Reviewerly/exHarmony huggingface dataset:
Application of the Mixed Teaching Model using Internet+ for Clinical Courses of Traditional Chinese Medicine

With the arrival of the Internet + era and the need for development and personnel training, the traditional teaching model of TCM (Traditional Chinese Medicine) needs to be transformed into a mixed teaching mode. In addition, the TCM English teaching resources from which learners can pick are restricted, and certain critical revised TCM material is not delivered on time. For high-quality delivery of service to TCM English learners, a system with materials and communication is considered extremely important. In this connection, “Internet +” provides both possibilities and problems for the advancement of TCM English instruction, which has considered a key to advanced TCM. Therefore, motivated by the development of “Internet +,” we try to establish a relevant analysis of the mixed teaching model and propose the implementation path and method of the mixed teaching model of traditional Chinese medicine: changing ideas and guiding students to enter into deep learning to build a teaching community; integration and reform of online teaching and classroom teaching of traditional Chinese medicine; emphasis on developmental evaluation. The evaluation is based on whether it provides students with meaningful Chinese medicine learning experience and deep learning status as the evaluation criteria.
Euclidean ideal classes in Galois number fields of odd prime degree

Weinberger in 1972, proved that the ring of integers of a number field with unit rank at least $1$ is a principal ideal domain if and only if it is a Euclidean domain, provided the generalised Riemann hypothesis holds. Lenstra extended the notion of Euclidean domains in order to capture Dedekind domains with finite cyclic class group and proved an analogous theorem in this setup. More precisely, he showed that the class group of the ring of integers of a number field with unit rank at least $1$ is cyclic if and only if it has a Euclidean ideal class, provided the generalised Riemann hypothesis holds. The aim of this paper is to show the following. Suppose that $\mathbf{K}_1$ and $\mathbf{K}_2$ are two Galois number fields of odd prime degree with cyclic class groups and Hilbert class fields that are abelian over $\mathbb{Q}$. If $\mathbf{K}_1\mathbf{K}_2$ is ramified over $\mathbf{K}_i$, then at least one $\mathbf{K}_i$ ($i \in \{1,2\}$) must have a Euclidean ideal class.
Parameter Learning of Bayesian Network with Multiplicative Synergistic Constraints

Learning the conditional probability table (CPT) parameters of Bayesian networks (BNs) is a key challenge in real-world decision support applications, especially when there are limited data available. The traditional approach to this challenge is introducing domain knowledge/expert judgments that are encoded as qualitative parameter constraints. In this paper, we focus on multiplicative synergistic constraints. The negative multiplicative synergy constraint and positive multiplicative synergy constraint in this paper are symmetric. In order to integrate multiplicative synergistic constraints into the learning process of Bayesian Network parameters, we propose four methods to deal with the multiplicative synergistic constraints based on the idea of classical isotonic regression algorithm. The four methods are simulated by using the lawn moist model and Asia network, and we compared them with the maximum likelihood estimation (MLE) algorithm. Simulation results show that the proposed methods are superior to the MLE algorithm in the accuracy of parameter learning, which can improve the results of the MLE algorithm to obtain more accurate estimators of the parameters. The proposed methods can reduce the dependence of parameter learning on expert experiences. Combining these constraint methods with Bayesian estimation can improve the accuracy of parameter learning under small sample conditions.
DEVELOPMENT OF RESOURCES FOR BLENDED LEARNING: DESIGN OF SIMULATORS IN SCIENTIFIC-TECHNICAL EDUCATION

DEVELOPMENT OF RESOURCES FOR BLENDED LEARNING: DESIGN OF SIMULATORS IN SCIENTIFIC-TECHNICAL EDUCATION
IMPROVING SKILLS OF PROCESSING AND MAPPING CADASTRAL INFORMATION BY AN INNOVATIVE QGIS PLUGIN WITH TEACHING PURPOSES

IMPROVING SKILLS OF PROCESSING AND MAPPING CADASTRAL INFORMATION BY AN INNOVATIVE QGIS PLUGIN WITH TEACHING PURPOSES
REFLECTIONS ON A MULTI-NATIONAL SOUTH-NORTH HIGHER EDUCATION CAPACITY BUILDING PROJECT IN MARINE EDUCATION AND TRAINING

REFLECTIONS ON A MULTI-NATIONAL SOUTH-NORTH HIGHER EDUCATION CAPACITY BUILDING PROJECT IN MARINE EDUCATION AND TRAINING
CHILDREN’S LITERATURE: A WAY FOR THE DEVELOPMENT OF MATH AND SOFT SKILLS

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 1639-1646ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0434Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
The development of japanese language learning for elementary school students based on collaborative learning

&lt;div&gt;&lt;p&gt;Japanese learning is not popular in Indonesia but has important value. In Bali, ten elementary schools have introduced Japanese as an additional skill for students to master Japanese from a young age. Therefore it is necessary to develop learning that follows the characteristics of children and can improve 21st-century skills. This study aims to develop Japanese language learning set for elementary students according to student characteristics, requiring students to collaborate and think critically. This study uses a mixed-method approach paradigm, namely Exploratory Mixed Method Design, which combines qualitative and quantitative approaches using the Four-D R&amp;amp;D design. Learning Set in the form of lesson plans, worksheets, and animation media for Japanese language learning that are produced contain activities that require students to collaborate, which can improve students' 21st-century skills. Based on the validity test results, it can be seen that this learning set has been tested for its effectiveness. The assessments carried out by teachers and students show that the lesson plans, worksheets, and animation media are suitable for learning Japanese at the elementary level. Thus, Japanese teaching teachers at elementary schools can use these learning sets to increase student motivation in learning and can improve 21st-century skills.&lt;/p&gt;&lt;/div&gt;
Red Blood Cell Aggregation Classification Based on Ultrasonic Radiofrequency Echo Signals by An Improved Convolutional Neural Network

In order to solve the main problem that traditional machine learning methods rely on manual experience to extract features, the method based on ultrasonic RF signal convolutional neural network is proposed to extract features for red blood cell (RBC) classification. Considering that VGG16 network uses small convolution kernel, which not only reduces network parameters, but also improves classification accuracy. Therefore, a compact network model RBCA-VGG10 is proposed by deleting layers and adjusting the structure of VGG16. Compared with LeNet, AlexNet, GoogleNet, ResNet models, the average classification accuracies of RBCA-VGG10 are improved by 10.15%, 9.30%, 4.40%, 8.34%, respectively, and no overfitting. Compared with B-mode images method and the RF signals based on empirical wavelet transform method by support vector machine (SVM) classifier, the average classification accuracies of RBCA-VGG10 model are improved by 8.97% and 6.52%, respectively.
Investigating the Impact of Delayed Mining upon Scaling Blockchain Applications under Malleability Attack—An e-Voting Scenario

Blockchain is a disruptive technology and has attracted significant attention with prominent applications across finance, medical and many other systems. Electronic voting or e-voting is one of the emerging applications of blockchain leveraging benefits such as integrity, and non-repudiation of data. Whilst existing research has focused on using blockchain for e-voting applications to achieve transparent and verifiable solutions, there exists a gap in study with respect to an in-depth investigation for the challenges surrounding the trade-off for scalability versus security in the context of transaction malleability attack when mining capability is compromised for the blockchain network. In order to present our research, we have used e-voting as an example application to demonstrate our investigations regarding the mentioned gap. The paper does not focus on building a blockchain based e-voting system. The study carries out empirical analysis by establishing blockchain networks with varying mining strength under an extreme load of incoming transactions to achieve a real world scenario of large blockchain based system. The developed system has then been exposed to transaction malleability attack while scaling throughput up to thousands of transactions. The findings reveal that blockchain networks upon scaling, are more vulnerable to transaction malleability attack while operating under lower mining strength as compared to the networks having higher mining power under same conditions. These research outcomes would help researchers and application architect to build highly secured and large scaled applications. To the best of our knowledge, there has not been any published empirical investigation present in the given context.
Demo Abstract: A Distributed Power Side-channel Auditing System for Online loT Intrusion Detection

As the number of IoT devices has increased rapidly, IoT botnets have exploited the vulnerabilities of IoT devices. However, it is still challenging to detect the initial intrusion on IoT devices prior to massive attacks. Thus, a new approach that monitors these ini-tial intrusions is needed. Power side-channel information can be used because it does not require any modification in programming languages or operating systems on diverse IoT devices. We propose a distributed power side-channel auditing system for online IoT intrusion detection. To meet the real-time requirement, we develop a lightweight power auditing device. We then design a distributed CNN classifier for online inference in a laboratory setting. Two distributed protocols are also proposed in order to protect data leakage and reduce networking redundancy. In this work, we demonstrate the feasibility of our real-time distributed system for intrusion detection on IoT devices.
Demo Abstract: Catch My Eye: Gaze-Based Activity Recognition in an Augmented Reality Art Gallery

The personalization of augmented reality (AR) experiences based on environmental and user context is key to unlocking their full potential. The recent addition of eye tracking to AR headsets provides a convenient method for detecting user context, but complex analysis of raw gaze data is required to detect where a user's attention and thoughts truly lie. In this demo we present Catch My Eye, the first system to incorporate deep neural network (DNN)-based activity recognition from user gaze into a realistic mobile AR app. We develop an edge computing-based architecture to offload context computation from resource-constrained AR devices, and present a working example of content adaptation based on user context, for the scenario of a virtual art gallery. It shows that user activities can be accurately recognized and employed with sufficiently low latency for practical AR applications.
Using Ubiquitous Mobile Sensing and Temporal Sensor-Relation Graph Neural Network to Predict Fluid Intake of End Stage Kidney Patients

End-Stage Kidney Disease (ESKD) patients on hemodialysis suf-fer from kidney failure, with the inability to remove excess fluid causing fluid overload. This can cause many morbidities, and is one of the most insidious and common risk factors for mortality in ESKD patients. Existing solutions for fluid intake monitoring such as self-report and weight gain monitoring are burdensome, non-continuous, and usually administered in clinics only. It is then critical to develop a ubiquitous fluid intake monitoring system to help ESKD patients better control their fluid consumption. In this study, we propose to leverage smartwatch sensor data (e.g., Photoplethysmography (PPG), Gyroscope, etc.) combined with a temporal sensor relation graph neural network (TSR-GNN) to predict fluid intake given past sensing data between two dialysis sessions. Our empirical experiments highlight promising findings about the feasibility of using ubiquitous sensing to predict fluid intake, and demonstrate that the proposed model TSR-GNN outperforms the selected baseline models in both accuracy and robustness. Additionally, an in-depth analysis of model interpretability by attention weights and GNNExplainer variant is conducted to better under-stand the inter-sensor interactions and sensor contributions to the fluid intake prediction results.
Application Implementation of K-means Algorithm Based on Wolf Pack Algorithm

K-means in non-hierarchical clustering analysis has become the most commonly used clustering algorithm because of its simple implementation and fast convergence speed. However, different selection of clustering center will greatly affect the clustering effect of k-means. If the clustering center is selected randomly, the algorithm will easily fall into the local optimal value and fail to achieve the optimal effect. Therefore, the uncertainty of initial clustering centers makes k-means algorithm lack of good stability. Thus, a K-means algorithm based on AEWPA (KAEWPA) is proposed in this paper to enhance the stability of clustering.
The Use of Recyclable Rubber Materials in Children’s Recreation Facilities

Recreation facility is a key point to children's approach to the society in their early life.Through both information and field research, the demands of children and parents for recreation facilities have been learned about.In order to create a safe environment for children's entertainment, based on properties and usage of recyclable rubber materials, as well as the summarization of different physiological activities at different ages, this paper designs the recreation facilities in urban parks.
DataSpecer: A Model-Driven Approach to Managing Data Specifications

AbstractIn this paper, we demonstrate DataSpecer, a tool for effortless management of data specifications based on a domain ontology. Using DataSpecer, the users can generate technical artifacts such as data schemas, e.g., in JSON Schema or XML Schema, and human-readable documentation for a specific dataset based on the provided ontology while maintaining the semantic mapping from the generated artifacts to the ontology. This significantly eases the task of developing data specifications and keeping the corresponding technical artifacts consistent in the process. The tool is based on a previously studied model-driven development (MDD) approach [5] that divides data modeling into layers. This approach was already partially implemented in the tools XCase [2] and eXolutio [3], however, only for XML Schemas and only based on a manually created model, not on an existing domain ontology. Our current tool provides support for the implementation of artifact generators for any target format, including human-readable documentation, and supports domain ontologies as the starting point of the MDD. The tool is already in use in Czechia with the Semantic Government Vocabulary (SGOV) [4] serving as the domain ontology.KeywordsData schemaData modelingOntologyAutomation
Semantic Video Entity Linking

Knowledge graphs are an established technology in the field of information retrieval and question answering. However, the focus is mostly on searching web pages and related documents and less on video formats, resulting in the fact that queries on videos for refining the search are often neglected. In this demo, we show a framework for recognizing faces in YouTube videos and linking them to the matching entities in DBpedia using the thumbnails available in DBpedia. By linking the videos from YouTube with the information from DBpedia, more complex search queries can be made possible. We will present both the frontend of the application, including the search, adding more YouTube videos and formulating complex queries, as well as the architecture and the libraries used in the application.
Exploration of Hybrid College English Teaching Mode Based on Big Data and Internet

The Era of Big Data has brought tremendous changes and possibilities for the transformation of individualized English teaching.Digital College English teaching mode based on big data is a challenge to the traditional teaching mode.This paper explores the application of "big data" in the process of teaching design, teaching content, teaching methods and teaching evaluation of hybrid English teaching.Meanwhile it compares data from the perspective of learning motivation, method, academic performance and satisfaction with the reference group and promotes digital teaching experiments.The teaching mode can not only give full play to teachers' leading role of guiding, enlightening and monitoring in the teaching process, but also vividly reflect students' initiative and enthusiasm as learning subjects and creativity.Hybrid College English teaching mode, based on big data and Internet, as one of the important teaching approaches, is a key factor affecting the future English educational reform.
Burden of Persuasion in Meta-argumentation

This work defines a burden of persuasion meta-argumentation model interpreting the burden as a set of meta-arguments. Bimodal graphs are exploited to define a meta level (dealing with the burden) and an object level (dealing with standard arguments). Finally, an example in the law domain addressing the problem of burden inversion is discussed in detail.
The Inclusion of Ideology in Jokowiâ€™s Speech Viewed from the Perspective of Appraisal Theory

Ideology is a belief towards a particular idea which is commonsensical as it is required by the society as the answer of particular issues. In common practices, ideology might become considerable since it is realised through the use of words that are meaningful and powerful. One language feature that can be used to convey ideology is appraisal resources. This research aimed to describe the inclusion of ideology viewed from the perspective of appraisal theory. It employed descriptive qualitative approach. The object of the study is a speech text performed by Jokowi in the World Economic Forum of Asean 2018. The results of the study depicted that there were seven ideology strategies that had been implemented viewed from the attitude appraisal analysis. The seven ideologies were implication, consensus, hyperbole, pre-supposition, national self glorification, lexicalization, and irony. Besides, the ideology inclusion in Jokowiâ€™s speech viewed from the perspective of engagement appraisal pointed that there were six ideology strategies that were implemented by the speaker. They are implication, evidentially, pre-supposition, national self glorification, hyperbole, vagueness, and consensus. In addition, the inclusion of ideology in Jokowiâ€™s speech viewed from the perspective of graduation appraisal conveyed that there were four ideologies applied in the speech, they were implication, hyperbole, national self glorification, and number game. By taking account of the results, this study is expected to escalate the quality of English teaching and learning dealing with words understanding beyond the meaning.
Usability Testing pada Aplikasi POTWIS

This research was conducted to evaluate POTWIS application by highlighting three usability concepts that effectiveness, efficiency, and the satisfaction of the user in using POTWIS application measured by usability testing methode, Performance Measurement, RTA, UEQ, and SUS technique. The purpose is to know the result of usability testing and improvement recommendation on POTWIS application based of the result of usability testing.The result of this research is based on analysis of statical calculation Mann Whitney U Test, UEQ dan SUS which showed (1) POTWIS application is not effective seen from the existance of the mistake made by respondent during the task, (2) POTWIS application is efficient seen from the time of task execution because the execution time between proficient group and beginner group was not significantly different, (3) POTWIS application has not met the scale of efficiency measured by UEQ with score 0.625 which showed below the average and (4) POTWIS application has not fulfilled user satisfaction measured by SUS with score 53.00 which is stated still below the average score is &gt; 68.
Self-Conditioned GANs for Image Editing

Generative Adversarial Networks (GANs) are susceptible to bias, learned from either the unbalanced data, or through mode collapse. The networks focus on the core of the data distribution, leaving the tails — or the edges of the distribution — behind. We argue that this bias is responsible not only for fairness concerns, but that it plays a key role in the collapse of latent-traversal editing methods when deviating away from the distribution's core. Building on this observation, we outline a method for mitigating generative bias through a self-conditioning process, where distances in the latent-space of a pre-trained generator are used to provide initial labels for the data. By fine-tuning the generator on a re-sampled distribution drawn from these self-labeled data, we force the generator to better contend with rare semantic attributes and enable more realistic generation of these properties. We compare our models to a wide range of latent editing methods, and show that by alleviating the bias they achieve finer semantic control and better identity preservation through a wider range of transformations. Our code and models will be available at https://github.com/yzliu567/sc-gan
Hybrid Aquila Optimizer with Moth-Flame Optimization Algorithm for Global Optimization

Aquila Optimizer (AO) is a recent search method proposed in 2021 to solve different optimization problems (OPs). One of the best-published search methods in the literature is Moth-Flame Optimization (MFO) algorithm. This paper gives a new version of AO based on hybridizing the Aquila Optimizer and Moth-Flame Optimization algorithm, called AOMFO. The proposed method takes advantage of the Aquila search operators to conduct an exhaustive search (exploration) and takes advantage of the MFO algorithm to conduct a deep search (exploitation). To validate the ability of the proposed hybrid AOMFO method in solving the OPs, 23 slandered benchmark functions are used. The results of the proposed AOMFO are compared with other well-known methods. The results clearly showed that the proposed hybrid AOMFO obtained better results than other well-known methods and reached the best solutions in several test cases.