Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF THEMATIC CHILDREN'S SONG AS A FUN LEARNING MEDIA FOR 2nd GRADE ELEMENTARY SCHOOL STUDENTS

Creating a pleasant classroom atmosphere is an important element in starting the learning process. In various studies, listening to songs or singing them has been shown to have a positive effect on a student's mood for participating in the learning process. However, children's songs that are relevant to be used as teaching media are very limited. This study aims to develop new songs that are suitable for thematic learning of the 2nd-grade elementary school (SD) students which also can be used as an online learning medium. The songs developed include 6 themes in the 2nd-grade curriculum. Songs were developed using the ADDIE method. The result of this study is an album containing 6 children's songs according to the 2013 Curriculum theme. The results of expert validation show that the songs have good quality in terms of song titles, song atmosphere, language style, song content, and song characteristics. Based on the test result with students, these songs can be sung easily and give a positive feeling effect.
Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion

Incorporating stronger syntactic biases into neural language models (LMs) is a long-standing goal, but research in this area often focuses on modeling English text, where constituent treebanks are readily available.Extending constituent tree-based LMs to the multilingual setting, where dependency treebanks are more common, is possible via dependency-toconstituency conversion methods.However, this raises the question of which tree formats are best for learning the model, and for which languages.We investigate this question by training recurrent neural network grammars (RNNGs) using various conversion methods, and evaluating them empirically in a multilingual setting.We examine the effect on LM performance across nine conversion methods and five languages through seven types of syntactic tests.On average, the performance of our best model represents a 19 % increase in accuracy over the worst choice across all languages.Our best model shows the advantage over sequential/overparameterized LMs, suggesting the positive effect of syntax injection in a multilingual setting.Our experiments highlight the importance of choosing the right tree formalism, and provide insights into making an informed decision.
Face Recognition Based on SRC Combined with Sparse Embedding Dimensionality Reduction

&lt;p&gt;Sparse representation-based classification (SRC) method has achieved good recognition results and shown strong robustness for face recognition, especially when the face image is affected by illumination variations, expression changes and occlusion. SRC method simply uses the training set as a dictionary to encode test samples. However, the high-dimensional training face data usually contain a large amount of redundant information, which will increase the complexity of this method. Therefore, the image dimensionality reduction procedure is separately performed by most of the existing methods before SRC is launched, but this may not be able to make full use of the discriminative information of the training samples. In this paper, based on the efficient SRC method, a sparse embedding dimensionality reduction strategy is combined with to achieve a face recognition method. For the proposed method, a projection matrix is used to project high-dimensional data into a low-dimensional space. At the same time, a discriminative coefficient constraint term in the objective function is introduced to reduce the classification residual of the sample through the distance relationship between all coefficients. Then the label information of the sample is used to iteratively update the projection matrix and coefficient representation. Finally, the test samples are projected into the low-dimensional space for classification. A large number of experimental results on three widely used face datasets show that the proposed method improves the discrimination of face images in low-dimensional space and can achieve better face recognition results.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
SISTEM INFORMASI PEMBAYARAN SPP PADA SMA KATOLIK 1 KABANJAHE

SMA Katolik 1 Kabanjahe School is an educational institution under the leadership of YPK Don Bosco KAM that always improves the quality of education services. Like education to find out the SPP payment information service. Efficient, fast, accurate, and secure tuition payment is needed for the administration of the school. For this, we need a system that requires SPP payment processes. At present, the administrative registration process in SMA Katolik 1 Kabanjahe School still uses a manual system and uses Microsoft Exel to process student transaction data. The problem that arises is the number of times the recording process causes a process that requires a long time making financial statements received less effective. The purpose of this research is to build an information system that will help the SPP payment process so that it reduces problems in making reports on cash receipts for the receipt of SPP payments. SPP Payment Information System Application in SMA Katolik 1 Kabanjahe School was designed with several stages consisting of data and information, needs analysis, and system design. The research method uses the waterfall method while the data collection method uses interviews, observation, and literature study. The design of this information system is created using the Visual Basaic.Net application and uses MySQL as its database.
Special Issue on “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”

Swarm intelligence technology is a high and new technology combining unmanned system technology, network information technology and artificial intelligence technology, which has become a research hotspot [...]
SCRUTINIZING COMPLIMENT RESPONSE STRATEGY IN TEACHING ENGLISH AS FOREIGN LANGUAGE IN INDONESIA

&lt;span lang="EN-US"&gt;Teaching compliment expression might be challenging for Indonesian EFL teacher, especially presenting compliment responses in some certain context of situation. The present study aims at investigating the compliment response strategies in some learning resources used by English teachers in Indonesia. The subjects of this study are the two EFL textbooks written by non-native English speakers and the supplementary materials derived from two Youtube channels and two materials from online learning websites created by native English speakers&lt;/span&gt;&lt;span lang="IN"&gt;.&lt;/span&gt;&lt;span lang="EN-US"&gt;The findings revealed accepting was the common compliment response strategy represented in all learning resources. it is also noted that the supplementary materials have various compliment response strategies completed with the meta-pragmatic information on each dialogue. The study implied that the EFL students should be equipped with pragmatic and sociolinguistic information in order to achieve the successful communication. &lt;/span&gt;
Word Transcription of MODI Script to Devanagari Using Deep Neural Network

-Many historical documents and letters are written in MODI script. Study of “Shivakalin” and “Peshvekalin” era documents is almost impossible without the knowledge of MODI script. This work aims to bridge the gap between Devanagari and MODI Script by developing a system to map the recognized MODI characters to its Devanagari equivalent. Our dataset would comprise 57 different classes of MODI Script characters. We has solved the problem of loss of information in CNN as the model goes deeper using Dense Net-121. The various approaches for feature extraction usually used include moment invariant, affine moment invariant, chain code histogram, intersection junction and for character classification. Deep Neural Networks on the other hand do not require any feature to be explicitly defined, instead they work on the raw pixel data to generate the best features and use them to classify the inputs into different classes. Hence, we propose a deep learning architecture for character recognition. Dense Net-121 uses little pre-processing compared to other image classification algorithms. This means that the network learns the filters which in traditional algorithms were hand-engineered. The system aims to provide a good recognition rate by implementing DCNN. Key Words:Siamese Neural Network, CNN, Devanagari, HOCR 1.INTRODUCTION ( Size 11, Times New roman) Modi is an ancient script. Crores of Modi documents. Origin: 12th century and used uptil the 20th century. Modi lipi converter is a low resource solution Many historical documents and letters are written in MODI script. Study of “Shivakalin” and “Peshvekalin” era documents is almost impossible without the knowledge of MODI script. This work aims to bridge the gap between Devanagari and MODI Script by developing a system to map the recognized MODI characters to its Devanagari equivalent, provides authors with most of the formatting specifications needed for preparing electronic versions of their papers. All standard paper components have been specified for three reasons: (1) ease of use when formatting individual papers, (2) automatic compliance to electronic requirements that facilitate the concurrent or later production of electronic products, and (3) conformity of style throughout a conference proceedings. Margins, column widths, line spacing, and type styles are built-in; examples of the type styles are provided throughout this document and are identified in italic type, within parentheses, following the example. Some components, suchas multi-leveled equations, graphics, and tables are notprescribed, although the various table text styles are provided. The formatter will need to create these components, incorporating the applicable criteria that follow.
Feasibility Test of Learning Media with Blended Learning Model and Augmented Reality-Assisted MOOCs

This study aimed at testing the feasibility of Learning Media with Blended Learning Model and Augmented Reality-assisted MOOCs. The research was conducted at Bengkulu University in 2020. The research instrument consisted of a questionnaire to test the validity of the material, a questionnaire to test the validity of the blended learning model, and a questionnaire to test the validity of augmented reality-assisted MOOCs used in learning media. Each of the three questionnaires was filled out by 3 validators. The results of this study indicated that the 3 instruments used were valid. This was evidenced by the results of the validity calculation which states that all questions are valid and no questions are issued. The results of this study showed that the learning media developed was feasible to use both in terms of material, blended learning model design, and in terms of the augmented reality-assisted MOOCs used.
Studies on Cross-modal Feature-based Mapping from Voice-source to Texture through Image Association by Listening Speech

The direct correlations between modality driven parameters of voice-source and texture were investigated.A perceptual experiment was conducted using vowel sounds with three representative phonation differences (modal, creaky and breathy) and texture images annotated with semantic terms.For quantitative analyses, acoustic features measuring vocal fold vibration, periodicity, spectral noise level, fundamental frequency and energy were calculated.Computational textural features containing coarseness, contrast, directionality, busyness, complexity, strength and brightness were extracted.The results showed that the most important feature is the amplitude difference between the first two harmonics (H1-H2).H1-H2 significantly correlates to coarseness, contrast, busyness, complexity, strength and brightness.Harmonic-to-Noise Ratios (HNRs) highly correlate to coarseness, busyness, complexity and strength.Significant correlations were also observed between Cepstral Peak Prominence (CPP) & coarseness, fundamental frequency (F0) & complexity, brightness and energy & strength.These parametric correlations can serve as basic scientific knowledge for cross-modal mapping.
PENERAPAN METODE MODELLING DALAM PENGARSIPAN DOKUMEN DI KPU TOBA

The document archiving system at the Toba Regency KPU is still done manually and takes a very long time. The purpose of "Document Archiving Design at the Toba KPU Using the Mixture Modeling Method" is to facilitate the Toba KPU agency in archiving incoming and outgoing letters. The Mixture Modeling method used serves to classify the types of letters based on the categories of letters that exist in the Toba KPU. The implementation of making this filing system uses PHP and MySQl programming.
Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction

With the rise in popularity of short-term Human Trajectory Prediction (HTP), Long-Term Crowd Flow Prediction (LTCFP) has been proposed to forecast crowd movement in large and complex environments. However, the input representations, models, and datasets for LTCFP are currently limited. To this end, we propose Fourier Isovists, a novel input representation based on egocentric visibility, which consistently improves all existing models. We also propose GeoInteractNet (GINet), which couples the layers between a multi-scale attention network (M-SCAN) and a convolutional encoder-decoder network (CED). M-SCAN approximates a super-resolution map of where humans are likely to interact on the way to their goals and produces multi-scale attention maps. The CED then uses these maps in either its encoder's inputs or its decoder's attention gates, which allows GINet to produce super-resolution predictions with substantially higher accuracy than existing models even with Fourier Isovists. In order to evaluate the scalability of models to large and complex environments, which the only existing LTCFP dataset is unsuitable for, a new synthetic crowd dataset with both real and synthetic environments has been generated. In its nascent state, LTCFP has much to gain from our key contributions. The Supplementary Materials, dataset, and code are available at sssohn.github.io/GeoInteractNet.
Named Entity Recognition Model Based on TextCNN-BiLSTM-CRF with Chinese Text Classification

&lt;p&gt;The traditional Chinese Named Entity Recognition (NER) method is difficult to define the entity category of the word according to the specific language environment, and the category is ambiguous, so it is difficult to accurately identify the entity. Named entity recognition based on deep learning can find entity categories in text, so it has received widespread attention. On the basis of the neural network model, this paper proposes a model based on TextCNN-BiLSTM-CRF and text classification (TextCNN-BiLSTM-TC-CRF) for Chinese NER. First, the TextCNN model is used to extract the word vector information of the text data; secondly, bidirectional LSTM is used the model extracts the contextual features of the text; then the neural network model is used to automatically extract the word features and the global features of the text for text classification; finally, the text sequence labeling and entity recognition are performed. Experiments verify that on a large-scale Chinese NER data set, the entity recognition model proposed in this paper has better evaluation indicators than other algorithms, with an F1 score of 98.7%.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Analisis Ketepatan Istilah Terminologi Medis Penyakit Sistem Respirasi Sesuai Klasifikasi Penyakit ICD-10 di Puskesmas Kecamatan Grogol Petamburan

Medical terminology is medical language which is a liaison between health workers' facilities. Medical terminology written on the diagnosis should be written by the right health care worker so that it has informative value in order to assist the coding officer in selecting the lead term for determining the diagnosis code. The purpose of this study was to analyze the accuracy of medical terminology in respiratory system diseases according to the ICD-10 disease classification at the Grogol Petamburan District Health Center. Using a quantitative descriptive research method on 90 samples of medical record cases of the respiratory system in August 2020. The sampling technique used was random sampling. The size was determined randomly using the slovin formula. The results showed that the accuracy of using medical terminology in medical records was 74 (82%) and inaccuracy in the use of medical terminology 16 (18%) using abbreviations, Indonesian language, and letter/spelling errors. Factors causing the inaccuracy. The Puskesmas already has a SOP for recording medical records, but the service points have not been explained in detail according to the SOAP, especially the assessment which requires doctors to write diagnoses using medical terminology according to the ICD-10 disease classification. Then the large number of patients can be a factor causing the doctor's inaccuracy in writing a diagnosis. It is recommended to the head of the medical record to evaluate the accuracy of outpatient medical terminology according to the ICD-10 disease classification, so that there are no inaccuracies in medical terminology using Indonesian language, abbreviations, and spelling errors.
KEKELIRUAN-KEKELIRUAN DALAM MEMAHAMI KONSEP-KONSEP TEORETIS HALEY TENTANG METAFORA: SEBUAH KAJIAN METALINGUAL

&lt;span&gt;This paper is the result of meta-lingual research on the application of Haley's metaphor theory. The research objective is to analyze and evaluate whether Haley's metaphorical theory concept is appropriately used and applied in the research data. The research design is qualitative with analytical-descriptive, conceptual-empirical, and literature methods. The data source is in the form of six scientific articles that apply Haley's metaphorical theory and have been published in six different scientific journals in Indonesia. The research data consists of two types of data, namely (i) main data in the form of meta-lingual data and (ii) auxiliary data in the form of object language data. The results of data analysis and discussion indicate that there are conceptual errors in the application of Haley's metaphorical theory in the six data sources. Generally, these mistakes can be grouped into three categories, namely errors in understanding (i) Haley's definition of metaphor, (ii) hierarchical terms of perceptual space, and (iii) application of the hierarchical concept of Haley's perceptual space in the analysis of metaphorical expressions. The main cause of these errors is that (i) Haley's theory is not referenced directly from Haley and (ii) only one of Haley's writings is referred to. Thus, the research results in that data source eventually become questions.&lt;/span&gt;
Emergence of New Public Discovery Services: Connecting Open Web Searchers to Library Content

A growing number of new public citation databases, available free of charge and accessible on the open web, are offering researchers a new place to start their searching, providing an alternative to Google Scholar and library resources. These new “public discovery services” index significant portions of scholarly literature, then differentiate themselves by applying technologies like artificial intelligence and machine learning, to create results sets that are promoted as more meaningful, easier to navigate, and more engaging than other discovery options. Additionally, these new public discovery services are adopting new linking technologies that connect researchers from citation records to full text content licensed on their behalf by their affiliated libraries. With these new sites logging millions of sessions a month, they present unique opportunities for libraries to connect to researchers working outside the library and challenges in how the library can make itself obvious in the user workflow.
PEMBUATAN SISTEM ALARM KEBAKARAN UNTUK MEMADAMKAN API DAN PENDETEKSI GAS BERBASIS ARDUINO UNO

The house number becomes the index or identity of house, because every house in a neighborhood has a different number. To find a house number for humans is very easy, but it becomes difficult when the computer is doing it all. Artificial Neural Network (ANN) is a branching science of artificial intelligence. Artificial intelligence (AI) and Convolutional Neural Network (CNN) is one of the branches of ANN. The data was processed in the form of images sourced from the udfdl.stanford.edu page with a total of 73.257 training data and 26.032 testing data. The data is preprocessed and processed using CNN so that the house number in each image will be automatically tested try the result will appear.
Forensic Auditing and Fraud Management: A Study of Pabod Breweries Limited, Port Harcourt, Nigeria

This paper scrutinized the nexus between forensic auditing and fraud management using Pabod Breweries, Port Harcourt, Nigeria as a case study. The primary data employed in the study were sourced from staff of the Pabod Breweries using questionnaires. Percentage and Pearson correlation were the techniques of analysis used in the study. The results of the study showed a significant positive relationship between forensic auditing and detection of fraud in the private sector. Also, the results showed that there is the existence of a significant positive relationship between forensic auditing and fraud prevention in the private sector. Thus, the study concluded that forensic auditing does significantly detect and prevent the occurrence of fraud cases in the private sector. Therefore, the paper recommended that the management of companies should ensure that forensic audits are carried out in their establishments at least once in a year, even if it is not statutorily required, in order to detect fraudulent practices in their organisation. In addition, forensic audit report should be implemented accordingly by the audited organisation in order to prevent fraud and avert white-collar crime in the organisation.
Conflict and Conflict Resolution between Administrative and Teaching Staffs Selected Schools in Winneba: The Role of Effective Communication

Conflict is inevitable in our human society due to the continuous interaction between people. The same occurs between administrative and teaching staff in schools as a result of their close interaction. This study explored how effective communication could be employed to resolve conflicts between administrative and teaching staff in two selected private senior high schools in Winneba. The study employed mixed method approach with the exploratory design to analyze and interpreted the conflicts that arise between administrators and teachers. Quota, simple random and purposive sampling approaches were used to select the respondents. Questionnaires and semi- structured interview guide were used to collect data for the study. The findings of the study revealed that conflicts exist between the administrators and teachers in the schools and such arises due to problems of communication. Staff meetings are the major means of communication between these two groups but social media use is also becoming a popular means of communication. Effective communication in the form of dialogue and negotiation can best help to resolve the conflict between teachers and administrators in the schools. It was concluded that lack of effective communication, and limited avenues for teachers to express their grievances, as well as poor feedback result in conflicts in the schools. Regular interaction through intensifying staff meeting and one-to-one communication can help resolve school conflicts. It was therefore recommended that workshops on effective communication should be organized for teachers and administrative staff to improve on their communication skills.