTa, a powerful pre-trained language model based on the transformer architecture. The UIT-ViQuAD 2.0 dataset motivates researchers to further explore the Vietnamese machine reading comprehension task and related tasks such as question answering, question generation, and natural language inference.
Implementation of K-Means Clustering Method for Trend Analysis of Thesis Topics (Case Study: Faculty of Computer Science, University of Jember)

The development of information technology causes a large number of digital documents, especially thesis documents, so that it can create opportunities for students to take the same and not varied topics. Thesis documents can be grouped by topic by identifying the abstract section. The results of the grouping can be seen with the trend with data visualization so that it can be analyzed to find out the trend of each topic. Retrieval of data in the repository of the University of Jember through a web scraping process as many as 490 thesis documents for students of the Faculty of Computer Science, University of Jember. The preprocessing stage is carried out by text mining methods which include cleaning, filtering, stemming, and tokenizing. Then calculate the weight of each word with the Term Frequency - Inverse Document Frequency algorithm, followed by the dimension reduction process using the Principal Component Analysis algorithm, which is normalized by Z-Score first. The outliers removal process is carried out before classifying documents. Furthermore, document grouping uses the K-Means Clustering method with Cosine Similarity as the distance calculation and the Silhouette Coefficient algorithm as a test. The test results were carried out with various k values and the optimal value was obtained at k = 2 with a Silhouette value of 0.80. Then the topic detection uses the Latent Dirichlet Allocation algorithm for each cluster that has been formed. Each cluster is visualized with a line chart and Trend Linear algorithm and analyzed to find out the trend. From the results of the analysis, it can be concluded that the topic of Decision Support System Development is trending down, and the topic of IT Performance Measurement and Forecasting is trending up. It can be concluded that the topic of Decision Support System Development needs to be reduced so that other topics can emerge.
Pedagogical discourse and communication ethic in policultural virtual educational environment

The COVID-19 pandemic has served as a catalyst for the transition of education to an online format. Clearly, most educational institutions and teachers were forced to adapt methods and forms of online educational process on the short notice. The issue of online education has long been considered from the scientific and practical sides, however, such a quick and forced transition to online revealed the problems, features and potential of online communication. The ethics of online communication in education is one of the most significant issues. The development of the information environment initiated the emergence of an educational multicultural environment, which entailed a certain kind of systemic changes, which, one way or another, will be reflected in the transformation of the elements of the information and educational environment (IEE) organization. This applies to both the traditional format of education and online educational practices. There is a number of specific features of multicultural educational environment that do not occur in monocultural environments: non-identical professional thesauri, various habitual communication patterns with a teacher, representation of educational information and educational content specifics, decision-making specifics, the attitude to creative tasks performing in the process of learning, ambiguous understanding of learning objectives, terminology, preferred type of control and measuring materials, etc.
Our Voice in a rural community: empowering Colombian adolescents to advocate for school community well-being through citizen science

Abstract Background Santa Ana is home to an Afro-descendant rural population of the island of Barú in Cartagena, Colombia. While a popular area for tourism, Santa Ana’s population is affected by multidimensional poverty, precarious work conditions, homelessness, broken streets and sewer systems, limited quality education, and a lack of recreation and sport spaces. While Santa Ana’s Community Action Board aims to unify efforts and resources to solve these problems, the state’s capacity to meet the requirements of the Board is limited. Methods We evaluated the relationship between healthy lifestyles and characteristics of Santa Ana’s school using the Our Voice Citizen Science Research Method. This systemic approach combines information and communication technologies with group facilitation to empower adolescents to: 1) collect and discuss data about factors in their local environments that facilitate or hinder well-being within their school community; 2) identify relevant local stakeholders who could help to address the issues identified; and 3) advocate collectively for local improvements to support increased well-being at a community level. Results Eleven citizen scientists ages 13 to 17 years from the science club of Institución Educativa Santa Ana were recruited and together conducted 11 walks within the school to collect data about the facilitators and barriers to student well-being. They identified barriers to well-being related to school infrastructure, furniture, bathrooms, and sense of belonging. They then advocated with school stakeholders and reached agreements on concrete actions to address identified barriers, including fostering a culture among students of caring for school property and presenting their findings to the community action board. This methodology allowed the community to realize how students can become agents of change and take collective action when motivated by solution-oriented methodologies such as Our Voice. Project ripple effects, including greater empowerment and participation in collective actions by students, also were observed. Conclusions This study underscores the importance of the school’s built environment in the well-being of students in rural areas. The Our Voice method provided the opportunity to inform school-based interventions, and promoted ripple effects that expanded productive dialogue to the community level and generated systemic actions involving actors outside of the school community.
Qubit Assignment Using Time Reversal

The Loschmidt echo, a method for probing reversibility in quantum systems, is used in conjunction with simulated annealing to provide a scalable and flexible approach to optimizing qubit assignment on near-term hardware.
Domain Adaptation with Augmented Data by Deep Neural Network Based Method Using Re-Recorded Speech for Automatic Speech Recognition in Real Environment

The most effective automatic speech recognition (ASR) approaches are based on artificial neural networks (ANN). ANNs need to be trained with an adequate amount of matched conditioned data. Therefore, performing training adaptation of an ASR model using augmented data of matched condition as the real environment gives better results for real data. Real-world speech recordings can vary in different acoustic aspects depending on the recording channels and environment such as the Long Term Evolution (LTE) channel of mobile telephones, where data are transmitted with voice over LTE (VoLTE) technology, wireless pin mics in a classroom condition, etc. Acquiring data with such variation is costly. Therefore, we propose training ASR models with simulated augmented data and fine-tune them for domain adaptation using deep neural network (DNN)-based simulated data along with re-recorded data. DNN-based feature transformation creates realistic speech features from recordings of clean conditions. In this research, a comparative investigation is performed for different recording channel adaptation methods for real-world speech recognition. The proposed method yields 27.0% character error rate reduction (CERR) for the DNN-hidden Markov model (DNN-HMM) hybrid ASR approach and 36.4% CERR for the end-to-end ASR approach for the target domain of the LTE channel of telephone speech.
EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for En-Gineering Applications

Jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a new developed meta-heuristic algorithm that solves complex and real world optimization problems. The global explore capability and robustness of JS are strong, but JS still has great development space in solving complex optimization problems with high dimensions and multiple local optima. Therefore, an enhanced jellyfish search (EJS) algorithm is developed in this study, and three improvements are made: (i) By adding sine and cosine learning factors, the jellyfish can learn from both random individual and best individual during Type B motion in swarm to enhance the optimization capability and convergence speed; (ii) Adding local escape operator can skip local optimal trap and boost the exploitation ability of JS; (iii) Opposition-based learning operator and quasi-opposition learning operator can increase and strengthen the population distribution more diversified, and better individuals are selected from present and new opposition-solution to participates in the next iteration, which can boost the solution&amp;rsquo;s quality, meanwhile convergence speed is fasted and its precision is increased. In addition, the performance contrast of the developed EJS and some previous outstanding and advanced methods are evaluated on CEC2017, CEC2019 test suite and six real engineering example of case. It is demonstrated that EJS algorithm escaped the trap of local optimum, enhanced the solution&amp;rsquo;s quality and the calculation speed. What&amp;rsquo;s more, the practical engineering applications of EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and it stretched one&amp;rsquo;s mind for solving such optimization problems.
A Deep Learning Enabled Chatbot Approach for Self-Diagnosis

Abstract: Healthcare is an essential need that is often inaccessible or unavailable. With rising populations and deadly viruses, the load on healthcare workers is the highest, while the ratio of healthcare workers per person has hit an all time low. This extreme demand for assistance in the face of unavailability has opened up avenues for computer assisted healthcare. In this paper, we propose an approach to use advancements in computer assisted healthcare in two directions. We employ machine learning algorithms to predict the disease correctly. We used Random Forest and XGBoost as this system's core classifiers that gave an accuracy of 100% and 99.0% correspondingly. The chatbot is used to trigger the appropriate function for the disease prediction concluding from the conversation with the user. It has an accuracy of 97.22%. Overall our chatbot has the potential to provide immediate assistance to those who are in need, and in turn, reduce the burden on healthcare workers.
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?

This work presents a detailed linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading times. First, regression analyses show a strictly monotonic, positive log-linear relationship between perplexity and fit to reading times for the more recently released five GPT-Neo variants and eight OPT variants on two separate datasets, replicating earlier results limited to just GPT-2 (Oh et al., 2022). Subsequently, analysis of residual errors reveals a systematic deviation of the larger variants, such as underpredicting reading times of named entities and making compensatory overpredictions for reading times of function words such as modals and conjunctions. These results suggest that the propensity of larger Transformer-based models to 'memorize' sequences during training makes their surprisal estimates diverge from humanlike expectations, which warrants caution in using pre-trained language models to study human language processing.
Deterministic controlled enhancement of local quantum coherence

We investigate assisted enhancement of quantum coherence in a bipartite setting with control and target systems, which converts the coherence of the control qubit into the enhanced coherence of the target qubit. We assume that only incoherent operations and measurements can be applied locally and classical information can be exchanged. In addition, the two subsystems are also coupled by a fixed Hamiltonian whose interaction strength can be controlled. This coupling does not generate any local coherence from incoherent input states. We show that in this setting a measurement and feed-forward based protocol can deterministically enhance the coherence of the target system while fully preserving its purity. The protocol can be iterated and several copies of the control state can be consumed to drive the target system arbitrarily close to a maximally coherent state. We experimentally demonstrate this protocol with a photonic setup and observe the enhancement of coherence for up to five iterations of the protocol.
Data Competence of Subjects of Pedagogical and Managerial Analytics for Evidence-based Development of Education

The relevance of the study is associated with the need to analyze the changing labor actions of education leaders and teachers in connection with the organization of the analysis of educational data accumulated by students in the digital educational environment. This, in turn, actualizes the need for the development of competencies in the analysis of educational data for a reasonable organization of human development and education. In the context of the digital transformation of education, the development of digital educational platforms, the development of digital services for assessing the quality of education, monitoring education, the issues of the methodology of data analysis and their competent application in pedagogical and managerial practice are being actualized. The article examines how educational analytics is used in the practice of schools, what problems educational leaders experience in pedagogy when working with data, what data sources they use. It was studied: what competencies are necessary for a teacher and the head of an educational organization in order to organize evidence-based development of education based on data. Leading research methods: structural and functional analysis, interviewing, polling, systematization, ranking, statistical analysis. The article summarizes the results of a study of the changing labor functions of teachers in the context of the digitalization of education, the competencies of teachers and managers in the application of data analysis in educational practice: digital competencies (digital competencies), data analysis competencies (data competencies), competencies for organizing development based on data (competence of a data-driven development organization). The problem is revealed: how the methodology and labor functions of teachers in the organization of education change based on the analysis of data: educational results, motivational choices of training profiles, levels of difficulty of educational tasks, topics of design and research work of schoolchildren, etc. Expansion of labor actions of teachers and heads of educational organizations and competences that ensure the implementation of labor actions are investigated. The development of data-competencies of teachers and education leaders for the evidence-based development of education and a person is analyzed. The research results presented in the article contribute to the theory and practice of teacher education, education management, as well as to the methodology of data science and the practice of its application in the field of education.
Aplikasi Terintegrasi Registrasi Torche Education Menggunakan Framework Django

The growth of frameworks for web application development is very rapid, especially on the back side (server) of web development or what is commonly called the backend. Several programming languages ​​offer frameworks that have their own advantages, such as the Python programming language which offers a web application development framework, namely Django. It is equipped with the fast development, fast processing, and scalability. The data collection process was carried out by researching several related sources regarding the modules in the Django framework. The research process was carried out by finding sources of information about the Django framework to support the design of the Torche Education class registration application. Sources of information are collected to become a reference for application development. The Torche Education class registration application was built during the internship process at the company as it is concerned with developing the application according to predetermined working hours. It started by conducting research on the modules in the Django framework for developing registration applications and then followed by implementing the framework and integrating the application into several platforms such as Google Drive, Discord, and Gmail. Based on the application development above, the backend framework technology has been implemented in Python programming, namely Django. The results and benefits of the research can be useful for facilitating related parties in the process of recruiting students for class registration and making it easier for users to get fast information.
Emotional Intelligence of Digital Generation Students: Experience of Engineering University

Digitization of everyday life has a fundamental influence on communications, information perceiving and thinking styles of modern generation students. The study of issues of emotional well-being in the student body is particularly significant today, because of increasing desocialization of young people. The aim of the paper is to implement and evaluates the effectiveness of the educational module for the development of emotional intelligence of the 1st year students of engineering Universities. The authors developed the educational module “Emotional intelligence”. The assessment of an empirical research using diagnostic tools of N. Hall’s test of emotional intelligence and the scale of emotional intelligence by D.V. Lyusin conducted on a sample of students (N=174) of engineering majors of Tomsk Polytechnic University. The results showed that most part of them has low and medium (closer to the lower border) levels of EQ. Positive dynamics were observed after studying the educational module. The quantitative indicator reflecting the number of students with a low level decreased two times, and the proportion of students with a high level of EQ increased from 2.9% to 11.1%. The maximum growth was noted for “empathy”, “recognition and understanding of emotions of others” (Hall’s test) and “interpersonal emotional intelligence” (D.V. Lyusin’s scale) indicators. The practically significant result is that development of the level of EQ will contribute to better adaptation in the educational space of the engineering University. The findings will be used for further scientific research to improve the educational paths of digital generation students.
The Use of Big Data Analytics in Detecting Academic Fraud

This study aims to find out the effect of using big data analytics on the detection of academic fraud so that it can provide improvements and create significant changes, especially in reducing the level of academic fraud among students. The variables used in this research are big data analytics as the independent variable and academic fraud as the dependent variable. This study uses primary data obtained from ques-tionnaires distributed to Trilogy University students. The sample is 258 students from all study programs at Trilogy University class 2017 - 2020. The data processing and analysis method uses Partial Least Square (PLS). The results of this study indicate that the use of big data analytics has a positive and significant effect on the detection of academic fraud. This shows that universities that use big data analytics are able to detect academic fraud committed by students.
Success concepts for causal discovery

Abstract Existing causal discovery algorithms are often evaluated using two success criteria, one that is typically unachievable and the other which is too weak for practical purposes. The unachievable criterion—uniform consistency—requires that a discovery algorithm identify the correct causal structure at a known sample size. The weak but achievable criterion— pointwise consistency—requires only that one identify the correct causal structure in the limit. We investigate two intermediate success criteria— decidability and progressive solvability —that are stricter than mere consistency but weaker than uniform consistency. To do so, we review several topological theorems characterizing which discovery problems are decidable and/or progressively solvable. These theorems apply to any problem of statistical model selection, but in this paper, we apply the theorems only to selection of causal models. We show, under several common modeling assumptions, that there is no uniformly consistent procedure for identifying the direction of a causal edge, but there are statistical decision procedures and progressive solutions. We focus on linear models in which the error terms are either non-Gaussian or contain no Gaussian components ; the latter modeling assumption is novel to this paper. We focus especially on which success criteria remain feasible when confounders are present.
Asthma Monitoring: Respiratory Monitoring System for Asthma Patients Based on the Internet of Things

Air is one of the most important things in human life. However, the denser the activity that triggers the growth of the air. Asthma is one of the main respiratory problems. Medically, asthma is difficult to cure, it's just that this disease can be controlled so it doesn't interfere with daily activities. This study aims to create a model for monitoring the respiratory rate of asthma patients. The stages in developing an asthma monitoring tool include the stages of knowing component needs, component design, program development, testing and evaluation. The respiratory rate of asthmatic patients can be calculated using the LM35 temperature sensor. The frequency value to be displayed in the web browser uses an Ethernet shield, which is very helpful for doctors to access patient information from anywhere in the hospital. Then by using data mining techniques, to find out the health status of patients without medical professionals helping. With the help of this system, disease detection can be done earlier.