Reviewerly/exHarmony huggingface dataset:
Concepts in Computational Cognition

The sign kinds of natural language are the concepts, the indexicals, and the names. Of these, only the concepts interact directly with the agent’s cognition-external environment, whereas indexicals and names receive their interpretation indirectly from cognition-internal content.
THE REASON OF APPLYING PREWRITING PHASES OF ARGUMENTATIVE WRITING

The objectives of this study were to describe the cognitive process occurs during the prewriting phases of argumentative writing among the undergraduate students and to explain why such cognitive process are realized in undergraduate students of prewriting phases in argumentative writing. This study is limited to the study of the cognitive process in prewriting phases for undergraduate students. This study is followed the theory of Kellog (1990:327) about the the activities in prewriting phases consist of (1) Collecting Information, and (2) Planning text. In accordance to the theory and research purposes, descriptive qualitative research design was appropriately implemented in the study. The data of this research were the students’ students utterances, which were collected through semi-structured interviews and interpreted through the Kellog’s Theory. The reason why the students should apply the prewriting phases were: (1) there was misconception of argumentative writing, (2) there was no knowledge about the generic structure of argumentative, (3) there is no awareness that in argumentative writing, the students should talk the controversial part of the topic, and (4) the students do not know that they have to state explicitly their standing point towards an issue and provided the reasons, facts, and empirical evidences. This leads to conclusion that cognitive process in prewriting is important to be applied in students writing process.
Digital Morphology: The Final Frontier

Morphology is central to biological anthropology and its allied fields of anatomical sciences, forensics, and other related disciplines. Many biological anthropology students have their first real foray into the discipline after completing a course in osteology, craniometry, or vertebrate morphology. Unfortunately, the natural history collections that support this type of research and training have not grown. Many countries have strict rules about natural history specimen collections, and these collections seem to be concentrated in a few developed countries, regardless of where the specimens had been collected. Thus, access to comparative material can be problematic where such collections are not readily available. Even if collections are available, accessing them can be severely restricted due to external circumstances, as the prolonged pandemic has shown. Luckily, digital morphology has emerged over the last decade as a new field that stands to change the landscape of specimenbased research and training. Concerted 2D and 3D digitization efforts, the emergence of online aggregate specimen repositories, and availability of comprehensive open-source software tools (such as 3D Slicer) for utilizing these resources has conveniently transformed the field of quantitative and comparative morphology. In this brief review, I will focus explicitly on the 3D Slicer ecosystem and how it can be leveraged as part of a curriculum or research program on digital morphology. In a nutshell, the primary differentiator of the 3D Slicer is not that it is just free but that it is open-source and extensible, making access to digital data more equitable for everyone. I will particularly focus on the 3D Slicer’s SlicerMorph extension, which facilitates 3D geometric morphometric data collection and analysis within the Slicer ecosystem, so all the steps in the digital morphology workflow from import, visualization, and data collection to visualizing the morpho-space can be achieved in a single, well-documented environment.
A BERT-Powered Writing Assistant for Academic Purposes in European Portuguese

In this paper, we will present the process of developing a resource that we consider to be useful for both native and non-native college students in the process of writing Portuguese academic texts: a BERT-powered Writing Assistant for academic purposes in European Portuguese. The Writing Assistant includes two main components: a phrase bank, that will be created using open scientific data in the form of scientific papers found in repositories, and a search engine, that uses BERT models for semantic searches. To create the phrase bank we will loosely follow the methodology developed by John Morley, creator of the Academic Phrasebank of the University of Manchester. The phrase bank will be based on 40 scientific papers taken from the repository of University of Minho. The corpus will be initially annotated, using some of the categories proposed by Morley, then the categories will be revised to better represent the reality of Portuguese academic discourse. The annotated phrases will then be simplified and stripped of any particular academic content. This phrase bank will “feed” the search engine. The search engine works with BERT machine learning models that allow us to make semantic searches. Students would just have to write a word, expression or sentence in the search bar to find equivalent or similar expressions on our phrasebank, even if the user has little to no knowledge of the vocabulary used in academic discourse, because Bert models are able to infer semantic context and find relevant results.
Designing a Vision Transformer based Enhanced Text Extractor for Product Images

Product images, such as those which appear in e-commerce sites, exhibit unique characteristics that are typically not present in natural images. The primary distinguishing characteristic is the presence of text (e.g., brand names, price, constituents) along with high local entropy (i.e., too much visual information in the form of both text and brightly coloured pictures condensed in a small region). Extracting the text from these images may have multiple benefits: catalogue enrichment, product matching, offensive content identification, and more. However, the images are sometimes unclear and blurry where it is difficult to recognise the text even with human perception, and these texts are often written in non-standard fonts (at times each character in a word has a different colour and/or style), or are oriented at odd angles or appear on curved surfaces; moreover, many of these words such as, the brand names, do not appear in dictionaries. In this work, we present a vision transformer based text extractor that can handle the aforementioned challenges for product images effectively, and outperforms our earlier model considerably. We further compare our new end-to-end text extraction solution with those of Google and Azure text extraction cloud offerings, and showcase its efficacy both in terms of accuracy and latency.
A Study on Content Validity Ratio (CVR) of Design Thinking Subject Learning Content

The rapid spread and development of the virtual educational environment occured with the onset of the COVID-19 pandemic in 2019. Interest and demand for effective educational methods for virtual learning from educational institutions have been increasing due to this phenomenon. As a result, there has been a shift from the origianly design thinking educaiton model that existed solely in person, has now had to transition into a virtual format. For this reason, research related to design thinking education in the online environment is insufficient. This study proposes an educational model for online design thinking based on flipped-learning. Students can actively form a team by learning common theories and sharing individual topics. The model was created based on the hypothesis that it would positively affect online team activity. The design thinking subject questionnaire was designed to cover four areas: class content, concept application stage satisfaction, instructor intervention, team activity. These four fields were tested originally by Lawshe's CVR(Content Validity Ratio) analysis in order to analyze the descriptive statistics for the mean and the standard deviation. Additionally, the KS(Kolmogorov-Smirnov) test was utilized in order to analzye the difference between these items. The validity of all the questions presented in the four areas: class content, concept application stage satisfaction, instructor intervention, and team activity method, were all proven to be valid. The items whose validity has been proven through this study can be used as primary data for systematizing the ideal design thinking class model of flipped-learning for instructors in the university class setting.
EFEKTIVITAS TEAM GAMES TOURNAMENT (TGT) DENGAN QUICK AND SMART TERHADAP AKTIVITAS DAN HASIL BELAJAR MATERI THINGS IN THE CLASSROOM

AbstractIn order to realize the ideals of education required appropriate learning. Reality has not been a student-centered learning. The results of interviews of teachers and students gained student learning outcomes is low because students are less enthusiastic and active role in the learning process. The condition can be pursued through Team Games Tournament (TGT) learning model with Quick and Smart which requires students to learn and understand the material. This study aims to determine the effectiveness of TGT aided Quick and Smart about Things in the Classroom material and learningoutcomes in one of state elementary school in Semarang. This study uses pretest-posttest control group design. Sample used are class VA (experimental class) and VB (control group). Sampling using random cluster sampling. The results showed experiment class is more active than control class, more than 80% experimental class students’s activity was high, posttest experimental class’s average was better than control class, N_gain class experimental’s score was high, achieved more than 80% of students posttest experimental group gained value ≥75. These results were confirmed by students and teachers response to learning model applied. Based on this research can be concluded that TGT aided Quick and Smart is effective to increase students’ activity and their learning outcome about Things in the Classroom.Keywords: Activity, Learning Result, Things in the Classroom, TGT Quick and Smart
Identification and Filtering of Web Spams Using a Machine Learning Method

In order to enhance the filtering of spam on the Internet and improve the experience of Internet users, this paper proposed to convert the email text into vector features using the vector space model, constructed a two-dimensional matrix, and used a convolutional neural network (CNN) to identify spam on the Internet. The CNN was compared with other two classifiers, support vector machine (SVM), and backward-propagation neural network (BPNN), in simulation experiments. The final results showed that the spam recognition algorithm with CNN as the classifier had better recognition performance than the algorithms with SVM and BPNN classifiers and was also more advantageous in terms of recognition cost and time for spam; in addition, the CNN had the best recognition performance when the number of extracted features was 15.
PEMANFAATAN TIK BERUPA APLIKASI ONLINE DALAM PEMBELAJARAN PASCA PANDEMI DI SEKOLAH DASAR

The ability to master ICT is one of the efforts to increase teacher professionalism in carrying out learning. The purpose of this research is to describe online applications that teachers use for learning after the Covid19 pandemic. This research is a survey research. The subject of this study involved a sample of teachers in Blitar City, totaling 54 people consisting of 13 men and 41 women with an age range of 26 years to 59 years. The teachers involved in this research teach in both public and private schools in the city of Blitar. The teachers involved in this study had more than five years of teaching experience. Data collection was carried out using a survey questionnaire by filling out the Google form. Data analysis was carried out simultaneously and integrated descriptively. The results of this study indicate that the 5 online applications most frequently used by teachers in learning are Whatsapp, Google Forms, Google Meet, Zoom Meeting, and Quizizz.
ElecDaug: Electromagnetic Data Augmentation for Model Repair based on Metamorphic Relation

With the application of deep learning (DL) in signal detection, improving the robustness of classification models has received much attention, especially in automatic modulation classification (AMC) of electromagnetic signals. A large amount of electromagnetic signal data is required to obtain robust models in the training and testing process. However, the high cost of manual collection and the issue of low quality of automatically generated data contribute to the AMC model’s defects. Therefore, it is essential to generate electromagnetic data by data augmentation. In this paper, we propose a novel electromagnetic data augmentation tool, namely ElecDaug, which directs the metamorphic process by electromagnetic signal characteristics to achieve automatic data augmentation. Based on electromagnetic data pre-processing, transmission or time-frequency domains characteristic metamorphic, ElecDaug can augment the data samples to build robust AMC models. Preliminary experiments show that ElecDaug can effectively augment available data samples for model repair. The video is at https://youtu.be/x5g6IVX_Q3s. Documentation and source code can be found here: https://github.com/ehhhhjw/tool_ElecDaug.git.
Analysis of tuberculosis patient data distribution using the aggregation function

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Eka Mala Sari Rochman, Miswanto, Herry Suprajitno; Analysis of tuberculosis patient data distribution using the aggregation function. AIP Conf. Proc. 4 January 2023; 2679 (1): 020011. https://doi.org/10.1063/5.0111605 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
An Analysis of Brainstorming Learning Strategy on the Students’ Writing Descriptive Text in Junior High School

This study was conducted to analyze how the brainstorming learning strategy on students in writing descriptive text with the aim of describing how the brainstorming learning strategy on students in writing descriptive text. This study uses a case study in qualitative research. The participants in this study were students of grade 8-b MTs Munawwarah Amal Bhakti Kec. Ujung Padang Kab. Simalungun, totaling 26 students consisting of 16 male students and 10 female students. Data collection was carried out using class observation, interviews with teachers and students and documentation. The findings of this study state that the brainstorming learning strategy in writing descriptive text for grade 8-b students makes students actively think to express their opinions through writing, Students can learn to think quickly and, Students feel comfortable and satisfied, Teach students to be disciplined, Increase vocabulary understanding in English. Based on the findings, the researcher gives suggestions as teachers should continue to apply brainstorming learning strategies in writing descriptive text, because in addition to training students to write, they also train students to think. Teachers can use audio-visual media such as in focus in explaining learning materials so that learning is more interesting. Teachers should require students to bring a dictionary in English lessons, in order to improve vocabulary comprehension. Schools must provide or facilitate media in teaching, so that learning becomes interesting and students can understand the lesson well.
Linked motion image‐based dynamic hand gesture recognition

Abstract The researchers have paid significant attention to dynamic images for hand gesture recognition. Dynamic images are gesture representation patterns that simultaneously capture spatial, temporal, and structural information from the video. Existing techniques to generate dynamic images provide low discriminability for the gestures, which follow the same trajectory, but in opposite directions, such as “swiping hand right” versus “swiping hand left.” Also, limited to similar gestures such as “Snap fingers” versus “Dual fingers heart.” To address these issues, we have proposed an algorithm to convert a depth video into a single dynamic image known as a linked motion image (LMI). We give the LMI to a classifier consisting of an ensemble of three modified pretrained convolutional neural networks. We conduct the experiments using a multimodal large‐scale EgoGesture dataset and The MSR Gesture 3D dataset. For the EgoGesture dataset, the proposed method achieved an accuracy of 92.91%, which is better than the state‐of‐the‐art methods. For the MSR Gesture 3D dataset, the proposed method accuracy is 100%, which outperforms the state‐of‐the‐art methods. This work also highlights the recognition accuracy and precision of each gesture. The experiments demonstrate the work's economic efficiency using a web‐based data science environment called Kaggle rather than high‐end systems like GPU.
Lecturer Creativity in the Use of Online Learning Media at Manado State University

Lecturers and students conduct lectures online through online applications such as google meet, zoom meeting, google classroom, and other online learning applications. In addition to using online applications, a lecturer of course also needs to develop his creativity by using these online learning applications coupled with creativity in combining them with other online means such as social media. This research is different from previous research where this research specifically wants to see and describe how the creativity of lecturers in using online learning media will determine the process and results of achieving certain subjects. The method used in this study is a qualitative research method where the researcher does not use data in the form of numbers but rather data in the form of stories. Methods of data collection using observation and interviews to lecturers at Manado State University. Data analysis uses data analysis from Miles and Huberman, interactive nature of data collection with data analysis, data reduction, data presentation, and drawing conclusions. The results of the research are as follows: (1) combining the unima learning management system with the zoom meeting application; (2) using youtube media and zoom meetings; (3) combining google class rooms with the google meet application; (4) the main media for the zoom meeting application being used with instagram and WhatsApp groups; and (5) using the zoom meeting application with the tiktok application. The conclusion means that basically the lecturers at Manado State University have been able to use online learning media creatively and apply them in the learning process.
PEDAGOGICAL TECHNOLOGY OF TEACHING CHILDREN WITH HEARING IMPAIRMENTS IN THE CONDITIONS OF NARRATIVE UNDERSTANDING OF THE METHODOLOGY

International scientific conference “Modern scientific developments in pedagogy and psychology” : conference proceedings (November 3–4, 2022. Riga, the Republic of Latvia). Riga, Latvia : “Baltija Publishing”, 2022. 204 pages.
A Conceptual Study on the Application of AI-based RPA to Improve the Defense Cost Management System

This study proposes three methodologies to improve the Defense Cost Management System through the application of RPA(Robotic Process Automation). This expects to improve the continuity, accuracy, and efficiency of cost estimation. First, an RPA-based cost estimation system is established. An AI(Artificial Intelligence) implemented RPA system performs validation of cost data and simple repetitive tasks, and thus, it prevents the errors and increases the efficiency in associated tasks. Second, an integrated operation of cost and cost-management is performed. Through this, the issues caused by database separation and key-value differences between the ‘Defense Integrated Cost System’, which manages cost tasks, and the ‘Defense Standards Comprehensive Information System’, which manages defense standards and standardization tasks, can be improved. Finally, a feedback system for cost analysis and cost information is established. This standardizes technologies and associated costs in the weapon acquisition phases. In addition, it collects, stores, and manages cost analysis and estimation records regularly, and makes it possible to reuse the information when a new weapon system requirement arises.
Fast Reset Protocol for Superconducting Transmon Qubits

For larger-scale quantum information processing, qubit reset plays an important role, as the coherent times for qubits are limited. However, previous schemes require either long reset times or a complex pulse calibration technique, leading to low efficiency in qubit reset. Here, we propose a fast and simple reset protocol for superconducting transmon qubits based on the coupler-coupled qubits architecture. In this setup, a mixing pulse is used to transfer the qubit excitation to the combined excitation of a low-qulity coupler and readout resonator, which will quickly decay to their respectively ground states, leading to efficient qubit reset to the ground state. Our numerical results show that the residual population of the qubit’s excited state can be suppressed to 0.04% within 28 ns; the reset time will be 283 ns if photon depletion of the readout resonator is required. Thus, our protocol provides a promising way for the high-efficiency superconducting qubit reset.
Software Defect Prediction Framework Using Hybrid Software Metric

Software fault prediction is widely used in the software development industry. Moreover, software development has accelerated significantly during this epidemic. However, the main problem is that most fault prediction models disregard object-oriented metrics, and even academician researcher concentrate on predicting software problems early in the development process. This research highlights a procedure that includes an object-oriented metric to predict the software fault at the class level and feature selection techniques to assess the effectiveness of the machine learning algorithm to predict the software fault. This research aims to assess the effectiveness of software fault prediction using feature selection techniques. In the present work, software metric has been used in defect prediction. Feature selection techniques were included for selecting the best feature from the dataset. The results show that process metric had slightly better accuracy than the code metric.
Comparison Perception, Satisfaction, and Learning Strategy of Korean and Vietnamese College Students in Online English Classes in Korea

This study explores to compare perception, satisfaction, and learning strategy according to the form of online English class for Korean and Vietnamese college students through frequencies analysis, cross tabulation analysis, and independent samples t-test. The result showed that Korean college students had a more positive perception on one-way English class, while Vietnamese college students had a more positive perception on two-way English class. Though most results of satisfaction area were not statistically significant between two online class types, foreign students wanted to take two-way English online class more than Korean students. Korean college students preferred to use self-regulated learning, while Vietnamese students mainly used communication with professors in learning strategy.