Reviewerly/exHarmony huggingface dataset:
A HOMEROOM TEACHER – A SUBJECT OF ORGANIZATION OF EDUCATION OF STUDENTS OF THE EDUCATIONAL ORGANIZATION

The article reveals the purpose of education in an educational organization; characterization of the basic document of education in an educational organization; the subject (class teacher) of the organization of educational activi- ties of students, his appointment; the essence and content of the activity of the class teacher; the characteristic of types of educational activity of the class teacher is given; the content of the initial stage of educational work with the class; the variants of the activity of the class teacher in uniting the class and educating its students are identified and characterized.
Pengembangan Idle Game “Havok Runner” Berbasis Android Menggunakan Metode Agile Game Development

Video games are one of the industries that are growing very rapidly every year. And in a video game industry, there are many types/genres of games that have been widely circulated in the community. One of them is a type of game where players interact a little while the game is running, namely idle games. Even though gaming is one of the industries that is growing very rapidly, based on the report of Diana Paskarina (COO of game loka nutmeg) the contribution of the creative game industry is still very small to state revenue. However, regardless of the existing constraints, there are opportunities/opportunities that can be utilized to improve the country's economy through idle games. This refers to the results of the Sensor Tower survey which shows that the income of the idle game genre in Q3 2016 to Q2 2018 has increased very rapidly. Therefore, researchers try to make a positive contribution by developing game applications with the idle game genre using the Agile Game Development method.
Model Berbasis Fuzzy Tsukamoto Untuk Perhitungan Besaran Gaji Dosen Pada Perguruan Tinggi Swasta

Payment of salaries is a routine activity carried out by the finance department of a company, agency or business entity. Likewise, private universities which are educational institutions have an obligation to pay the salaries of their lecturers. With the large number of lecturers and the many variables in determining the amount of lecturer salaries, it is difficult for the finance department at private universities to determine the amount of a fair salary and in other words provide a salary that is in accordance with the efforts that have been made by each lecturer. With these problems, we need a method of calculating teacher salaries. The fuzzy tsukamoto method is the most ideal method in determining the salary of lecturers at private universities because it has non-binary and non-linear problems. With the problems and solutions raised above, this study aims to provide a faster, more precise and accurate method of calculating lecturer salaries at private universities, of course with input variables that are adjusted to the rules in private universities. Salary variables, especially lecturer salary bonuses at private universities, are years of service, jafung, rank/class and the most important is the performance variable. In the performance variable, there are three specific variables, namely the tridharma of lecturers (teaching, research or publication, and community service). To achieve a bonus with many criteria, it is required that the three performance variables have many criteria, and vice versa.
Sentiment Analysis Method for Agricultural Product Review Based on Corpus Characteristics and Deep Learning Model

Although deep learning models are widely used in text sentiment analysis, it is a challenging task to extract richer semantic features to improve model performance in corpora with weak label characteristics. This study crawls the agricultural product review of Jingdong e-commerce as a corpus, and proposes a deep learning method based on the characteristics of the corpus for sentiment analysis. The method first uses frequent item mining to construct a sentiment dictionary, and converts weakly labeled data into high-quality corpus through sentiment value calculation. Secondly, Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) are combined in the sentiment analysis model, and the word vectors trained by Glove and Word2vec are imported into the multi-channel neural network, so that the model can learn local and global semantic features in parallel, and embed the attention mechanism in the channel. The experimental results show that the performance of the model considering the characteristics of the corpus is significantly improved, and the MAtt-CNN-BiLSTM model constructed in this paper has the best performance in the experiments under the three datasets.
Generalized Linear Integer Numeric Planning

Classical planning aims to find a sequence of actions that guarantees goal achievement from an initial state. The representative framework of classical planning is based on propositional logic. Due to the weak expressiveness of propositional logic, many applications of interest cannot be formalized as a classical planning problem. Some extensions such as numeric planning and generalized planning (GP) are therefore proposed. Qualitative numeric planning (QNP) is a decidable class of numeric and generalized extensions and serves as a numeric abstraction of GP. However, QNP is still far from being perfect and needs further improvement. In this paper, we introduce another generalized version of numeric planning, namely generalized linear integer numeric planning(GLINP), which is a more suitable abstract framework of GP than QNP. In addition, we develop a general framework to synthesize solutions to GLINP problems. Finally, we evaluate our approach on a number of benchmarks, and experimental results justify the effectiveness and scalability of our proposed approach.
Holistic Inferential Criteria of Adequate Formalization

Peregrin and Svoboda propose an inferential and holistic approach to formalization, and a similar approach (to correctness) is considered by Brun. However, while the inferential criteria of adequacy explicitly endorsed by these authors may be holistic "in spirit," they are formulated for single formulas. More importantly, they allow the trivialization of equivalence and face problems when materially correct arguments come into play. Against this background, this paper tries to motivate holistic inferential criteria that compel us to distinguish carefully between non-trivially equivalent formalizations as well as between materially and logically correct arguments on an inferential basis.
Penerapan Euclidean Distance untuk Deteksi Kemiripan Citra Berbasis Scale Invariant Feature Transform (SIFT)

The similarity of a pair of images can be measured using the distance measurement method from the feature extraction. The feature extraction method used in this research was the Scale Invariant Feature Transform (SIFT). This method is an extraction method that is invariant to changes in scale, rotation, translation and illumination. In this research, each keypoint of test image is matched for its level of similarity with the Euclidean distance method. The similarity of each keypoint of the tested image is matched by Euclidean distance and it will be claimed similar if it has the smallest distance. Next, the corresponding keypoint of the tested image gets recall test by varying parameters of transformation in size, rotation, color, and angle as well as different image. The research results demonstrated that the recall averages of dataset were 100 for similar image test, 95 for size and rotation changes tests, 98 for colour changes test, 97 for angle changes test, and 0 for image test with different objects. Based on the results, SIFT is suitable for detecting the similarity of image object shapes.
Preferences of Students for Online Education on Pramuka Island Thousand Island DKI Jakarta during the COVID-19 Pandemic

The implementation of Online Learning to date has made students accustomed to using technology in the learning process which will also change the habit of using student preferences in the learning process in choosing non-print information sources because of the ease of access. The thing that is very important at this time during the Covid-19 pandemic and the learning process at home is that the most accessible source of information is via the internet or non-print, so that it is very possible for students' preferences for information sources to change from print to non-print will increase, sources of information Online is the main source of information used amidst social and physical restrictions during the Covid-19 pandemic. This study focuses on the e-learning platform that is implemented in area of the Thousand Islands DKI Jakarta using a theoretical framework to evaluate e-learning performance. The method used is factor analysis used to find factors that built the success of implementation and regression analysis to build an e-learning evaluation model. The results obtained indicate that there are a number of main factors, namely intelligent content, content development, content governance, content innovation which can then be used to build mathematical models that describe the current system performance and predictions in the future, and can be used to build a system development plan in the future with the value is 7.540 (from a scale of 0.000 - 10.000)
Alternative Communication Glove for Typing Japanese Characters

Proper communication between a patient and their caregiver is paramount to insuring proper medical treatment. Speech disabilities and certain medical operations may hinder this communication and can cause issues for all parties involved. We have developed a wearable device, named the alternative communication glove (ACG), that allows a user to type any Japanese character using only two independent finger movements. We propose this unique methodology to compensate for a niche group of users that may be overlooked by current products and research. The main benefit of the ACG is that it can allow the user to express themselves while in any body position and without the need for eyesight.
The LBC-3 lightweight encryption algorithm

Abstract This article presents a developed lightweight data encryption algorithm called LBC-3. Its essential difference from the known algorithms is the R1 function and the procedure for generating round keys. The main characteristics of this lightweight algorithm and all the transformations used in the encryption and decryption processes are described. The process of generating the round keys of the algorithm is also considered. The results of the study of the cryptographic properties of the algorithm using the “avalanche effect” and statistical tests are presented. The avalanche property was tested for each round with each bit of the source text changing. Based on the work carried out, it was found that the proposed encryption algorithm is effective in providing a good avalanche effect, and the binary sequence obtained after encryption is close to random. The research revealed good cryptographic properties of this algorithm.
FastATDC: Fast Anomalous Trajectory Detection and Classification

Automated detection of anomalous trajectories is an important problem with considerable applications in intelligent transportation systems. Many existing studies have focused on distinguishing anomalous trajectories from normal trajectories, ignoring the large differences between anomalous trajectories. A recent study has made great progress in identifying abnormal trajectory patterns and proposed a two-stage algorithm for anomalous trajectory detection and classification (ATDC). This algorithm has excellent performance but suffers from a few limitations, such as high time complexity and poor interpretation. Here, we present a careful theoretical and empirical analysis of the ATDC algorithm, showing that the calculation of anomaly scores in both stages can be simplified, and that the second stage of the algorithm is much more important than the first stage. Hence, we develop a FastATDC algorithm that introduces a random sampling strategy in both stages. Experimental results show that FastATDC is 10 to 20 times faster than ATDC on real datasets. Moreover, FastATDC outperforms the baseline algorithms and is comparable to the ATDC algorithm.
Mixed membership Gaussians

In recent years, there has been significant progress in unsupervised representation learning. Classical statistical and machine learning techniques can be used on these representations for downstream tasks and applications. Here, we consider an unsupervised downstream task, namely, topic modeling with mixed membership models. Prototypical mixed membership models like the latent Dirichlet allocation (LDA) topic model use only simple discrete observed features. However, state-of-the-art representations of images, text, and other modalities are given by continuous feature vectors. Therefore, we study mixed membership Gaussians that operate on continuous feature vectors. We prove that the parameters of this model can be learned efficiently and effectively by Pearson's method of moments and corroborate our theoretical findings on synthetic data. Experiments on standard labeled image data sets show that mixed membership Gaussians on state-of-the-art image representations yield topics that are semantically meaningful, coherent, and cover the labels that have not been used during training well.
Quality checks procedure for erroneous measurements detection of spectral components of solar irradiances

The shortwave solar radiation that arrives under the Earth's surface is spectrally composed of the ultraviolet, photosynthetically active, and near-infrared components. Having information about the availability of these parameters in different locations allows public agencies and scientific institutions to plan, execute, and manage energy projects in Brazil. Therefore, this information can be obtained from weather measurements with wide temporal and spatial coverage. However, the solar radiation measuring process is not an easy task, due to the uncertainties caused by instrumentation and operational problems. Thus, the aim of this study was to implement a procedure for erroneous measurements detection of ultraviolet, photosynthetically active, and near-infrared solar irradiances, collected in Botucatu (SP) - Brazil during the years 2001 to 2006, in sub-hourly temporal resolution (5 minutes in Wm-2), to certify the quality of the obtained measured values. For this purpose, analyzes were carried out by two quality check analyzes, the first one to verify whether the measurements were within physically possible limits and the second one whether they were within extremely rare but accepted limits. It was possible to identify that the near-infrared solar irradiance had the highest number of erroneous measurements flagged (0.310% of the total values), followed by photosynthetically active (0.162% of the total values) and, with less flags, from ultraviolet (0.047% of the total values). Measurements of spectral components of solar irradiances that were not flagged as erroneous by the quality checks procedure are considered valid, and can be used in future scientific investigations.
STUDI KASUS: ANALISIS PROSEDUR PEMERIKSAAN MR IMAGING ORBITA DENGAN MEDIA KONTRAS PADA KASUS RETINOBLASTOMA

Diagnosis of orbital is recommended for orbital MRI examinations, on of the pathology is retinoblastoma. orbital organs contain a lot of soft tissues so the orbital MRI is one of the right choices because it can appear anatomically and pathologically in a cross-sectional orbital, in two dimensions and three dimensions. Examination protocols on orbital MRI in retinoblastoma cases have several sequences in pre- and post-contrast agent. The purposes of this research was to determine the orbits MRI procedure in the retinoblastoma case.The Methods of this research is a descriptive of qualitative with case study method. The data were collected in unit of radiology DR. Saiful Anwar Malang by using observation method, documentation and interview of radiolog and radiographers. Data analyzed by using presented and data reduction to get the conclusion and suggestion. The results is an orbital MRI examination is concerned with MRI safety with patient screening and informed consent. The protocol for pre-contrast agent orbital MRI was T1 3D TSE axial, T2 TSE axial, DWI axial, T2 TSE fat saturated axial , and T2 DRIVE axial. On post-contrast agent using sagittal T1 FFE sequences, T1 3D TSE axial . The use of slice thickness is 3mm and 1mm in 3D image, 2D T2 DRIVE and 2D T1 FFE post contrast. gadolinium contrast agent as much as 5 mmol/10ml injection intravenously. The selection of sequences in the protocol of orbital MRI can produce detailed orbital anatomy images and provide sufficient clinical information to diagnose retinoblastoma.
Text-to-image generation based on AttnDM-GAN and DMAttn-GAN: applications and challenges

The deep fake faces generation using generative adversarial networks (GANs) has reached an incredible level of realism where people can’t differentiate the real from the fake. Text-to-face is a very challenging task compared to other text-to-image syntheses because of the detailed, precise, and complex nature of the human faces in addition to the textual description details. Providing an accurate realistic text-to-image model can be useful for many applications such as criminal identification where the model will be acting as the forensic artist. This paper presents text-to-image generation based on attention dynamic memory (AttnDM-GAN) and dynamic memory attention (DMAttn-GAN) that are applied to different datasets with an analysis that shows the different complexity of different datasets’ categories, the quality of the datasets, and their effect on the results of the resolution and consistency of the generated images.
Skeleton based human action recognition with relative position encoding

Aiming at the fact that the current graph convolution operation based on skeleton graph is limited in local adjacent nodes, or the overall relative position information of skeleton is omitted, an enhancement method of joint point position information based on relative position encoding of skeleton is proposed. The proposed method takes the central joint point of the human trunk as the root node, and all joint nodes form a tree structure according to the natural connection of the body, and the code of each joint node inherits the code of its parent node and also includes its own number in the sibling node. In addition, considering that the number of channels in the graph-based convolutional network model is generally larger, and the channel information itself has a strong correlation, the channel information frequency division and recombination operation is proposed to reflect the difference of information in different frequency bands in the channel. Experiments show that the proposed method has a certain effect on improving the effect of the embedded model.
Detection of abnormal dangerous behavior in electrical power grids based on the fusion of contextual features

Intelligence inspection of the electrical power grid can effectively improve the efficiency of inspection. However, the complex scenes between grid work and the interference of similar objects seriously affect the recognition accuracy of target identification. To address the above problems, this paper proposes the detection of abnormal dangerous behaviors in power grids based on contextual feature fusion. First, the feature extraction backbone network is constructed to obtain image features. Second, the hierarchical contextual attention mechanism is constructed to capture contextual features. Finally, the target detection model with contextual feature fusion is constructed to achieve grid abnormal risk behavior detection. The model proposed in this article is compared with the existing object recognition model in the simulation of the three datasets of Safety helmet (hardhat) wearing detect dataset, Hard Hat Workers dataset, Safety Helmet Detection dataset. A large number of experiments have proved the object recognition algorithm proposed in this paper is effective and outperforms existing algorithms. The average recognition accuracy of the proposed model is 0.948, which is improved by 1.02%.
Intelligent control method of UAV cluster based on neighborhood following

In the actual control process of unmanned aerial vehicle (UAV), the current intelligent control method of the UAV cluster has insufficient binding force on the common control of multiple UAV clusters, which easily leads to the problem that the UAV is out of control and can not continue the task. Therefore, an intelligent control method for UAV clusters based on neighborhood following is proposed. The adaptive following control model of the UAV group is designed, and the mathematical model of the UAV group motion is built. The task scheduling between individuals is optimized to enhance the frequency of information exchange between UAVs. Based on the domain following, the intelligent control law of following target between individuals in the UAV cluster is established. The comparative experimental results show that the method can ensure the close combination of UAV groups, and can reach a stable state in about 60s. The control efficiency is greatly improved compared with the traditional method, which has practical significance for strengthening the intelligent control technology of the UAV groups in the future.
PERSPEKTIF ORANG TUA TENTANG PROSES BELAJAR SISWA SEKOLAH DASAR DI MASA PANDEMI COVID 19 DI GAMPONG IE MASEN KAYEE ADANG KECAMATAN SYIAH KUALA KOTA BANDA ACEH

The world is currently busy with the emergence of corona virus diseases (Covid 19). The government has made various efforts to limit activities, one of which is conducting an online learning process. The role of parents is important to accompany children during their studies. The purpose of this study was to determine the parents' perspective on the learning process of elementary school students during the covid 19 pandemic in Ie Masen Kayee Adang Village, Syiah Kuala District, Banda Aceh City in 2021. This type of research is descriptive observational, the research was carried out on June 14 to 19, 2021. The population in this study was all parents of elementary school age children living in Gampong Ie Masen Kayee Adang, Syiah Kuala District, Banda Aceh City, with sampling using proportional random sampling technique as many as 61 people. The results showed that most of the respondents' parental perspectives on the student learning process were in the less category as many as 35 respondents (57.4%), student learning process methods were in the good category as many as 31 respondents (50.8 %), student learning process are in the good category as many as 36 respondents (59%), student learning process are in the good category as many as 33 respondents (54.1%). it is hoped that this research can provide input for teachers in designing learning methods so that it is easy for students to understand when learning during the COVID-19 pandemic, so that it can make it easier for parents in learning process and can improve the learning achievement of children.
Pacularities of the scientific and technical activities monitoring of employees of national research and educational institutions

The author tries to analyse the foreign and domestic experience of data management of the research activity as well as national, regional and university data management systems. In addition, these theses include the result of systems with open source code testing, which can be used in research activity.