Reviewerly/exHarmony huggingface dataset:
Professional Training of Primary School Teacher to Organize Dialogic Learning for Students: Theoretical Context

The urgency of the problem of future primary school teacher training for dialogic learning of students taking into account modern challenges of globalization and digitalization of education is substantiated. The contradictions taking place in the practice of professional training in pedagogical universities are emphasized. The theoretical aspect of the outlined problem is highlighted. The characteristics of the content of the following concepts are given: dialogic learning, educational dialogue, dialogic communication, pedagogical communication, the teacher’s communication culture, communication etiquette, etc. The components of teachers’ pedagogical skills as personal and professional factors of successful organization of dialogic learning in school are presented. It is proved that the organization of dialogic learning at school requires the formation of a teacher's system of skills, namely: to organize and manage the dialogic process in learning; create a positive communicative atmosphere for the exchange of thoughts and ideas; pedagogically expedient to stimulate communication between students; apply advanced dialogic learning technologies; motivate students to create or solve educational tasks using productive dialogue; effectively use means of verbal and nonverbal communication; make emotional contact and listen to the interlocutor, etc. The strategy of communicative approach in the educational process of the pedagogical university is widespread, first of all as for humanities. The author notes that the readiness of future primary school teachers to organize a productive educational dialogue is determined not only by the level of development of communicative abilities and skills, but also other personal traits: intellectual qualities, motivation for teaching, love for children, emotional balance, empathy, tolerance, professional responsibility, high level of moral culture, readiness for innovative activity, desire for self-improvement and self-education, etc.&#x0D; The author's material from the dissertation “Training of primary school teachers to organize dialogic learning” for the degree of Doctor of Philosophy (2020) is partially used in the article.
Prototype Memory for Large-Scale Face Representation Learning

Face representation learning using datasets with a massive number of identities requires appropriate training methods. Softmax-based approach, currently the state-of-the-art in face recognition, in its usual “full softmax” form is not suitable for datasets with millions of persons. Several methods, based on the “sampled softmax” approach, were proposed to remove this limitation. These methods, however, have a set of disadvantages. One of them is a problem of “prototype obsolescence”: classifier weights (prototypes) of the rarely sampled classes receive too scarce gradients and become outdated and detached from the current encoder state, resulting in incorrect training signals. This problem is especially serious in ultra-large-scale datasets. In this paper, we propose a novel face representation learning model called Prototype Memory, which alleviates this problem and allows training on a dataset of any size. Prototype Memory consists of the limited-size memory module for storing recent class prototypes and employs a set of algorithms to update it in appropriate way. New class prototypes are generated on the fly using exemplar embeddings in the current mini-batch. These prototypes are enqueued to the memory and used in a role of classifier weights for softmax classification-based training. To prevent obsolescence and keep the memory in close connection with the encoder, prototypes are regularly refreshed, and oldest ones are dequeued and disposed of. Prototype Memory is computationally efficient and independent of dataset size. It can be used with various loss functions, hard example mining algorithms and encoder architectures. We prove the effectiveness of the proposed model by extensive experiments on popular face recognition benchmarks.
An examination of handwritten signatures forged using photosensitive signature stamp

Signature examination is the most common examination performed by any document examiner. Determination of the authenticity of a handwritten signature on a questioned document is an important task for forensic document examiners in the forensic science field. As a result of continuous developments in technology, a signature stamp can now be created using a photosensitive seal to enable the reproduction of a handwritten signature. These stamps are commonly used in China and several other countries. In this study, 10 types of black photosensitive stamp-pad ink, 10 brands of fountain pen ink, 15 types of black gel ink and six types of black erasable gel ink found on the Chinese domestic market were collected and 10 photosensitive signature stamps were created using the signatures of 10 people. Microscopic analysis, infrared (IR) and fluorescence analyses and microspectrophotometry (MSP) techniques were used to examine the resulting photosensitive signature stamp impressions when applied to printing papers, writing papers and invoice papers. By comparing the printing and spectral characteristics of the photosensitive signature stamp impressions with those of the signatures executed using the fountain pens, gel pens and erasable gel pens, it was possible to determine whether each signature was written or stamped using a photosensitive signature stamp. To validate these results, a 96.7% absolute accuracy and a 99.3% detection rate were achieved over a total of 150 blind tests conducted by five forensic document examiners, thus demonstrating that a combination of the four analysis methods used in this work can provide a more scientific approach and improve the accuracy and the detection rate of the examination process.KEY POINTSA signature stamp is a photosensitive seal made in the style of a handwritten signature.Although microscopic analysis can usually provide better examination results, a comprehensive examination method that includes microscopic analysis and ink composition analysis is required to improve the accuracy and the detection rate of the examination process.This study collected and tested photosensitive stamp-pad inks, fountain pen inks, gel inks and erasable inks.Infrared and fluorescence analyses and microspectrophotometry were able to distinguish the photosensitive ink from both erasable ink and fountain pen ink. Supplemental data for this article are available online at https://doi.org/10.1080/20961790.2021.1898755.
Investigating Students’ Self-Efficacy on the Use of Oral Peer Feedback in Speaking Subject

The study attempted to investigate students' self-efficacy on the use of oral peer feedback in speaking subject. Afterward, The researcher used a questionnaire to find out the level of student self-efficacy in giving oral peer feedback and the interview to find out how students with high and low self-efficacy give their oral peer feedback. This study employed qualitative strategies by using case study. The participants were 25 students who had used oral peer feedback in their speaking subject. The findings revealed that the students’ self-efficacy played such a big role in giving oral peer feedback in speaking subject. Their beliefs influence the process of achieving their goals and their performance as well. Furthermore, students with high self-efficacy tend to believe that they can handle any situation that they face effectively, use certain strategies, and learn from their experiences. Although, the students with low self-efficacy did not have certain strategies to face their performance which lead them into failure. Thus, the failure caused them to feel anxiety, sad, apathetic and worried all the time. Based on the findings self-efficacy has a great effect on the students’ achievement and performance in giving oral peer feedback in speaking subject and even the other courses.
Clustering Kualitas Kinerja Karyawan Pada Perusahaan Bahan Kimia Menggunakan Algoritma K-Means

Assessment of the quality of employee performance is one of the important things and is very much needed by the company, however, PT Clariant Adsorbents Indonesia does not currently have an employee performance quality system. This study aims to see the productivity of an employee and the effectiveness of an employee's performance in the future. Employee performance appraisal is divided into several clusters that are highly productive, moderately productive and less productive. The method used in this study is the K-means method, where the k-means method is the most popular method in the clustering algorithm. The k-means method looks for some of the most optimal partitions of the processed data by minimizing the error of the criteria using the optimal iteration. The variables used consist of employee names, work quality scores, responsibility values, cooperation values, attendance values, and discipline values. This research in processing data using Rapidminer Version 7.6.0.0.1 using the K-means method. The final result of this research is to get the grouping of the assessment into several categories that are very productive, quite productive and less productive and the clustering results are 0.42% for cluster 1, very productive category, which consists of 16 employee data, 0.47% for cluster 2 quite productive category, which consists of 18 employee data, 0.11% for cluster 3, less productive category, which consists of 4 employee data.
Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu

A University can have many student data in their database because many students did not graduate on time. Data mining technique can be used to process student data to predict student graduation on time. Support Vector Machine (SVM) algorithm is one of data mining techniques. Objectives of this research was implementation of SVM algorithm to model the prediction of student graduation on time in private university in Indonesia. This research was conducted using CRISP-DM (Cross Industry Standard Process for Data Mining) method. There are five steps in that method such as understanding business to predict student graduation in time which is not available, data understanding by choosing the right attribute for the next step, data preparation includes cleaning the null data and transforming data into category which has been specified, modeling was used by implementing data training and data testing on SVM algorithm and evaluation to validate and measure the accuracy of the model. The result of this research shown that accuracy value of data testing was 94,4% using 90% data training and 10% data testing. This concluded SVM algorithm can be used to model the prediction of student graduation on time. &#x0D;
From Forecast to Decisions in Graphical Models: A Natural Gradient Optimization Approach

Graphical models and in particular Hidden Markov Models or their continuous space equivalent, the so called Kalman filter model, are a powerful tool to make some inference that can be used in decision making contexts. The estimation of their parameters is usually based on the Expectation Maximization approach as this is a natural statistical way to train them. When used for decision making, it may be more relevant to find parameters that are relevant to our decisions rather than just try to fit the model from a statistical point of view. Hence, we can reformulate the determination of graphical model as an inference problem where the true concern is the quality of the decisions from the forecast given by the model. We show that the resulting optimization problem can be reformulated as an information geometric optimization problem and introduce a natural gradient descent strategy that incorporates additional meta parameters. We show that our approach is a strong alternative to the celebrated EM approach for learning in graphical models. Actually, our natural gradient based strategy leads to learning optimal parameters for the final objective function (which is our decision) without artificially trying to fit a distribution that may not correspond to the real one. We support our theoretical findings with the question of decision in financial markets and show that the learned model performs better than traditional practitioner methods and is less prone to overfitting.
Dynamic Gesture Recognition Based on RF Sensor and AE-LSTM Neural Network

In this paper, we present a novel deep neural network (DNN) dedicated to dynamic gesture control based on a radio-frequency (RF) type sensors. The proposed network is based on auto-encoder (AE) and long short-term memory recurrent neural network (LSTM-RNN). The encoder for reduction and effective feature extraction is obtained by unsupervised training an AE with a large untagged RF database. Further a light LSTM network is trained use a small size of tagged datasets for dynamic gesture classification. Training the network with semi-supervised learning methods greatly reduces the requirement on the size of the tagged gestures datasets. Experimental results demonstrate that our AE-LSTM method outperforms existing networks in terms of accuracy and realizability. The proposed application is used for the human-computer interaction (HCI) of intelligent home, automatic driving and robot scenes.
Development of prototype electronic dashboard service performance of LPKA class I Palembang

LPKA Palembang provides services to the community in the form of coaching in the fields of education, skills training, community guidance, and alleviation, services for Andikpas visits and complaints, remission submission services, assimilation, and other submissions especially to support alleviation programs such as the Conditional Leave (CB) program, Leaving Towards Free (CMB), and Parole (PB). To provide the best convenience and quality in providing services, a system is needed that can not only be used to submit and process service data online, but also can integrate service data so that it can be more easily accessed anytime and anywhere. The system created is an electronic dashboard or e-dashboard that can present data visually so that it is easily understood by readers. This study aims to build a prototype that can provide an overview of the e-dashboard system to be built. From the prototype evaluation, user feedback can be obtained which can be used to build an actual e-dashboard system so that it can reduce deviations from the system being built. The stages in this research are planning, designing, building, and evaluating the prototype. The results of this study are the development of e-Dashboard prototype that had been tested by 80% of LPKA Class I Palembang employees. The developed prototype has features that almost meet user needs because the system development is based on a quick analysis of the needs of future e-Dashboard users.
Developing a Knowledge Base on Climate Change for Metropolitan Cities

French Metropolitan Cities have developed their PCETs program (Territorial Climate and Energy Plan) to meet the objectives set by the European Union for 2020 and the National Climate Plan, in France. This research aim is defining and initiating a knowledge base on climate change and more specifically on WEEE (waste electrical and electronic equipment) for large Metropolitan cities such as Metropole of Lyon and Metropole of Montpellier. This Knowledge base will contain best (and bad) practices, experiences, suited metrics, collaborative measures, projects, etc., using the Knowledge Management approach exploring artificial intelligence techniques. For our methodology we work on the state of the art for building such a knowledge base, taking into account the previous works of Boulanger [1, 2], Mercier-Laurent [3–6] and Kayakutlu and Mercier-Laurent [7]. We begin to list existing materials in order to implement a knowledge base on the recycling of products starting by electronic devices.
Phrase Based Statistical Machine Translation Javanese-Indonesian

This research aims to produce a statistical machine translation that can be implemented to perform Javanese-Indonesian translation and to know the influence of the main data sources of statistical machine translation namely parallel corpus and monolingual corpus on the quality of Javanese-Indonesian statistical machine translation. The testing was carried out by gradually adding the quantity of parallel corpus and monolingual corpus to seven configurations of Javanese-Indonesian statistical machine translation. All machine translation configuration experiments were tested with test data totaling 500 lines of Javanese sentences. Results from machine translation are evaluated automatically using Bilingual Evaluation Understudy (BLEU). Test results in seven configurations showed an increase in the evaluation value of the translation machine after the quantity of parallel corpus and monolingual corpus was added. The quantity of parallel corpus in configurations 1 and 2 increased by 3,6%, configurations 2 and 3 increased by 8,23%, configurations 3 and 7 increased by 14,92%. Additional monolingual corpus quantity in configurations 4 and 5 increased BLEU score by 0,18%, configurations 5 and 6 increased by 0,06%, configurations 6 and 7 increased by 0,24%. The test results showed that the quantity of parallel corpus and monolingual corpus could increase the evaluation value of statistical machine translation Javanese-Indonesian, but the quantity of parallel corpus had a greater influence than the quantity of monolingual corpus
A Sentiment Analysis of Egypt’s New Real Estate Registration Law on Facebook

In response to the increasing influence of social media networks on shaping the public opinion, sentiment analysis systems and applications have been developed to extract insights and gain an overview of the wider public opinion behind certain topics so as to support businesses, manufacturers, government agencies, and policymakers with their decisions and plans.Despite the importance of sentiment analysis in providing policymakers with effective mechanisms to understand the attitudes of customers and citizens which can be usefully used in decision-making processes and planning for the future, so far studies on sentiment analysis are very limited in Egypt.Much of the work is still done using survey tools such as questionnaires and polls to gather information about the citizens' attitudes towards given issues and topics.Despite the effectiveness of such methods, citizens' reflections on social media platforms and networks remain more powerful in providing comprehensive insights and overviews.Furthermore, social media-based sentiment analysis is usually more representative being based on larger numbers of participants, which has positive implications to reliability.Opinions expressed on social media are often the most powerful forms of feedback for businesses because they are given unsolicited.In light of this argument, this study seeks to provide a sentiment analysis of Egypt's New Real Estate Registration Law on Facebook.To extract information about the users' sentiment polarity (positive, neutral or negative), Facebook posts were used.The rationale is that Facebook is still the most popular social media platform in Egypt.Text classification was then used for classifying the selected data into three main classes/values: Positive, Negative, and Neutral.The findings indicate that sentiments expressed in the users' posts and comments have a significant negative attitude towards the new law.Despite the effectiveness of the automatic evaluation and analysis of the sentiments and opinions of the users in social media concerning the new Real Estate Registration Law, linguistic approaches including Critical Discourse Analysis (CDA), functional linguistics, and semiotics need to be incorporated into sentiment analysis applications for gaining a better understanding of people's attitudes towards specific issues.
Desain dan Implementasi Sistem Navigasi pada Automated Guided Vehicle (AGV)

This research aims to build mechanical system of Automated Guided Vehicle (AGV) and navigation system for AGV. The navigation system consists of RFID reader and rotary encoder. The testing result show that mechanical system of AGV was successfully finished. There is a mechanical problem on drive system of AGV. Each part of navigation system works well according to design. Line sensor can be used for path detection with the given threshold values. Encoder can be used to measure the speed of AGV with the maximum error accuracy less than 2 rpm. This result shows that every part of AGV is ready to run localization and navigation algorithm, even though, it needs to do some improvement on driving parts.
CF Model: A Coarse-to-Fine Model Based on Two-Level Local Search for Image Copy-Move Forgery Detection

Copy-move forgery is the most predominant forgery technique in the field of digital image forgery. Block-based and interest-based are currently the two mainstream categories for copy-move forgery detection methods. However, block-based algorithm lacks the ability to resist affine transformation attacks, and interest point-based algorithm is limited to accurately locate the tampered region. To tackle these challenges, a coarse-to-fine model (CFM) is proposed. By extracting features, affine transformation matrix and detecting forgery regions, the localization of tampered areas from sparse to precise is realized. Specifically, in order to further exactly extract the forged regions and improve performance of the model, a two-level local search algorithm is designed in the refinement stage. In the first level, the image blocks are used as search units for feature matching, and the second level is to refine the edge of the region at pixel level. The method maintains a good balance between the complexity and effectiveness of forgery detection, and the experimental results show that it has a better detection effect than the traditional interest-based copy and move forgery detection method. In addition, CFM method has high robustness on postprocessing operations, such as scaling, rotation, noise, and JPEG compression.
Analysis of frequent itemset generation based on trie data structure in Apriori algorithm

Apriori is one technique of data mining association rules that aims to extract correlations between sets of items in the transaction database. The main problem with the Apriori algorithm is the process of scanning databases repeatedly to generate itemset candidates. This research examines the combination of pruning by using the trieapproach and multi-thread implementation in three algorithms to obtain frequent itemset. Trie is a data structure in the form of an ordered tree to store a set of strings where every node in the tree contains the same prefix. The use of a full combination trie (different from frequent pattern (FP) tree using links) allows the implementation of arrays and the hash calculation to achieve the addressing of itemset combination. In this research, the measure to get the address is called Hash-node calculation used to update support value. For these three alternatives, run time processing is analyzed based on the number of itemset combinations and transaction data at a certain minimum support value. The experimental results show that an algorithm thatexploits resource capabilities by applying multi-threadperforms almost seven times betterthanan algorithm implemented in single-thread in calculating hash-node. The fastest run time of the multi-thread approach is 43 minutes with 150-itemset combinations on 100,000 transaction data.