Reviewerly/exHarmony huggingface dataset:
The Politeness Strategies of Thai Undergraduates in an Instant Messaging Application

The aim of this study is twofold: to analyze politeness strategies in the online conversations of Thai students, and to suggest how this analysis can be applicable to pedagogical practice. A corpus of a 21-month instant online conversation among students and teachers has been analyzed. Throughout the time of data collection, the teachers continually encouraged students to use English and French to promote the use of foreign languages that they were learning. The result of statistical analysis showed the relation between speech acts and politeness strategies used in the data. It could be claimed that the students’ language proficiency governed their politeness strategies. As a result, the authors proposed a modification of Brown &amp; Levinson’s (1978) Weightiness formula for non-native speakers of English as: Wx = LP(S) × [D(S,H) + P(H,S) + Rx]. The results also showed that emoticons were used as redressive actions in politeness. Further, interlanguage pragmatics in the data were discussed based on linguistic competency and socio-cultural norms in the participants’ L1. The results suggest that teachers and curriculum developers could better understand students’ communication behaviors and language competency through computer-mediated communication. Finally, we offer suggestions to promote online communication in the context of active learning.
Online Learning in the COVID-19 Pandemic: Catastrophe or Opportunity?

Now the global pandemic has forced most of higher education institutions in the world to adopt online teaching. This paper uses the dual perspectives of teachers and students and uses qualitative research methods to investigate the online learning situation of higher education institutions in Hungary under the pandemic. Research questions are mainly focused on challenges, to what extent online courses can circumvent the weaknesses of traditional classroom teaching and opportunities in paradigm shift. The result depends on the opinions collected, to know the changes that can be expected in the future.
A Study on Performance Evaluation of MWIR Image Detection Based on YOLO Model Using RGB Channel Image

Recently, artificial intelligence is being used in many business fields. In the field of image, it is used in many different forms, starting with simple object detection, tracking, synthetic image generation, and style conversion. In particular, the object detection field has already been applied and used in many fields such as national defense, product defect detection, and security thanks to tremendous development. However, current object detection models are mainly performed with RGB images. Due to this direction of research, a separate study is underway for a model for IR image. Because of this, the development of deep learning models for IR images is much slower than RGB images. In addition, due to the lack of IR image data, research on IR image deep learning models is becoming more and more laggy compared to other deep learning studies. This paper proposes that the model trained on RGB images shows excellent performance in IR images. The object detection deep learning model learns shape information by using feature extraction. Our results show that IR images showing the shape of an object and images learned as RGB images can be sufficiently inferred. As a result, the model trained with RGB images shows robustness even in IR images.
Evaluación de un ambiente digital de aprendizaje: diseño y validación del instrumento

The purpose of this research was to determine the validity of an instrument that evaluates a digital learning environment, which allows get the student´s perception of the operation and implementation of the components of that environment. The information was collected from 85 students that belong to the University of San Luis Potosí in Mexico. The design of the investigation was a quantitative approach in transactional descriptive cut. The analyzes that were carried out were validation by expert judgment and students, validation by internal consistency and validation of referencie with diferent populations. The results showed that the instrument is valid provides enough information to measure the dimensions that a digital learning environment must contain and determine the quality of the environment, in addition to this, it was possible to determine that the instrument is clear and understandable for students who use it. The conclusions are that 1) the instrument provides sufficient information to determine the quality of the environment which allows continuous improvement of the educational goals, and 2) It is clear an understandable instrument for the students who respond it.
Classification of histological images of thyroid nodules based on a combination of Deep Features and Machine Learning

Background: Thyroid nodules are a prevalent worldwide disease with complex pathological types. They can be classified as either benign or malignant. This paper presents a tool for automatically classifying histological images of thyroid nodules, with a focus on papillary carcinoma and follicular adenoma. Methods: In this work, two pre-trained Convolutional Neural Network (CNN) architectures, VGG16 and VGG19, are used to extract deep features. Then, a principal component analysis was used to reduce the dimensionality of the vectors. Then, three machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, and Random Forest) were used for classification. These investigations were applied to our database collection, Results: The proposed investigations have been applied to our private database collection with a total of 112 histological images. The highest results were obtained by the VGG16 transfer deep feature and the SVM classifier with an accuracy rate equal to 100%.
Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter

Background The web-based health question-and-answer (Q&amp;A) community has become the primary and handy way for people to access health information and knowledge directly. Objective The objective of our study is to investigate how content-related, context-related, and user-related variables influence the answerability and popularity of health-related posts based on a user-dynamic, social network, and topic-dynamic semantic network, respectively. Methods Full-scale data on health consultations were acquired from the Metafilter Q&amp;A community. These variables were designed in terms of context, content, and contributors. Negative binomial regression models were used to examine the influence of these variables on the favorite and comment counts of a health-related post. Results A total of 18,099 post records were collected from a well-known Q&amp;A community. The findings of this study include the following. Content-related variables have a strong impact on both the answerability and popularity of posts. Notably, sentiment values were positively related to favorite counts and negatively associated with comment counts. User-related variables significantly affected the answerability and popularity of posts. Specifically, participation intensity was positively related to comment count and negatively associated with favorite count. Sociability breadth only had a significant impact on comment count. Context-related variables have a more substantial influence on the popularity of posts than on their answerability. The topic diversity variable exhibits an inverse correlation with the comment count while manifesting a positive correlation with the favorite count. Nevertheless, topic intensity has a significant effect only on favorite count. Conclusions The research results not only reveal the factors influencing the answerability and popularity of health-related posts, which can help them obtain high-quality answers more efficiently, but also provide a theoretical basis for platform operators to enhance user engagement within health Q&amp;A communities.
A Variety of Languages and Banyumasan Identity Appear in Verbal Communication on the YouTube Channel " Cerita Koplak"

The goal of this research is to characterize the variety of Banyumasan languages and identities that may be found on the "Cerita Koplak" YouTube channel. This study was conducted through the stage of collecting data utilizing the observational approach and note-taking procedures. The sophisticated a
Achieving Zero-copy Serialization for Datacenter RPC

Remote Procedure Call (RPC) is widely used in distributed systems and it usually needs to serialize data before transmission. Serialization accounts for a large proportion of the overhead in RPC and becomes a bottleneck for RPC communications. Because the size of the output serialized message cannot be predicted in advance, there could be multiple memory reallocations and copies in typical serialization libraries (e.g., FlatBuffers), which dominates the overhead. We propose the novel serialization library, zFlatBuffers, to eliminate these avoidable copies during the serialization process and realize zero copy during communication. Unlike the typical serialization library, FlatBuffers, the message generated by zFlatBuffers consists of multiple non-contiguous buffers due to its zero-copy nature. Moreover, we integrate zFlatBuffers with RDMA-based RPC systems. For RDMA Unreliable Datagram, we modify the message buffer of eRPC to enable it to transmit messages composed of multiple buffers. We also build the zRPC system based on RDMA Reliable Connection, which transmits the zFlatBuffers message by the scatter/gather function. Compared to the original FlatBuffers, zFlatBuffers improves the throughput of eRPC and zRPC by 11.2%-33.7% and 5.8%-53.6%, respectively.
PRE-ECLAMPSIA DIAGNOSIS EXPERT SYSTEM USING FUZZY INFERENCE SYSTEM MAMDANI

Various institutions utilize computer information systems to analyze and process data. An expert system is an information system that is used to help analyze and determine decisions on a problem based on rules determined by experts. This research focuses on creating a prototype expert system for diagnosing pre-eclampsia or pregnancy poisoning in pregnant women based on measuring blood pressure and checking proteinuria. The existing data is then analyzed using the Mamdani system's fuzzy inference method. Supporting theory regarding the fuzzy inference system of Mamdani, pre-eclampsia and its examination indicators will be used as a basis for creating this expert system prototype. The data used were secondary data on preeclampsia patients in the form of medical records of blood pressure measurements, proteinuria examinations and doctor diagnoses of preeclampsia patients at two Regional General Hospitals (RSUD), namely Atambua and Kefamenanu, totaling 20 samples. The interface or user interface of this prototype system is made as simple as possible so that it can be operated by all ordinary people. The programming language used is Visual Basic (VB) with the Visual Studio 2010 developer application. The initial prototype of this system will continue to be developed until it can become a Information systems or real applications used in hospitals. The results of this research are that the expert system for diagnosing preclampsia can be used well and easily by hospital staff and show congruence between the system diagnosis results and the diagnosis results from obstetricians or experts in the 20 processed data.
Library Management System

- There are books of every sort available at libraries. Web-based software called Intranet Library Management System is used. This system has a list of all the books and allows remote users to view it simultaneously from wherever on campus. However, users need to be signed up for that. Two tiers of architecture make up this system. Students submit requests, which the server receives, processes, extracts data from the database, and then returns the completed request back to the client. This system offers a unique interface and login for students and librarians. Students may look for books online and renew their books. The librarian can change the database. They may simply send messages to the librarian from any location within the campus to propose new books. They may examine the due dates and issuance and return dates for any books. This system produces reports that may be used to evaluate the performance of the library. The management can then take the necessary action to upgrade the facilities. Keywords : Library Management System, Two Tier architecture
Recognizing Public Satisfaction Toward Kampus Mengajar Program with Naive Bayes

Merdeka Belajar Kampus Merdeka (MBKM) is a policy governed by the Minister of Education and Culture to improve college students’ capability, creativity, and innovation. Several programs are offered to realize MBKM, one is Kampus Mengajar which gives the opportunity for the participants in problem-solving, strategic development, effective, innovative, and fun learning. The teaching campus program gets different responses from the public which is conveyed on social media, various comments in the form of comments that are positive, negative, or neutral. To recognize public opinions of Kampus Mengajar’s implementation, this research conducted a sentiment analysis using A thousand and five hundred Twitter datasets containing Kampus Mengajar as a keyword. We apply pre-processing before classifying the dataset in Naive Bayes. Thus, SMOTE is carried out to overcome imbalanced data. The results showed that the level of accuracy in determining categories was 77.45% and the micro average was 77.45% in determining sentiment and had a precision level of 81.46% and a recall of 77.45%.
Analysis of Intercultural Communication Competencies in Prospective Primary School Teachers' Use of Internet Technologies

It can be said that self-efficacy perceptions of using the Internet for intercultural communication purposes is a characteristic that can be developed by putting computer and Internet use skills into practice. Before starting the studies to improve this characteristic, there is a need for research to reveal the current situation. The purpose of this study is to examine the intercultural communication competencies and internet self-efficacy of prospective prospective primary school teachers in Kazakhstan in a relational way. In this study, since it is aimed to examine whether these two variables differ in terms of gender and professional seniority by examining the contribution of internet self-efficacy scores in predicting intercultural communication competence scores of teachers, a relational survey model with descriptive method was used. The research sample consists of prospective primary school teachers at two different universities, in which there are different campuses in Taldykorgan, Ural, Almaty and Semey in Kazakhstan in the 2022-2023 academic year. In this context, questionnaires were collected from 261 teachers through face-to-face questionnaires. According to the findings of the study, teachers' intercultural competence and Internet self-efficacy were found to be at a moderate level. Participant prospective teachers' intercultural competence and Internet self-efficacy differed in relation to gender and age. Finally, internet self-efficacy of prospective primary school teachers significantly predicted their intercultural competence.
Overview of Agile frameworks in Computer Science education

This paper explores the application of Agile frameworks in education to enhance the learning process. The lack of a generalized approach to Agile in education prompted an analysis and comparison of various frameworks, including Scrum, eduScrum, Agile Learning Loops (ALL), and other Scrum-based approaches. The Agile Manifesto was also reinterpreted for educational settings. The review highlights the differences and similarities among these frameworks in terms of their suitability for education, support for classic increments, project-based approach, documentation, technological stack, and role distribution. The analysis concludes that while Agile is increasingly used in education, there is no standardized methodology. The reviewed frameworks offer valuable insights, but their implementation requires experimentation and adaptation to individual educational contexts.
A scalable and fully tuneable VCSEL-based neural network

We experimentally demonstrate an autonomous, fully tuneable and scalable neural network of 350+ parallel nodes based on a large area, multimode semiconductor laser. We implement online learning strategies based on reinforcement learning. Our system achieves high performance and a high classification bandwidth of 15KHz for the MNIST dataset. Our approach is highly scalable both in terms of classification bandwidth and neural network size.
Quaternary Tree Structure as a Novel Method for Huffman Coding Tree 

The binary code used by the current Huffman-based techniques slows down decoding speed. The quaternary Huffman coding method is a fresh take on the traditional Huffman coding that is suggested in this study. Each symbol is encoded into a quaternary code rather than a binary code using the quaternary Huffman coding technique. For Huffman coding, a quaternary code stream necessitates a shallower Huffman tree. Less traverse time could be a benefit of a shorter Huffman tree, with the enhancement of both encoding and decoding speed. The main objectives of this research are to verify the feasibility of using quaternary code in terms of both decoding speed and memory usage mathematically, to develop a new code generation technique called quaternary Huffman coding, and to analyze the performance of the proposed system in terms of both storage and decoding time. In this study, we first quantitatively investigate the quaternary tree structure's features for building Huffman codes. We thoroughly investigate the new tree structure's nomenclature and substantiate the findings. It is found in the research that the quaternary code performs better than binary code in terms of decoding speed in data compression and decompression techniques.
Autonomous Uninhabited Underwater Vehicle for Searching, Detecting and Destroying Mines

The paper studies the principles of functioning of an autonomous uninhabited underwater vehicle that solves the problems of searching, detecting and destroying mines. The basic principles of the construction and operation of the anti-mine AUV are considered, hydroacoustic means of searching for mines with a description of the optimal detection and classification algorithm are given, and the system for destroying mines is described. The basics of the optimal design of anti-mine AUVs in terms of the design of the general structure of the vehicle and recommendations for the use of manufacturing materials are given.
Instant App – Emergency Situations in California

This chapter details the step-by-step process of creating an Instant App in ArcGIS Online from scratch. Initially, a map is developed in ArcGIS Pro, encompassing earthquake data, the Saint Andreas fault, hospitals, schools, tsunami information, and well locations in California. The map is then configured with appropriate labels and pop-up windows. Subsequently, it is shared as a web map along with web layers in ArcGIS Online. The web map is accessed, analyzed, and further configured within the Map Viewer, utilizing the Content toolbar. By utilizing various layers, the Instant App is designed to identify the precise locations of earthquakes, tsunamis, wells, schools, and hospitals, which can serve as emergency resources for shelter, medical assistance, and potable water. Additionally, the app incorporates the ArcGIS World Geocoding Services to determine nearby entities based on a specific address in California. Finally, the Instant App is published and made accessible to the global community.
From entanglement to discord: A perspective based on partial transposition

Here, we show that partial transposition, which is initially introduced to study entanglement, can also inspire many results on quantum discord, including (I) a discord criterion of a spectrum invariant under partial transposition, stating that one state must contain discord if its spectrum is changed by the action of partial transposition, and (II) an approach to estimate the geometric quantum discord and the one-way deficit based on the change of the spectrum. To compare with entanglement theory, we also lower bound the geometric quantum entanglement and the entanglement of relative entropy. Thus, on one hand, we illustrate an approach to specify and estimate discord based on partial transposition. On the other hand, we show that, entanglement and discord, two basic notions of nonclassical correlations, can be placed on the same ground such that their interplay and distinction can be illustrated within a universal framework.
A Federated Blockchain Architecture for File Storage with Improved Latency and Reliability in IoT DApp Services

Blockchain technology can address data falsification, single point of failure (SPOF), and DDoS attacks on centralized services. By utilizing IoT devices as blockchain nodes, it is possible to solve the problem that it is difficult to ensure the integrity of data generated by using current IoT devices. However, as the amount of data generated by IoT devices increases, scalability issues are inevitable. As a result, large amounts of data are managed on external cloud storage or distributed file storage. However, this has the disadvantage of being outside the blockchain network. This makes it difficult to ensure reliability and causes high latency during data download and upload. To address these limitations, we propose a method for managing large amounts of data in the local storage node of a blockchain network with improved latency and reliability. Each blockchain network node stores data, which is synchronized and recovered based on reaching a consensus between smart contracts in a cluster network. The cluster network consists of a service leader node that serves as a gateway for services and a cluster node that stores service data in storage. The blockchain network stores synchronization and recovery metadata created in the cluster network. In addition, we showed that the performance of smart contract execution, network transmission, and metadata generation, which are elements of the proposed consensus process, is not significantly affected. In addition, we built a service leader node and a cluster node by implementing the proposed structure. We compared the performance (latency) of IoT devices when they utilized the proposed architecture and existing external distributed storage. Our results show improvements up to 4 and 10 times reduction in data upload (store) and download latency, respectively.
Hand Gesture Recognition and Motion Detection System for Interactive Applications

Security systems and interactive multimedia applications are two examples of the many fields in which Gesture Sense (GS) technology, a significant development in human-computer interaction, has found use. This paper introduces the "Arduino Hand Gesture Identification and Motion Detection System," a revolutionary combination of Python, Arduino Uno R3, and ultrasonic sensors intended to improve interactive apps through accurate gesture identification. Real-time tracking of hand movements is made possible by ultrasonic sensors, which are renowned for their fine-grained motion detection capabilities. Python allows the Arduino Uno R3 to run complex gesture detection algorithms. A user's engagement is improved by an LCD that presents recognized gestures and status updates, which provides immediate visual feedback. The system's outstanding accuracy, responsiveness, and usability are demonstrated by severe testing, which shows that it typically recognizes objects with an accuracy of 93%. In the context of interactive applications, this research study demonstrates how GS technology has the potential to revolutionize user experiences by ensuring seamless and immersive interactions. Key Words: Interactive application, Human-computer interaction, Hand gesture recognition, Motion detection.