Reviewerly/exHarmony huggingface dataset:
Academic reading challenges and strategies used by Chinese undergraduate students in a selected private university in Malaysia

Academic reading is an essential skill required of higher education for students and academic researchers. However, Chinese students lack reading skills and do not receive adequate reading instruction in academic reading. This study attempted to identify the academic reading challenges among Chinese students who study at UCSI University and to investigate the appropriate reading strategies employed by them. While being informed by the Sociocultural Theory of Learning (Vygotsky, 1978), the study used a mixed-methods research design which collected quantitative data from questionnaires and qualitative data from semi-structured interviews. Findings showed that the Chinese students lacked mastery of academic vocabulary and reading skills, typically in finding information. Another finding also showed that the Chinese undergraduate students used both cognitive strategies and metacognitive strategies in academic reading. The findings could help Chinese undergraduate students overcome their reading difficulties and propose suggestions to teachers on how to teach reading more effectively.
Comparing Rotation Forest Model And Enhanced Random Forest Model On Imbalanced Data (Application To Classification Of Poverty Households In Sampang Regency, 2019)

&lt;p class="Els-Abstract-text"&gt;&lt;em&gt;The first priority of the SDGs is poverty eradication (no poverty). In the Indonesian context, poverty cases has a high correlation with the profession in agriculture sector. For example, in the 2019 Susenas data in Sampang Regency, more than 80% of the sample households categorized as poor have a household head who works in agriculture. Poverty alleviation efforts always begin with identifying/classifying poverty households/families. However, data on poor households is usually unbalanced data that requires special handling in its analysis. This study uses a classification models that are widely used today (in data science world), namely Random Forest and its development methods (Rotation Forest, and Enhanced Random Forest), in classifying poor and non-poor households. The results showed that the forest-based model studied had a low estimation ability when used in cases of unbalanced data, so an approach such as resampling technique was needed before carrying out the classification process. This study cannot conclude which one of the forest model is the most robust for unbalanced data or which method is the most suitable for the use of resampling techniques, but the results of the study show that the use of resampling techniques will improve the quality of the estimation results, especially on sensitivity side.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;&lt;p align="center"&gt; &lt;/p&gt;
Codeless App Development: Evaluating A Cloud-Native Domain-Specific Functions Approach

Mobile applications play an important role in the economy today and there is an increasing trend for app enablement on multiple platforms. However, creating, distributing, and maintaining an application remain expert tasks. Even for software developers, the process can be error-prone and resource-consuming, especially when targeting different platforms simultaneously. Researchers have proposed several frameworks to facilitate cross-platform app development, but little attention has been paid to non-technical users. In this paper, we described the Flow framework, which takes the advantage of domain-specific languages to enable no-code specification for app modeling. The cloud-native coordination mechanism further supports non-technical users to execute, monitor, and maintain apps for any target platforms. User evaluations were conducted to assess the usability and user experience with the system. The results indicated that users can develop apps in Flow with ease, but the prototype could be optimized to reduce learning time and workload.
Understanding the Role of Expert Intuition in Medical Image Annotation: A Cognitive Task Analysis Approach

To improve contemporary machine learning (ML) models, research is increasingly looking at tapping in and incorporating the knowledge of domain experts. However, expert knowledge often relies on intuition, which is difficult to formalize for incorporation into ML models. Against this backdrop, we investigate the role of intuition in the context of expert medical image annotation. We apply a cognitive task analysis approach, where we observe and interview six expert medical image annotators to gain insights into pertinent decision cues and the role of intuition during annotation. Our results show that intuition plays an important role in various steps of the medical image annotation process, particularly in the appraisals of very easy or very difficult images, and in case purely cognitive appraisals remain inconclusive. Overall, we contribute to a better understanding of expert intuition in medical image annotation and provide possible interfaces to incorporate said intuition into ML models.
Pair Analytics in a Visual Analytics Context

This case study details the development of “pair analytics” as practical approach to applied analysis and as a scientific research method. The hybrid research project itself was part of a larger research program approved for the Canadian government for their offset program and supported by Federal and Provincial research internships. As a real-world analysis approach, the pair analysis sessions conduced actionable causal chain analysis of aircraft safety. As a scientific method, pair analytics advanced our knowledge of the cognitive science of interpersonal communication in Joint Activities. The paper describes how aerospace researchers and cognitive scientists were able to design a research approach that met constraints from both areas. It concludes with discussion of the implications of this work for highly integrated basic and responsive research in other areas of visualization and analytics.
Augmented Reality Technology to Facilitate Proficiency in Emergency Medical Procedures

Background: Augmented reality (AR) conveys an experience during which the user’s real-time environment is enhanced by computer-generated perceptual information; it is being investigated as a solution to enhance medical education and clinical practice. There is little literature on its utility for teaching emergency procedures. Methods: A within-subjects trial was performed comparing traditional training to AR guidance for two emergency procedures. Lay-subjects and emergency medical technicians received video training and AR guidance for performing bag-valve-mask ventilation and needle-decompression. Subjects performed both procedures in a simulation setting after each training modality. Subject performance, acceptability and usability were analyzed. Results: There was no difference in procedural performance between lay or EMT subjects for AR training, and no difference in subject-reported usefulness between the AR and control training. Conclusion: AR mediated guidance for emergency medical procedures is feasible and efficacious. Subject performance after AR training was statistically undistinguishable from a didactic educational modality.
Management of digital transformation in the system of higher education: Global practice

The article discusses various approaches to management processes in the era of digital transformation. Digital challenges in the post-pandemic period have updated new qualification requirements for the higher education system. The transition to distance forms of work, which demonstrated inconsistency in providing quality education, revealed the main problems beginning from a weak infrastructure to the lack of optimal ways of educational activity. The higher education system requires transformation to become competitive in the digital environment and be flexible in assimilating technological innovations. The paper presents the analysis of the main directions of digitalization of higher education with the determined priorities. The study is designed on the methods of bibliometric analysis — counting of the number of publications on a given topic, their content and thesaurus analyses. There were used the built-in tools for analyzing the national bibliographic database of scientific citations and closed systems for searching and machine analysis of large texts of the abstract-analytical system Dimensions.ai. The VOSviewer application was used for the content analysis of the terms, and the resources wordstat.ru and google.trends.com were used to calculate the statistics of requests of the key concepts. The results of the analysis identified a variety of transformational decisions that can be made only at the level of strategic management. System transformations may depend on the given external or internal framework regulated by different subjects of the educational process. The directions of development in the field of digital transformation also depend on the external and internal framework. The article proposes the use of the collegial management format which changes the scheme of interaction between the subjects of education.
Enriched 360-degree videos in teacher education. Proposal of expert-based hotspots in 360-degree environments via eye-tracking and retrospective thinking aloud

Classroom recordings (CR) can be beneficial for the competence development of student teachers regarding the formation of professional teaching perception (PTP). Due to technical limitations, fixed-frame CR have so far only been able to depict a section of the lesson. 360-degree CR remove this technical limitation and thus create new possibilities for PTP development (especially Noticing). However, 360-degree videos can also overwhelm viewers. In order to use spherical CR purposefully for PTP education, guiding support is needed. This paper focuses on the research around the placement of optical aids (hotspots) in 360-degree CR. (DIPF/Orig.)
PELATIHAN PENGGUNAAN APLIKASI SPSS UNTUK MENUNJANG TUGAS AKHIR MAHASISWA

The training activity on the usage of the Statistical Analysis SPSS Application (Statistical Package for the Social Sciences) was conducted to enhance students' skills in analyzing data to support the completion of their final assignments. This article elucidates the methods and benefits of this training, tailored to suit the students' material needs, aiding them in finalizing their assignments. These sessions were conducted through a series of workshops accommodating students from diverse scientific backgrounds. The outcomes of these activities provided insights into the competency and proficiency regarding SPSS application use in enhancing students' statistical analysis skills. The implications of these activities could serve as a guide for higher education institutions in designing training programs that support the development of student’s academic skills in data analysis, thereby bolstering their final assignment requirements. The results of these activities demonstrate a significant improvement in students' understanding of statistical concepts, SPSS utilization, and the necessary data analysis skills required for final assignment research.
Implementation of association rule using apriori algorithm and frequent pattern growth for inventory control

Business success is a business that is able to compete and grow keep abreast of developments in the business world. Especially in the retail sector, where competition is getting tighter. Business owners need to pay attention to the layout of goods and stock management to improve service and meet consumer needs because consumers often have difficulty in finding goods. On the other hand, shortages and excess stock often occur due to lack of goods management. Based on these problems, appropriate techniques are needed for the management of goods supply, one of which is to apply techniques found in the branch of science. Data mining is a technique of association rules. This study aims to find patterns of placement and purchase of goods in generating Association Rule using FP-Growth algorithm. The dataset in this study used data on sales of goods in clothing stores. The results of the study of 140 transactions there are 24 association rules consisting of 7 association rules with 2-itemsets and 17 association rules with 3-itemsets that most often appear in transactions. Based on the order of the highest support value, namely CKJ→STX^LK with a support value of 67%, while the highest confidence value, there are 3 association rules that get the same value, namely STX^CKJ→LK, STX^CAK→LK, STX^RI→LK with a value of 100%. Thus, the rules of association produced by the frequent itemset algorithm, FP-growth, can serve as decision support for the sales of goods in small and medium-sized retail businesses
Aplikasi Daftar Cek Masalah (DCM) Bagi Anak Usia Dini di Wilayah Lahan Rawa

Guidance and counseling teachers must innovate and continue to upgrade themselves in relation to the provision of therapeutic services, considering the problems that occur in society today are very complex and require effective and efficient services. Therefore, it is necessary to develop a DCM (Problem Checklist) instrument application to make it easier for practitioners engaged in therapeutic services to sort out complex problems for counselees or students. The research method used in this research is research and development methodology by adopting the brog and gall stages which are simplified into 5 stages, namely: (1) preliminary study. (2) hypothetical development, (3) hypothetical model review, (5) hypothetical model revision. The research instrument used by the researcher is using a wide expert validation which includes 2 types of validators, namely: (1) expert validators in the field of guidance and counseling, and (2) practitioner validators to test products developed by researchers. The data analysis used in this research is qualitative analysis. The results of this study based on a qualitative validation test showed that this model was generally considered good. However, there are some improvements in the model developed by researchers based on input from experts, both academics and practitioners so that the model is really effective and efficient for use in the field. It is recommended to the next researcher to conduct a limited trial using this model.
IDENTIFY FACTORS AFFECTING STUDENT ACHIEVEMENT USING THE BINARY LOGISTIC REGRESSION ANALYSIS

The success of students in completing their study program is indicated by the achievements reflected in their Grade Point Average (GPA). Grade Point Average (GPA) is expressed in numerical form and is an academic achievement calculated based on all courses taken in all semesters that have been passed by students. This study aims to obtain a model of external factors that affect Grade Point Average (GPA) and variables that affect Grade Point Average (GPA). The response variable in this study is Grade Point Average (GPA) which is categorical and divided into two categories. The method used is binary logistic regression analysis. This study used primary data with the research subjects being second, third, and fourth-semester students in the Mathematics Education Department of Alauddin Makassar State Islamic University. The indicators on the instrument can be categorized as valid and reliable to measure service variables, learning, facilities and infrastructure, and student interest in entering college. The results of binary logistic regression analysis show that service has a significant effect on Grade Point Average (GPA), besides that learning, facilities, infrastructure, and the number of students enrolling in college have an effect but are not significant on Grade Point Average (GPA).
Perancangan Media Pembelajaran Interaktif Mata Pelajaran Tematik Bahasa Indonesia Untuk Siswa Kelas 1 Studi Kasus di SDN Leyangan

This Study aims to design interactive learning media in indonesian subjects, especially theme 2 sub-theme 4 lesson 5. Based on the identified students, that there are still students who are still not able to read and write fluently. This is due to factors from within the students themeselves such as the willingness and ability of the child. This type of research is a research and development research and development (R&amp;D) method. This study uses six stages: (1) Potential and problems, (2) gathering information, (3) product design, (4) design validation, (5) design revision, (6) product trial, (7) product revision. Validation was carried out by media experts (lecturers) and material experts (teachers). The subjects of the research trial consisted of grade 1 students at SDN Leyangan. The trial was conducted by 5 students. Data collected by interviews, documentation, and questionnaires (questionnaires). The data are the results of an assessment of product quality and suggestions for revising the product, which are then analyzed descriptively. &#x0D; The results showed that interactive learning media for Indonesian thematic subjects as a media tool designed to be suitable for use in learning theme 2 sub-theme 4 lesson 5 class 1 SDN Leyangan. This is shown by (1) the assessment of the media expert (lecturer) included in the very good criteria with a score of 37, (2) the assessment of the material expert (teacher) included in the very good criteria with a score of 36, and (3) in the trial use included in the very good criteria with a score of 36.
Pengembangan Media Pembelajaran Berbantuan Lectora Inspire dan Plotagon pada Materi SPLDV untuk Meningkatkan Kemampuan Berpikir Kritis Siswa

This research aims to describe the process and results of the development of learning media with 3D characters assisted by Lectora Inspire and Plotagon software to improve student's critical thinking skills in the material of systems for solving systems of linear equations in two variables. This research uses research and development methods with a 4D development model. This development model consists of four stages, namely definition, design stage, development stage, and dissemination stage. The learning media developed must meet the feasibility requirements, namely valid, practical, and effective. The results of this research show that the average validity value of the 3 validators is 90% and is included in the valid category. The level of practicality of learning media is 92% based on user questionnaire responsses and is included in the practical category. The level of effectiveness is determined based on the N-Gain results of 28 students who took the pretest and posttest with a percentage result of 79% so it is in the effective category. Based on these results, the learning media developed was proven to be valid, practical, and effective in improving students' critical thinking skills.
A Yaw tracking algorithm for head movement from inertial sensors data

Monitoring head movements is important in many aspects of life from medicine and rehabilitation to sports, and VR entertainment. In this study, we used recordings from two sensors, i.e. an accelerometer and a gyroscope, to calculate the angles of movement of the gesturing person’s head. For the yaw motion, we proposed an original algorithm using only these two inertial sensors and the detected motion type obtained from a pre-trained SVM classifier. The combination of the gyroscope data and the detected motion type allowed us to calculate the yaw angle without the need for other sensors, such as a magnetometer or a video camera. To verify the accuracy of our algorithm, we used a robotic arm that simulated head gestures where the angle values were read out from the robot kinematics. The calculated yaw angles differed from the robot’s readings with a mean absolute error of approx. 1 degree and the rate of differences between these values exceeding 5 degrees was significantly below 1 percent except for one outlier at 1.12%. This level of accuracy is sufficient for many applications, such as VR systems, human-system interfaces, or rehabilitation.
Investigating Linguistic Variation of Pakistani Online Fiction Book Reviews: A Multidimensional Study

Recent technological advancement on social media and the internet has given popularity to the online book review and acknowledged it as a distinctive register. The research aims to explore the linguistic variation of Pakistani online fiction book reviews with Biber (1988) five textual dimensions. A specialized corpus of these book reviews was developed through online newspapers, magazines and academic journals spanning over 10 years. The compiled corpus was analyzed by employing Biber (1988) multidimensional tagger. To explore the distinct identity of Pakistani online fiction book reviews as a register, the current research aims to explore linguistic variation across three mediums on five textual dimensions of Biber’s 1988 study. The findings of the study reveal Pakistani academic writing as highly informational, nonnarrative, exceedingly explicit, non-persuasive and impersonal in style. This study may be fruitful for journalistic writing courses, social media professionals, and ESP course developers.
A Secured, Multilevel Face Recognition based on Head Pose Estimation, MTCNN and FaceNet

Artificial Intelligence and IoT have always attracted a lot of attention from scholars and researchers because of their high applicability, which make them a typical technology of the Fourth Industrial Revolution. The hallmark of AI is its self-learning ability, which enables computers to predict and analyze complex data such as bio data (fingerprints, irises, and faces), voice recognition, text processing. Among those application, the face recognition is under intense research due to the demand in users’ identification. This paper proposes a new, secured, two-step solution for an identification system that uses MTCNN and FaceNet networks enhanced with head pose estimation of the users. The model's accuracy ranges from 92% to 95%, which make it competitive with recent research to demonstrate the system's usability.
An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning

Computer vision plays a significant role in mobile robot navigation due to the abundance of information extracted from digital images. On the basis of the captured images, mobile robots determine their location and proceed to the desired destination. Obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement due to the complexity of the environment. This research provides a real-time solution to the issue of extracting corridor scenes from a single image. Using an ultra-fast semantic segmentation model to reduce the number of training parameters and the cost of computation. In addition, the mean Intersection over Union (mIoU) is 89%, and the high accuracy is 95%. To demonstrate the viability of the prosed method, the simulation results are contrasted to those of contemporary techniques. Finally, the authors employ the segmented image to construct the frontal view of the mobile robot in order to determine the available free areas for mobile robot path planning tasks.