Reviewerly/exHarmony huggingface dataset:
Propuesta para el acompañamiento a los estudiantes de nuevo ingreso, por parte del personal del Centro de Estudios Generales, en el marco de la coyuntura postpandemia

Considering the present juncture that the educational system is going thru after the Pandemic of Covid-19, which ultimately caused a series of restrictive measures by the Ministry of Education, and previously, by a series of strikes in the public sector, it was assumed that students of recent entrance in the Centre for General Studies (Centro de Estudios Generales, CEG) could present an educational lag. This could consequently generate some apprehension and uncertainty, and possibly wider student during the academic year 2020. Considering such eventuality, a special Conference titled in the same manner as this article was organized; such event was geared towards the educational and administrative body of the CEG with the main purpose of creating some bio-pedagogical instruments for sensitizing professors with respect to posible traumas of the new post-Pandemic students. In this article we present the essential basis of the Conference grounded upon the Theory of emotional intelligence by Daniel Goleman, as an ideal resource for confronting such adverse circumstances. All these aspects are approached using the bio-pedagogic emergent paradigm. First, we detail some contextual factors, and secondly we follow with the topic of educational lag, the basis for resilience, after critical events, emotional intelligence, and bio-pedagogical didactics.
Intelligent level evaluation method of the self-driving vehicle based on CRITIC-cloud model

In order to evaluate the intelligence level of self-driving vehicles, a based CRITIC-cloud model is proposed. The intelligence level evaluation index system is established from perception, planning, and control. The CRITIC weight method is used to assign weights to each evaluation index. Taking advantage of the excellent advantages of the cloud model that can perform a good conversion between qualitative concepts and their corresponding quantitative data, the data displayed by self-driving vehicle for running process is transformed into a more intuitive and visual concept of intelligence level through a cloud model, The expectation, entropy and hyper entropy of the cloud model are used for qualitative evaluation of the self-driving vehicle, to establish a qualitative to quantitative evaluation model, which provides a judgment for the self-driving vehicle.
A Compact Wearable Data Glove Based on Flexible Beam Sensors

Collecting finger joint angle by data gloves has broad application prospects in interpersonal interaction and remote operation. Typical conventional approaches use heavy rigid mechanism, fatigable silicone frame or complex vision algorithm. In this paper, a new lightweight wearable data glove device based on resistive flexible beam deflection sensors is proposed. 5 resistive strain sensors are attached to the back of a carbon fiber beam, which is fixed with the finger at its tip and root. The beam is modeled as discretized segments linked by flexible joints based on the Principle Axes Decomposition theory. A linear system of geometric and force equations is later established through the Produce-of-Exponential (POE) formula. Afterwards, 5 local curvatures of the beam are derived form linear relations between sensors’ resistance and bending curvatures, then sent to the linear system to reconstruct beam posture. The finger joint postures is finally obtained through the Pythagorean theorem. Calibration experiments are conducted to determine the linear coefficient between resistance and curvature of the sensors. Experimental results show that the best accuracy of reconstructing finger joint angles for an plastic index finger model are 5.8mm, 7.5° for endpoint positions and 7.3° for joint angles. Through wearing tests, the entire measuring device doesn’t restrict the movement of fingers. These results indicate that the flexible beam sensor is efficient and convenient for collecting finger joint angles.
Vehicle autonomous return based on 2D grid map

This paper focuses on the research of autonomous return home for unmanned combat platforms, and proposes an autonomous return home method based on 2D grid maps. In the process of map construction, wheel odometer and IMU information are introduced, and EKF is used to fuse the data of the two. The experiment shows that this method can make the grid map match the actual environment better. In the return home process, based on the created map, a heuristic algorithm is used to plan the global path, and a dynamic window method is used to plan the local path. Finally, an autonomous homing experiment was conducted on the Ackermann steering robot to verify the effectiveness of the proposed autonomous homing method.
Zero-Shot Human-Object Interaction (HOI) Classification by Bridging Generative and Contrastive Image-Language Models

Existing studies in Human-Object Interaction (HOI) classification rely on costly human-annotated labels. The goal of this paper is to study a new zero-shot setup to remove the dependency on ground-truth labels. We propose a novel Heterogenous Teacher-Student (HTS) framework and a new loss function. HTS employs a generative pretrained image captioner as the teacher and a contrastive pre-trained classifier as the student. HTS combines the discriminability from generative pre-training and efficiency from contrastive pre-training. To facilitate learning of HOI in this setup, we introduce pseudo-label filtering which aggregates HOI probabilities from multiple regional captions to supervise the student. To enhance the multi-label learning of the student on few-shot classes, we design LogSumExp (LSE)-Sign loss which features a dynamic gradient re-weighting mechanism. Eventually, the student achieves 49.6 mAP on the HICO dataset without using ground truth, becoming a new state-of-the-art method that outperforms supervised approaches. Code is available.
Pengaruh Model Problem Bassed Learning Berbantuan Media Audio Visual Terhadap Keterampilan Memproduksi Teks Ceramah

There are three purposes of this research. First, describe the skills of producing lecture texts for class XI students of SMA Negeri 2 Pariaman before applying the Problem Based Learning (PBL) Model with Audio Visual Media Assistance. Second, describe the skills of producing lecture texts for class XI students of SMA Negeri 2 Pariaman after applying the Problem Based Learning (PBL) Model with Audio Visual Media Assistance. Third, analyze the effect of applying the Problem Based Learning (PBL) Model with Audio Visual Media Assistance on the skills of producing lecture texts for class XI students of SMA Negeri 2 Pariaman. This type of research is quantitative research. The research method applied is descriptive analysis method. The procedures of this research are: (1) pretest, (2) treatment, and (3) posttest. The population in this study were students of class XI SMA Negeri 2 Pariaman who were registered in the 2018/2019 school year as many as 258 students. The sample in this study were students of class XI IPA-5, totaling 30 students. Based on the results of data analysis found. First, the skills of producing lecture texts for class XI IPA-5 students of SMA Negeri 2 Pariaman before the implementation of the Problem Based Learning (PBL) Model with Audio Visual Media Assistance were in almost sufficient qualifications (55,57). Second, the skills of producing lecture texts for class XI IPA-5 students of SMA Negeri 2 Pariaman after the implementation of the Problem Based Learning (PBL) Model Assisted by Audio Visual Media are in good qualifications (77.97). Third, there is an effect of applying the Problem Based Learning (PBL) Model Assisted by Audio Visual Media on the skills of producing lecture texts for class XI IPA-5 students of SMA Negeri 2 Pariaman after the t-test at level 0.95, obtained tcount &gt; ttable or 5.68 &gt; 1.70.
Case Study of Memphis Inc.'s Cybersecurity Breach

In a world where cybersecurity breaches could cause reputational and financial loss, understanding how an organization can recover is paramount. This case study will cover an organization's experience with a cybersecurity breach due to poor security posture and risk management. The organization employs a risk management consulting firm to help rebuild the culture utilizing strategic, leadership, organizational culture, and change management models to implement a risk management program. Designing a program that includes a framework, third-party validation, and maintaining security also includes the implementation and adoption process by senior leadership and employees. This case study will review these tools and give the business and its customers the confidence to move forward after a breach. Any names used in this case study are aliases and do not represent the real names of individuals or organizations involved.
FedShop: A Benchmark for Testing the Scalability of SPARQL Federation Engines

While several approaches to query a federation of SPARQL endpoints have been proposed in the literature, very little is known about the effectiveness of these approaches and the behavior of the resulting query engines for cases in which the number of federation members increases. The existing benchmarks that are typically used to evaluate SPARQL federation engines do not consider such a form of scalability. In this paper, we set out to close this knowledge gap by investigating the behavior of 4 state-of-the-art SPARQL federation engines using a novel benchmark designed for scalability experiments. Based on the benchmark, we show that scalability is a challenge for each of these engines, especially with respect to the effectiveness of their source selection & query decomposition approaches. FedShop is freely available online at: https://github.com/GDD-Nantes/FedShop .
EXAMINING THE CONCEPT OF OCCUPATIONAL BURNOUT AND THE LEVEL OF OCCUPATIONAL BURNOUT IN TERMS OF DEMOGRAPHIC VARIABLES

Purpose- The aim of the study is to examine how the level of occupational burnout is affected by demographic variables. Methodology- Occupational burnout level was examined in terms of demographic variables. Demographic variables; gender, marital status, age, education level of the person, monthly income level, working time in the current institution (seniority period) and total seniority in business life. The Maslach Burnout scale was used to measure the dimensions of professional burnout, and the three dimensions of the scale, emotional exhaustion, depersonalization, and the feeling of decrease in personal achievement, and demographic variables were examined. Questionnaire method was used. Findings- According to the results of the research, demographic variables have an impact on burnout in general. A high level of relationship was found between income level, gender, age, seniority of the person and professional burnout. A moderate relationship was found between marital and educational status and professional burnout. Among the dimensions of occupational burnout, the dimension most affected by demographic variables is the dimension of emotional exhaustion. The highest effect is between income level and gender demographic variables and emotional exhaustion dimension. Conclusion- As a result, due to the fact that demographic variables have an effect on occupational burnout, the result of the study emerges that businesses should consider the demographic variables while considering the occupational burnout status of the employees. The high effect of income level and gender variables on occupational burnout is important. Considering these differences and considering the demographic variables in general, reducing the level of professional burnout, which has a high impact on the performance of the employees, has a high importance in terms of improving the performance of the enterprises and studies should be carried out in this direction. Keywords: Occupational burnout, demographic variables, feelings of emotional exhaustion, depersonalization and decreased personal achievement. JEL Codes: O15, M11, M12
Text to Speech for Dzongkha Language

Text to Speech plays a vital role in imparting information to the general population who have difficulty reading text but can understand spoken language. In Bhutan, such system were not available and building a system will leverage the use of the language by different segment of people. This article describes an attempt to create a functioning model of a Text to Speech system for the Dzongkha language by creating a transcription or grapheme table for phonetic transcription from Dzongkha text to its comparable phone set. The transcription tables for consonants and vowels have been produced in such a way that they allow for improved computer compatibility. 3000 phrases were painstakingly transcribed and recorded using a single male voice. On the FESTIVAL platform, the voice synthesis was based on a statistical approach with concatenative speech creation. The model is generated using the two variants CLUSTERGEN and CLUNITS of the FESTIVAL speech tools FESTVOX where the earlier method produce more natural speech than the later for the large data set. The development of system prototype is of the first kind for the Dzongkha language in spite of attempts being made by researchers.
A Study on the Instructional Function of Korean Questionnaire

The purpose of this paper is to examine the instructional function of interrogative in the Korean conversation. First of all, in this paper, from the perspective of ‘Systemic Functional Linguistics’, ‘instructions’ is viewed as a type of utterance ‘function’, and it is set as a concept distinct from ‘question’ or ‘request’. In addition, the subcategories of ‘instructions function’ are divided into the intention of utterance such as ‘suggestion’, ‘command’, ‘demand’, ‘prohibition’, and ‘caution’. Each function is confirmed through its use in the corpus of all provided by the National Institute of Korean Language. In addition, it was judged by 30 experts and as a result, it showed a high coincidence rate of the correct answers. It means that the instructions function of the interrogative is already a familiar strategy for native Korean speakers and can be understood by any Korean. As a result, each instructions function represented by the interrogative could be confirmed in the actual corpus. This study is significant in that it analyzed the instructions function of interrogative by referring to the perspective of conversation analysis research from the perspective of Systemic Functional Linguistics, and that the results of the study can be used for Korean language education.(Yonsei University)
Artificial Intelligence Stimulus Response System

The Stimulus Response System (SRS) is based on the concept of artificial intelligence (AI) based mark-up language. (AIML) and is used to train the system for recognizing the user input. By making use of the previously built knowledge base this system is trying to analyze the user input and it provides the response. The Chatter Bot’s, reported in the literature, and was used mainly the indexing technique. The method is strongly dependent on the contents that are stored in a file or a database. Thus there is a need to develop a new system that allows you to carry on conversations with the system as long as possible. Stimulus Response System creates and adds knowledge to a computer. As a result, the computer will appear as human. The main objective of the work is to design an interactive software package which make the computer simple by building content that induces the client to carry on interactive conversations with the system as long as possible.
Integrity Audition of Shared Data on Cloud with User Privacy Preserving

This paper introduces a robust solution, "Secure Data Storage and Auditing," aimed at addressing contemporary challenges in data security and integrity verification within cloud environments. Through an integrated system of modules, the project achieves secure data storage through encryption and cryptographic tagging, followed by independent data integrity audits. Users' privacy is preserved as the Third-Party Auditor (TPA) verifies data without direct access to content. The project demonstrates effective data management, user privacy preservation, and real-time auditing, contributing to data security in cloud-based infrastructures. The methodology showcases the project's systematic approach to module integration, ensuring a comprehensive solution for secure data management and accountability.
An Automatic Calibration Method for the Field of View Aberration in a Risley-Prism-Based Image Sensor

Risley-prism-based image sensors can expand the imaging field of view through beam control. The larger the top angle of the prism, the higher the magnification of the field of view, but at the same time, it aggravates the problem of imaging aberrations, which also puts higher requirements on the aberration correction method for the Risley-prism-based image sensor. To improve the speed, accuracy, and stability of the aberration correction process, an automatic calibration method for the Risley-prism-based image sensor is proposed based on a two-axis turntable. The image datasets of the calibration plate with different prism rotation angles and object distances are acquired using a two-axis turntable. Then, the images of the calibration plate are pre-processed using the bicubic interpolation algorithm. The calibration parameters are finally calculated, and parameter optimization is performed. The experimental results verify the feasibility of this automated calibration method. The reprojection error of the calibration is within 0.26 pixels when the distance of the imaging sensor is 3.6 m from the object, and the fine aberration correction results are observed.
The Best is Yet to Come: A Reproducible Analysis of CLEF eHealth TAR Experiments

The CLEF eHealth Technology Assisted Reviews (TAR) in Empirical Medicine Tasks focused on evaluating the effectiveness of various technology-assisted review systems in assisting healthcare professionals in retrieving relevant information from vast amounts of medical literature. It ran for three years, from 2017 until 2019, giving the opportunity to research groups to conduct experiments and share results on automatic methods to retrieve relevant studies with high precision and high recall. In this paper, we perform a reproducibility study of one of the top-performing systems of both the years 2018 and 2019 by rerunning the original code that was provided by the authors of the paper. The goals of this paper are 1) to document the pitfalls in the description of the code, 2) to reorganize the code using a better reproducibility approach (R markdown), 3) to propose some minor changes to the code that would improve the performances of the system.
Development of a Model for Transversal Competence Assessment in K-12: an Internal Validation Study for the Digital Competence

While competence-based education is gaining momentum in K-12, integration into the curriculum is still challenging. We present a Competence Assessment Model that supports creating scenarios that use several active methods for gathering learning evidence and implementing assessment rules. The study adopts a design and development research approach that spans three phases of conceptual development and model internal validation. The usability documentation method was applied to digital competence as a test case involving teachers, learning designers, and researchers across six European countries. Results confirm the model’s effectiveness for supporting competence assessment design and identify key organisational issues and teacher challenges for its adoption.
DIGITAL PATHOLOGY AND AI: A PARADIGM SHIFT IN PATHOLOGY EDUCATION

From transferring microscope images to telepathology and whole-slide imaging scanners, digital pathology's evolution spans a century. Today, digital pathology and AI are revolutionizing medical education. With digital pathology, students can access a vast repository of virtual slides, enabling them to study diseases and conditions more comprehensively. AI enhances this by aiding in image analysis, diagnosis, and pattern recognition, providing students with valuable insights and preparing them for real-world challenges in medicine. Recent advancements strongly impact its adoption, influencing medical education. Digital pathology streamlines data storage, remote communication, and AI potential. Interactive AI-driven platforms offer tailored learning experiences, fostering critical thinking and are adaptable to each student's pace and need. Integrating digital pathology and AI transforms medical education, enabling comprehensive disease study through virtual slides. This fusion adequately prepares the physicians of tomorrow, enhancing practical expertise and pathology education. The future sees AI and digital pathology integral to medical education, equipping the future physicians to navigate evolving technology and deliver exceptional care. Embracing this duo ensures readiness for the dynamic healthcare landscape
El radioteatro como experiencia humanista en los procesos de enseñanza y aprendizaje en modalidad presencial remoto

Due to the consequences of the Pandemic, the Centre for General Studies has modified its face-to-face academic activities migrating towards a remote attendance mode, betting on digital alphabetization with a deep commitment to excellence in an uncertain context, which represented a spectrum of educational opportunities supported by technological resources.&#x0D; Creative and scenic games used during the teaching experience, facilitated a sort of practical lab for ne expressive languages from a humanist standpoint in which actual practice, collective interactions, playful relations, are the essential pillars for the new learning processes.&#x0D; The new methodological approach generated processes of research and training that encompass significant possibilities for adaptability and creative skills thanks to the use of the technique known as Radio-theater, which consists in developing performance skills and digital capacities to produce quality scenic material and critical content using theatrical and technological resources.
Descripción de las tendencias del comportamiento, las relaciones sociales y la vida cotidiana, de la población estudiantil universitaria en el contexto de la pandemia del COVID-19

The current social challenges of contact and relationships with other people in the face of the necessary protection from the risk of contagion of the coronavirus and life in capital letters, requires an understanding on the part of higher education authorities worldwide. This article describes the behavior trends, in social relationships and daily life, by the student population from the National University of Costa Rica (UNA), Chorotega Branch Campus Liberia, in the context of the COVID-19 pandemic. 182 students were involved, under a descriptive phase quantitative study. The results of the study indicate that the COVID-19 pandemic caused social distancing in the population under study, altering trends in behavior, lifestyle, which in the face of confinement abruptly modified social contact, causing an exponential increase in the use of technological tools to mediate communication.