Reviewerly/exHarmony huggingface dataset:
CHSE Rafting Guide Implementation Analysis in the New Normal Era on Payung Rafting Tourism Object

Introduction: The development of the tourism sector in Indonesia is so fast, but since the COVID-19 pandemic, tourist visits have decreased. In the new normal era, there is a change in behavior to continue to carry out activities by implementing health protocols. Several tourist attractions began operating with the implementation of health protocols. The government issued guidelines for the implementation of Cleanliness, Health, and Safety Environmental Sustainability (CHSE). CHSE rafting tour is guided to meet the needs of tourists for clean, healthy, safe, and environmentally friendly tourism products and services during the COVID-19 pandemic. Payung Rafting Managers try to implement health protocols to the maximum. This study was conducted to determine the extent to which Payung Rafting managers apply the CHSE Guidelines.&#x0D; Methods: This research is quantitative study with cross sectional design. Analysis of the fulfillment of the CHSE rafting guidelines at the Payung Rafting tourist attraction was carried out, using the CHSE rafting instrument released by the Ministry of Tourism and Creative Economy. This activity has been carried out on August 9, 2021.&#x0D; Results: The results showed that Payung Rafting was able to meet 69% of the CHSE guidelines, while based on the results of the Pearson Chi-Square test, a value of 0.119 was obtained with a significance value of 0.730 (p&gt; 0.05).&#x0D; Conclusion: This study statistically shows there is no difference between the CHSE guidelines and implementation in Payung Rafting, but descriptive analysis shows that Payung Rafting can apply 69% of the CHSE guidelines.
Model-free inverse reinforcement learning with multi-intention, unlabeled, and overlapping demonstrations

Abstract In this paper, we define a novel inverse reinforcement learning (IRL) problem where the demonstrations are multi-intention, i.e., collected from multi-intention experts, unlabeled, i.e., without intention labels, and partially overlapping, i.e., shared between multiple intentions. In the presence of overlapping demonstrations, current IRL methods, developed to handle multi-intention and unlabeled demonstrations, cannot successfully learn the underlying reward functions. To solve this limitation, we propose a novel clustering-based approach to disentangle the observed demonstrations and experimentally validate its advantages. Traditional clustering-based approaches to multi-intention IRL, which are developed on the basis of model-based Reinforcement Learning (RL), formulate the problem using parametric density estimation. However, in high-dimensional environments and unknown system dynamics, i.e., model-free RL, the solution of parametric density estimation is only tractable up to the density normalization constant. To solve this, we formulate the problem as a mixture of logistic regressions to directly handle the unnormalized density. To research the challenges faced by overlapping demonstrations, we introduce the concepts of shared pair , which is a state-action pair that is shared in more than one intention, and separability , which resembles how well the multiple intentions can be separated in the joint state-action space. We provide theoretical analyses under the global optimality condition and the existence of shared pairs. Furthermore, we conduct extensive experiments on four simulated robotics tasks, extended to accept different intentions with specific levels of separability, and a synthetic driver task developed to directly control the separability. We evaluate the existing baselines on our defined problem and demonstrate, theoretically and experimentally, the advantages of our clustering-based solution, especially when the separability of the demonstrations decreases.
Word Frequencies in the Written Texts of Learners of Turkish as a Foreign Language at CEFR Level A

This study aims to list the vocabulary items used by learners of Turkish as a foreign language at CEFR level A and their frequency values, and reveal the factors affecting the differences in the word frequency lists created based on certain demographic criteria. It also aims to compare the lists with the frequency lists of the course book used by the learners and the list of words frequently used by native speakers of Turkish. For these purposes, written texts of 123 students at Ankara University TÖMER were collected and digitized, and using all the words in those texts, a lemma list was created for this learner corpus. Then, separate lists were created to show how certain variables might affect the frequency of words used by learners. The results show that the most significant factor leading to differences in the lists is the level the students were studying at.
EFFECTIVENESS OF USING ENGLISH SONGS TO IMPROVE VOCABULARY MASTERY AMONG EFL ELEMENTARY SCHOOL STUDENTS IN SETU BABAKAN AREA

Vocabulary refers to words that people use to communicate with others. Vocabulary plays as a fundamental role in learning a language and make English vocabulary mastery as the most important skill that a person needs to master before having the other English skills. This study aims to analyze the effectiveness of English songs to teach English vocabulary for elementary school students. This study applied quantitative research with one group pretest-posttest pre-experimental research design to analyze whether the media chosen in this study is effective to be used in English vocabulary learning classes. A total of 20 elementary school students who learnt in Fun Course program chosen as the samples using purposive sampling. Paired sample t test was analyzed and obtained tcount 8,156 at α = 0,05 (5%) ttable 1,729 (tcount 8,156 &gt; ttable 1,729). Thus, the alternative hypothesis (Ha) in this study is accepted and null hypothesis (H0) in this study is rejected which states that there is a significant effectiveness of using English songs in improving elementary school students’ vocabulary mastery in RT 9, Setu Babakan area.
Screening for anaemia in children’s of age group 6 months to 5 years using palmar pallor and digital hemoglobinometer by finger prick method

Background: Screening for anemia in children’s under five years of age using palmar pallor and Digital hemoglobinometer by finger prick method. Methodology: A total of 200 subjects were enrolledfrom outpatient department of ACS Medical College. Children of the age group 6 months to 5 years were considered in the study. Out of the total, 84 were boys and 116 were girls. Blood sampleswere collected from the subjects using a finger prick method and measured using Digital Haemoglobinometer. Data was collected and result was analysed using SPSS software. Results: Out of the 200 children examined, 91 (45%) were found to be no palmar pallor, 102 (51%) were some palmar pallor and 7 (4%) were severe anemia (severe palmar pallor),Whereas using Digital Haemoglobinometer, 64 (34%) no anemia (Hb ≥11 mg/dL), 115 (57%) children’s were found to beanemic (Hb &lt;11 mg/dL) and 18 (9%) were severe anemiawith haemoglobin levels &lt;7 mg/dL.The age of the children ranged from 6 months to 5 years with mean age of 3 years. Conclusion: Digital Hemoglobinometer (DH) can significantly reduce misdiagnosis of anaemia compared with clinical assessment alone.
PELATIHAN VISUAL BRANDING DAN COREL DRAW BAGI PELAKU UMKM DI KECAMATAN GEDONG TATAAN.

This visual branding training for UMKM prioritizes the crucial role of logos as the main component of visual branding for product marketing activities for benefits and how to design a logo that is easy to remember in accordance with logo principles so that the logo can give a memorable impression to potential consumers. The achievement of this activity is how the future training participants will know and be able to produce logo designs that represent the image of their business. Presentation of visual branding materials using presentation, discussion and practice methods. Presentations are used when the service team explains the function of visual branding and its components such as logos, taglines and promotional strategies. In the stage of introducing the graphics application, the service team gave the Corel Draw module to the trainees. The first day of the training, after the presentation of the material, the service team immediately installed the application on the trainees who brought laptops. The service team asked participants to understand and study the tools in Corel Draw in accordance with the content of the modules that have been given. Keywords— Trainning, Visual Branding, Gedong Tataan.
[Characteristics of Heavy Metal Pollution in Farmland Soil of the Yangtze River Economic Belt Based on Bibliometric Analysis].

The Yangtze River Economic Belt is one of the major strategic development regions in China. It is of great significance to clarify the characteristics and sources of heavy metal pollution in farmland soil of the Yangtze River Economic Belt for the prevention and control of heavy metal pollution and to ensure safe agricultural production. After collecting extensive literature data, we analyzed the pollution characteristics, environmental risk, and potential sources of heavy metals (Cd, Cr, Hg, As, Pb, Cu, Zn, and Ni) in farmland soil of the Yangtze River Economic Belt through the integrated use of spatial and geo-accumulation index analyses. The results showed that:① the proportion of soil samples exceeding the risk screening values for soil contamination of agricultural land of Cd, Cu, Pb, Hg, Zn, and As were 39.8%, 18.5%, 8.3%, 6.9%, 6.9%, and 6.4%, respectively. Compared with the risk standard, soil Cd had the highest rate of exceeding the standard. ② The contents of Cr, Cu, Zn, and Ni in the upper reaches were higher than those in the middle and lower reaches, and the contents of Cd, As, and Pb in the middle reaches were higher than those in the upper and lower reaches. ③ The results of the geo-accumulation index analysis showed that the contamination degree of the eight heavy metals decreased in the order of Cd(0.42)>Hg(-0.28)>Pb(-0.32)>Zn(-0.39)>Cu(-0.42)>Cr(-0.7)>As(-0.81)>Ni(-0.73), where the accumulation risk of soil Cd and Hg was relatively higher. ④ Higher environmental background and mining activities were the main factors affecting the accumulation of heavy metals in soils in the upper and middle reaches. By contrast, rapid urbanization, industrial production, and intensive agricultural activities were the main factors affecting the heavy metal accumulation in soils in the middle and lower reaches. In view of the current status and control needs of heavy metal pollution in the Yangtze River Economic Belt, it is recommended to strengthen source prevention and control and to also carry out hierarchical control and regional classification management of heavy metal pollution in farmland soils according to the degree of heavy metal pollution, geological background, and quality of agricultural products. The ultimate objective of this research was to archive the farmland soil environmental quality safety and agricultural green sustainable production in the Yangtze River Economic Belt.
Enhance User Interface to Deaf E-Learning Based on User Centered Design

Abstract—The cognitive learning approach through visual media is a characteristic of learning for deaf students because these students can receive learning more quickly. However, this method became an obstacle when this process was online conducted because of the effect of the Covid-19 pandemic. Based on the explanation, a need for media learning based on interactive media can help students in the studying process. This research focuses on developing learning media using User Center Design (UCD) method to a center for the development system. In this research, we develop a user interface (UI) for deaf students, especially in the Putra Asih inclusive school in the city of Kediri, Indonesia. The evaluation of this research using ISO-9241 shows the effectiveness of using the application obtained 87,15%, effectiveness of the user interface design obtained 80,05%, and user satisfaction obtained 71,18% where all parameters make sense and acceptable based on a response from the users.
Packet Filtering Automation System Design Based on Data Synchronization on IP Profile Database Using Python

Data traffic on a dense network is a threat to cybercrime and a high vulnerability for technology companies so that the challenges to prevent it will be more diverse. Adding a strengthening of the boundary wall or firewall and sorting data through packet filtering plus writing firewall rules to prevent malware and attacks from outside at the network device level is an alternative to protect the traffic you have. When heavy traffic makes data exchange uncontrollable, this research will create an automation design so that the sorting of incoming data packets through selection based on the specified rules runs in real-time so that the prevention of crime that enters the network is more swiftly handled. The system is running successfully by connecting the MQTT Collector as a subscriber that uses the python programming language to retrieve profiling data from the IP Profile database. The system was tested on a mikrotik router RB951Ui-2HnD which then the blocking track record will be stored in the Dynamic Firewall Data database in MongoDB. Also added a tool for controlling data in storage in the form of a program release. The results of the test show that data with an average base score above 20 is blocked and then stored in the block list in checking the collection in the database every 30 seconds. In addition, the data in the database will be checked every day within 30 days which will then be released and recorded in the release log in MongoDB.
Semiotic capacity of insanity: thought disorder modelling as a tool for possible text world making (based on texts by S. Lem)

The thought disorders issue is common for the literature and is supposed to be used in different ways: from revealing the conundrum between the psychic of the character and the outer world to creation of the hypertext, multifunctional view of the text world. It was S. Lem who did use the thought disorders to create the fantasy view of the world which was supposed to be drastically different from the reality. The focus of this article will be on the study of examples of making the possible text world by means of thought disorders expressions in the texts by S. Lem (Fables for Robots, The Star Diaries, The Futurological Congress). Particularly, we are inclined to analyze indexes, icons and symbols as the sign system of the text, that enable the author to create the fantasy view of the text world. Thus, one might recognise an example of the dissolved thinking in the dialog between Ijon Tichy and the sales-assistant about sepulkas. This thought disorder is characterised by wrong, unusual complex of opinions, when random concepts are wired one after another without any sense. As a result, the chain of senseless and incomprehensible concepts is made, for instance: sepulkas, sepulkarium, kachezh and so on. The semiotic capacity of these signs comprises the distance from reality, when characters use unknown concepts seemingly well-known for other characters in the text. In conclusion, the fantasy view of the text world appears to be more seomiotically autonomous and independent from the real world.
Improving Accuracy of Code Smells Detection with Data Balancing Techniques

Abstract Code smells are indicators of potential symptoms or problems in software due to inefficient design or incomplete implementation. These problems can affect software quality in the long term. Code smell detection is fundamental to improving software quality and maintainability, reducing the risk of software failure, and helping to refactor the code. Data imbalance is the main challenge of machine learning (ML) techniques in detecting the code smells. Several prediction methods have been applied for code smells detection in our previous works. However, many of them show that, ML methods is not always suitable for code smells detection due to the problem of highly unbalanced data. To overcome these challenges, the objective of this study is to present a code smells detection method based on ML models with data balancing techniques to mitigate data unbalancing issues by taking a corpus of Java projects as experimental datasets. In our experiments, we have used Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). The performance of these models has been evaluated based on accuracy, precision, recall, f-measure, ROC curve. The results indicate that the use of data balancing techniques had a positive effect on the predictive accuracy of the models presented.
Anomaly data detection algorithm of small photovoltaic power station based on machine learning

With the intensification of the global energy crisis and the increasing shortage of fossil fuels, more and more countries have begun to strengthen the development and utilization of new energy. In recent years, with the help of policies and capital, the photovoltaic industry has achieved rapid development and occupies a leading position in new energy. The output power of a photovoltaic power station is affected by various factors such as solar radiation intensity, temperature, and installation method. The aging of photovoltaic modules, surface dust and component damage will also affect the power generation efficiency of photovoltaic power plants. The identification of abnormal data can not only help power station owners and operation and maintenance manufacturers to find potential equipment failures and other problems at the first time, but also can effectively avoid safety risks and economic losses. This paper proposes an abnormal data detection algorithm for small photovoltaic power plants based on machine learning. First, the operating data of photovoltaic power plants are normalized, and then the abnormal scores of the data samples are calculated by the iForest method, and then the classification center is calculated by the K-means method. This method can realize abnormal data detection in small photovoltaic power plants without irradiators, effectively avoid potential failures and risks of photovoltaic power plants, and ensure the operation safety of power plants and power grids to a certain extent.
An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage

In the last few years, the spreading of new technologies, such as augmented reality (AR), has been changing our way of life. Notably, AR technologies have different applications in the cultural heritage realm, improving available information for a user while visiting museums, art exhibits, or generally a city. Moreover, the spread of new and more powerful mobile devices jointly with virtual reality (VR) visors contributes to the spread of AR in cultural heritage. This work presents an augmented reality mobile system based on content-based image analysis techniques and linked open data to improve user knowledge about cultural heritage. In particular, we explore the uses of traditional feature extraction methods and a new way to extract them employing deep learning techniques. Furthermore, we conduct a rigorous experimental analysis to recognize the best method to extract accurate multimedia features for cultural heritage analysis. Eventually, experiments show that our approach achieves good results with respect to different standard measures.
World Professional Skills Competitions as a Way of Professional Education Internationalization

This article deals with the development of secondary vocational education and training (SVET) in the context of globalization processes. International cooperation is one of the priorities in the development of the SVET system. Constant updating of the educational content and technologies is necessitated by the professional qualiﬁcation requirements and international standards. A promising area of professional education internationalization could be arrangement of international competitions and professional skills championships in the WorldSkills format. Far East State Transport University (FESTU) took part in organizing the WorldSkills BRICS Championship in the “Rail transport technologies” competence in 2022 and it is described in this paper. The professional skills competitions enhance the development of professional and expert communities, professional development of scientists, engineers, and workers in Russia and globally. Such competitions can become one of the main forms of vocational education internationalization, which will enhance and improve education quality overall.
ThEconSum: an Economics-domained Dataset for Thai Text Summarization and Baseline Models

Language resources as datasets are an essential component in developing an effective automatic text summarization (ATS) system. Some public datasets are relatively uncommon when compared with popular languages, due to the complexity of language preprocessing resulting in a labor-intensive annotation by linguists. ATS techniques are to condense the size of text into a shorter output and reduce the time for finding the information from the huge textual data. This paper presents the Thai ATS construction with Economics-domain data, called ThEconSum, which manifests some linguistic challenges for Thai summarization. Existing public public datasets were employed for developing the ATS system in Thai economic news articles.
A Construction of Thai WordNet through Translation Equivalence

WordNet is a crucial language resource associated with artificial intelligence activities, for instance, constructing building models for advancement of computational linguistics and natural language processing, or representing statistical insights through knowledge graphs that emulate cognition and human understanding. Thai WordNet has been developed in many approaches, e.g., a merge approach in gold standard, and semi-auto construction with a bilingual dictionary. However, existing Thai WordNet is not easy to find words fit with the definition of synsets; and cover cultural gaps between the different languages of which needed to be aware. This paper presents a methodology of Translation Equivalence in order to construct Thai language resource, called LST22 Thai WordNet.
ANALISIS SENTIMEN MASYARAKAT PADA KEBIJAKAN VAKSINASI COVID-19 DI TWITTER MENGGUNAKAN METODE MESIN VEKTOR PENDUKUNG DENGAN KERNEL RADIAL BASIS FUNCTION BERBASIS FITUR LEKSIKON

Twitter is one of the popular social media used to get news quickly and briefly. After the outbreak of the COVID-19 virus and the government's policy to vaccinate against COVID-19 in Indonesia, more and more public opinion has been expressed through tweets. This makes the topic of COVID-19 vaccination interesting for sentiment analysis. Through sentiment analysis, information in the form of text data can be extracted to classify information related to positive or negative opinions. In this study, the classification of public opinion on COVID-19 vaccination was carried out using the supporting vector machine method without and with lexicon-based features. The manual labeling data used were 2981 tweets. The results of the classification of public opinion on COVID-19 vaccination in Indonesia with a supporting vector machine without the lexicon feature obtained accuracy, g-mean and AUC of 83%, 50% and 61.35%, respectively. Meanwhile, with lexicon-based features, the performance of the supporting vector machine method for classifying public opinion on COVID-19 vaccination in Indonesia obtained accuracy, g-mean and AUC of 90%, 86.63% and 87%, respectively. Based on these results, the performance of the supporting vector machine method with lexicon-based features provides better results for the performance of classifying of public opinion on COVID-19 vaccination compared to supporting vector machines without lexicon-based features.