Reviewerly/exHarmony huggingface dataset:
Worldview Function Of Ethno Pedagogical Theory In Teacher Training In Globalization Context

The world community strives to create a global strategy for human education regardless of place of residence and educational level.This can lead to solving many problems if the images broadcast by the general, global education system are adequate for the real World.At the same time, this can dramatically accelerate the movement of our civilization towards the fateful line, if images that are not adequate in reality are deliberately or ignorantly embedded in this global system.In our time, trends in the development of the world educational space are predicted, types of regions are distinguished on the basis of the interaction of educational systems and their response to integration processes.This paper examines the features of the individual's worldview at the present stage.The author describes the main trends in the development of the modern educational environment, the signs of education in the era of globalization, the principles of the world education system.The author presents a model of the structure of a person's worldview and traces the transformations in the structure of a person that occur in the process of forming a worldview.The author analyses the role of ethno-pedagogical theory in the formation of the worldview of future teachers and formulates the tasks of ethno-pedagogical training and the principles.
PENERAPAN METODE HIERARCHICAL TOKEN BUCKET PADA JARINGAN INTERNET WARUNG JAWA

Bandwidth management is very important in a computer network concept. Bandwidth management functions to regulate network bandwidth so that each network user gets an evenly distributed bandwidth as a whole, even though there are many network users. In this Java shop, the distribution of bandwidth to each client has not been implemented. , there is no bandwidth management on the Java cafe internet network. The results of the internet speed test at the Java cafe use speedtest.net. The result is the distribution of bandwidth between client 1 and client 2 is not evenly distributed. On client 1 get download and upload speeds up to 0.65 and 1.14 Mbps. While on client 2 only get 2.9 and 1.14 Mbps, due to the absence of a bandwidth management system. Hierarchical Token Bucket (HTB) is a method of bandwidth management, HTB can provide more bandwidth to the client in a few seconds the client will get more bandwidth, the research methodology used is (Network Development Life Cycle) with a system development method for the network which includes the stages of Analysis, Design, Simulation, Implementation, Monitoring, and Management. The results show that bandwidth distribution in each client can be done with each client getting a maximum bandwidth of 768Kbps and getting a bandwidth limit-at of 268Kbps so that customers can still get an internet network even though internet traffic is heavy. . and the use of burst limit as a research method can make each client get additional bandwidth when the internet network is empty and can be accepted by clients or visitors to Java stalls.. and the use of burst limit as a research method can make each client get additional bandwidth when the internet network is empty and can accepted by clients or visitors to shop jawa..
IMPROVING CHINESE MEDICAL NAMED ENTITY RECOGNITION USING GLYPH AND LEXICON

Abstract. Medical named entity recognition is the first step in processing electronic medical records. It is the basis for processing medical natural language text information into medical structured information, which has extremely high research value and application value. In this paper, we have proposed a model that aims to identify various types of named entities such as disease, imaging examination, laboratory examination, operation, drug, and anatomy from Chinese electronic medical record. We construct a fusion Glyph and lexicon model based on BERT. Experimental studies have shown that increasing character-level semantic representation can improve the performance of named entity recognition. In order to boost it, the major measures of our model include: (1) a CNN structure is proposed to capture glyph information. (2) Soft-Lexicon method is introduced to encode lexicon information. Our models show an improvement over the baseline BERT-BiLSTM-CRF model. The experimental results on CCKS2019 dataset showed that the F1 score was 84.64, which was +1.99 higher than the baseline level.
The Language Laboratory in Denmark

In this article on the language laboratory situation in Denmark I shall confine myself to the university level, which includes, besides universities, teachers' training colleges and commercial colleges.To describe this level, however, a brief survey of the students' linguistic background and qualifications will be useful. QualificationsWhen a Danish student begins studying at university level, he has been learning English for seven or eight years, German for five or six, and probably French for three years.A small number of students take Russian instead of French.The majority of language students take one or two of the three main foreign languages taught in Danish schools.It is, of course, possible to study other languages ab initio.However, since the number of students doing so is comparatively restricted, there has not been much effort invested in software for these languages until just recently.
An Enhanced Approach for Sentiment Analysis Using Association Rule Mining

Abstract: In today’s world social networking platforms like Facebook, YouTube, twitter etc. are a great source of communication for internet users and loaded with large number of emotions, views and opinions of the people. Sentiment analysis is the study of attitudes, emotions and opinions of the people and is also known as opinion mining. Sentiment analysis is used to find the opinion i.e. negative or positive about a particular subject. In this paper an Enhanced sentiment analysis approach is presented by using the Association rule mining i.e. Apriori and machine learning approach such as Support Vector Machine. The Enhanced approach is compared with the baseline approach, on accuracy, precision, recall, and F1-score measures. The Enhanced approach for sentiment analysis is implemented using the R programming language. The Enhanced approach shows better performance in comparison to the baseline approach. Keyword: Sentiment Analysis, Opinion Mining, Support Vector Machine, Association Rule Mining, Machine Learning
An Online E-voting System based on an Adaptive Ledger with Singular Value Decomposition Technique

Regular E-voting systems for elections may count the votes in less time,less cost,save the privacy of citizens,but still considered risky as votes can be tampered.E-voting systems based on a network distributed ledger show fast results,more trusted,save privacy,cannot be tampered,and distributed in which no central organization controls the system.This paper illustrate an e-voting system to solve the challenge of a massive ledger that is distributed among network-nodes using a data reduction technique as a security-matching-tool,singular value decomposition(SVD) that handle a copy of election results in another form and matched with the SQL-database results to announce a successful election-event representing a transparency-powerful-secured-system
Comparison of Indonesian Population Information System Service Performace Based On Information System Functional Scorecard Theory

The Balikpapan Population and Civil Registration Office has implemented service automation by using an information system to support the population and civil registration administration services in Balikpapan, Indonesia. The information system implemented is called The Population Administration Information System (SIAK). The office has previously implemented an information system similar to SIAK, namely the Population Administration Organizing System (SPAK). One of the office aims is to improve the quality of SIAK. SIAK quality is improved in order to achieve optimal service. As a step of achieving vision and mission, implementing work functions and optimizing the utilization of information systems, the comparison of information system performance between information systems that have been used with information systems that have previously been used. The guidelines used in assessing the performance of both systems are Information System Functional Scorecard (ISFS) theory. The data collected by questionnaires then conducted Paired-T Test so that the results of the two systems were obtained differences and can be compared to know which system is superior. SIAK is an information system that is declared superior to SPAK. Furthermore, the comparison results become a reference in providing recommendations for SIAK performance improvement based on the performance comparison results with SPAK.
Painting With Light: Artistic Experiments into the use of Virtual Reality as an Animation Production Environment

While many researchers have examined the technical characteristics of using VR as a production environment for animation, its artistic potential has only sporadically been investigated. We want to contribute to this line of thought through reflection on a number of expanded animation workshops organized in the context of the Painting With Light project. In this paper we use flow theory in order to discuss the experience of using VR as a 3D prototyping tool. Our findings suggest that this practice can add an improvisatory and co-creative dimension to animation.
The Analysis of Grammatical Errors in University Students’ Essays

This study aims to identify and analyze grammatical errors in student essays. In this case, every student's grammatical error consisting of errors in the use of diction, syntactic structure and meaning were identified and described in detail within the framework of the correct grammatical rules. This research was conducted using a qualitative descriptive method because the data collected was in the form of language use, especially grammatical errors. The subject of this research is English Education Study Program students who joined essay writing courses. Meanwhile, the object of research was grammatical errors in student essays. The data were collected through the collection of student essays obtained from the end-of-semester test and interview guide. The percentage of grammatical errors made by students in their essays in sequence were as follows; fragments: 280 (17.5%), runs on: 235 (14.7%), verb forms and verb tense: 225 (14.1%), passive: 220 (13.8%), linking verb deletion: 160 (10 %), plural disagreement: 120 (7.5%), unclear reference: 115 (7.2%), proposition misuse 89: (5.6%), subject-verb disagreement: 80 (5%), and parallelism: 74 (4.6%). Grammatical errors that needed to be considered more seriously were parallelism and passivity, which, although they look low in percentage terms, for they were rarely used in student essays; but when both forms were present, the grammatical construction tended to be wrong. In addition, both forms were also difficult to understand, compared to other types of errors that could be learned quickly.
A SIFT Description Approach for Non-Uniform Illumination and Other Invariants

The new improvement keypoint description technique of image-based recognition for rotation, viewpoint and non-uniform illumination situations is presented. The technique is relatively simple based on two procedures, i.e., the keypoint detection and the keypoint description procedure. The keypoint detection procedure is based on the SIFT approach, Top-Hat filtering, morphological operations and average filtering approach. Where this keypoint detection procedure can segment the targets from uneven illumination particle images. While the keypoint description procedures are described and implemented using the Hu moment invariants. Where the central moments are being unchanged under image translations. The sensitivity, accuracy and precision rate of data sets were evaluated and compared. The data set are provided by color image database with variants uniform and non-uniform illumination, viewpoint and rotation changes. The evaluative results show that the approach is superior to the other SIFTs in terms of uniform illumination, non-uniform illumination and other situations. Additionally, the paper demonstrates the high sensitivity of 100%, high accuracy of 83.33% and high precision rate of 80.00%. Comparisons to other SIFT approaches are also included.
A Dynamic Clustering Method of Hot Topics Based on User Interaction and Text Similarity

This paper proposes a dynamic clustering method for hot topics based on user interaction and text similarity. It focuses on the analysis of the clustering process from the perspective of movement and combines the two aspects of text similarity and user interaction to comprehensively consider the topic clustering of microblogs, improve the accuracy of clustering. The simulation results demonstrate that the clustering process is dynamic and can be displayed intuitively. Moreover, the model has strong extensibility, which parameters can be added, deleted and changed according to individual needs, and can be personalized for various applications.
The Constraints between Edge Depth and Uncertainty for Monocular Depth Estimation

The self-supervised monocular depth estimation paradigm has become an important branch of computer vision depth-estimation tasks. However, the depth estimation problem arising from object edge depth pulling or occlusion is still unsolved. The grayscale discontinuity of object edges leads to a relatively high depth uncertainty of pixels in these regions. We improve the geometric edge prediction results by taking uncertainty into account in the depth-estimation task. To this end, we explore how uncertainty affects this task and propose a new self-supervised monocular depth estimation technique based on multi-scale uncertainty. In addition, we introduce a teacher–student architecture in models and investigate the impact of different teacher networks on the depth and uncertainty results. We evaluate the performance of our paradigm in detail on the standard KITTI dataset. The experimental results show that the accuracy of our method increased from 87.7% to 88.2%, the AbsRel error rate decreased from 0.115 to 0.11, the SqRel error rate decreased from 0.903 to 0.822, and the RMSE error rate decreased from 4.863 to 4.686 compared with the benchmark Monodepth2. Our approach has a positive impact on the problem of texture replication or inaccurate object boundaries, producing sharper and smoother depth images.
CONCEPTUAL FRAMEWORK FOR HEAVY-DUTY VEHICLE PLATOONING IN PHYSICAL INTERNET SYSTEMS

One of today's most significant challenges is sustainability, which is closely linked to environmentally friendly solutions and resource efficiency. As a solution to these goals, the concept of the Physical Internet emerged, defining the logistics network of the future as a global, open, and interconnected system. Concerning the conditions of vehicles based on Physical Internet-based systems, we cannot ignore the latest vehicle technology innovations that appear more and more intensively in parallel. The framework proposes planning at the strategic, tactical, and operational levels. Different levels of coordination implement different approaches to platoon coordination in line with the network architecture of PI-based logistics systems. We recommend the highest level of offline design in fixed π-hubs. The tactical level involves designing π-hubs online. We propose the implementation of speed-based solutions at the operational planning level.
Research on interactive recommendation system based on reinforcement learning

The recommendation system plays a strong auxiliary role in the user's handling of information overload. For products, user comments and descriptions are important reference data. Based on reinforcement learning, more accurate recommendations can be made through the interaction between users and product-related data items. However, the interactive recommendation will face some problems caused by data sparseness. For text data information, users and items can be mapped to the feature space to alleviate this problem. This paper proposed the depth deterministic strategy gradient algorithm is used to train the recommendation model. The experimental results on three real data sets show that the model has a relatively accurate recommendation effect and acceptable running time, and it can alleviate the impact of data sparsity on the recommendation effect to a certain extent.
Detection of Small Size Traffic Signs Using Regressive Anchor Box Selection and DBL Layer Tweaking in YOLOv3

Traffic sign recognition is a key module of autonomous cars and driver assistance systems. Traffic sign detection accuracy and inference time are the two most important parameters. Current methods for traffic sign recognition are very accurate; however, they do not meet the requirement for real-time detection. While some are fast enough for real-time traffic sign detection, they fall short in accuracy. This paper proposes an accuracy improvement in the YOLOv3 network, which is a very fast detection framework. The proposed method contributes to the accurate detection of a small-sized traffic sign in terms of image size and helps to reduce false positives and miss rates. In addition, we propose an anchor frame selection algorithm that helps in achieving the optimal size and scale of the anchor frame. Therefore, the proposed method supports the detection of a small traffic sign with real-time detection. This ultimately helps to achieve an optimal balance between accuracy and inference time. The proposed network is evaluated on two publicly available datasets, namely the German Traffic Sign Detection Benchmark (GTSDB) and the Swedish Traffic Sign dataset (STS), and its performance showed that the proposed approach achieves a decent balance between mAP and inference time.
DESIGN AND IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE-BASED HEART DISEASE DIAGNOSIS SYSTEM

As one of the deadliest diseases in the world, heart disease requires serious treatment. The weaknesses of providing services for heart disease in Bali Province are that there is no online diagnostic system to make it easier for people to check their health conditions to find out whether they have heart disease. Based on this research, the design and implementation of a web-based online heart disease diagnosis system are carried out. The diagnostic system uses Artificial Intelligence and inputs data from the user based on several questions posed by the system. This research uses Case-Based Reasoning (CBR) algorithm with Design Science Research Methodology (DSRM) and a case study qualitative research method. The test results show that the system designed and implemented can run well and perform accurate diagnostics according to the design and user needs.
An Image Comment Method Based on Emotion Capture Module

The task of generating comments for images has been studied by many scholars. Existing methods, such as sentence generation based on recurrent neural network (RNN), have poor variability due to their structural limitations. In addition, the generated comment is so stiff that lacks the emotional color and value orientation of human language. In order to make the image comment more open and meet the needs of emotion control, we designed a comment generation and style transfer module for pictures. This paper mainly completes the following work: (1) Imitate human emotions by giving positive and negative colors to the resulting sentences. Using the characteristics of generative adversarial network, comments can not only meet the requirement of verisimilitude, but also encourage naturalness and diversity. (2) Design an emotion capture module (ECM) and a more compound loss function. It can complete the emotional reversal of the caption content that needs to be controlled, making it more colloquial and life-like. Experimental results on public datasets and generated dataset show that the proposed model has good effect on style transfer accuracy and content preservation.
Students Perception in Technology Integration and its Alignment within the ISTE Standards

This is an article on students' perception of the integration of technology in a college during the Spring of 2019.The SAMR Model was used as the framework which categorizes four different degrees of classroom technology integration.The study identified that there were positive results in students' performance based on the experiment which was conducted between two groups of control and experimental.There were 60 college students who participated and 30 went through interventions while the rest 30 students were controlled group.A questionnaire was distributed to the experimental group to gather information on their perception of the effectiveness of the integration.The survey identified that the perception of students on the impact of technology was positive based on eight factors such as enjoyment, building confidence, enhancing skills, active involvement of students to name a few.These factors were also discussed based on the Social Cognitive Theory of behavioral, cognitive, and environmental factors.The study also was found aligned with the 7 ISTE Standards for Students (2016).
Driving value creation in the new economy following the COVID-19 crisis. Data-mining students’ satisfaction from online teaching in the virtual academic climate.

&#x0D; This study examines the advantages of online teaching from the perspective of students at eleven institutions of higher education, universities and colleges, in Israel. The study was conducted at the end of the second semester of their academic studies, after students had experienced “face to face” studies, and they were asked to reply freely to an open question on their evaluation of the benefits of transitioning to online teaching. Students were forced to cope with a new reality, where they were compelled to study in a “digital classroom”. The academic-social climate, the conduct of lecturer-student relations, and the relations among the students themselves, changed instantaneously, with no preparation by any of those involved. Hence, the research findings can illuminate the strengths of online teaching, with a view to the future. Was the impact of teaching and learning in the coronavirus circumstances a one-time event for the students or one from which it is possible to examine and embrace new ways of learning as they see them?&#x0D; &#x0D; Based on 1,937 fully completed surveys from eleven academic institutions, we employed a mixed methods research design. Major themes were manually tagged, and an empirical model was developed. Structural Equation Modeling (SEM) was utilized to test the model’s goodness-of-fit. Findings present a host of parameters that have a significant positive influence on students’ positive perception of the transition to online teaching. &#x0D; &#x0D; This study is the first to thoroughly examine advantages and disadvantages of the transition to online teaching among a large group of students, from several different academic institutions, and presents both qualitative and empirical results. Ethical implications of the findings are discussed.&#x0D;