Reviewerly/exHarmony huggingface dataset:
Rancang Bangun E-Learning Pronunciation Bahasa Inggris STKIP Setiabudhi Rangkasbitung Berbasis Web

Lecture courses in the English Language Education Study Program of STKIP Setiabudhi Rangkasbitung are still conducted in face-to-face class, so the students who do not attend lectures cannot know the pronunciation material at that time, because the Pronunciation course is a practical course in the English pronunciation system. The E-Learning Pronunciation is built so that lectures can be carried out anywhere and anytime without reducing the quality of the teaching and learning process. Therefore, the students who are left behind can continue to follow the Pronunciation course material, as well as habituating students in utilizing communication and information technology. E-Learning Pronunciation is important to be built to improve the ability of students’ pronunciation when doing distance learning, so that students are clearer and more firm in understanding Pronunciation so that there are no errors in English pronunciation. Participants in this study were first semester students of English education study programs. This study uses an experimental research design with the Prototype System development method and system of testing uses Black box testing.
Sistem Pendukung Keputusan Pemilihan Pegawai Honorer Kelurahan Babura dengan Metode MFEP

The employee is a person who works at the company with a predetermined time and also with a fixed salary as well. Honorary employees have fewer facilities than employees but. A company is not responsible for providing various traditional employer benefits, including taxes, social security and workers' compensation. Like a wise in the village office of Babura, each village office has a number of temporary employees to help work from the village office, including in Babura village office. This village also has several people as honorarium employees. In a certain period, employee salaries will change and will be replaced by new people. But to choose employee salaries cannot also be done easily. There are several requirements that must be met so that the prospective employee is well received. Several criteria will be taken into account in the selection process for prospective employee employees. Decision support systems are a good method of determining which employees are appropriate or not. The Multifactor Evaluation Process (MFEP) method can assist the Babura village office in determining who will be selected to be an honorarium employee at the office. By applying this method, the Babura village office will be greatly helped in assessing prospective employee salaries to be employed in the village office
Semantic Segmentation of Remote Sensing Images Based on Multi-Model Fusion

Abstract Convolutional neural networks have created a new field in the research of semantic segmentation of remote sensing images. However, different network structures have different effects on the semantic segmentation of different land types. In this paper, the original data set is expanded, and an improved U-Net model is used to train a model for each type of feature target. Then combined with conditional random field (CRF) and image overlapping strategy for optimization processing; Finally, the two binary classification models obtained by training are fused to obtain multi-classified semantic segmentation images. Solve the obvious problem of large-scale remote sensing image edge stitching. The experimental results show that this method has higher accuracy in solving the segmentation of large-scale remote sensing images.
ETHNOCULTURAL APPROACH AS THE BASIS FOR THE ETHNOORIENTED CONTENT OF YOUTH EDUCATION

The relevance of the article is due to the need to create conditions for the provision of competencies in the field of intercultural and interethnic communication, and future specialists must comply with the mandatory requirements for the implementation of professional educational programs for undergraduate training in «Organization of work with youth». The analysis of existing programs and Federal State educational standards of higher education allows to assert that higher educational institutions use the necessary pedagogical tools to ensure the training of specialists, endowment with relevant competencies focused on compliance with the values and norms of national culture, competencies aimed at creating conditions for the spiritual and cultural development of young people, competencies ensuring the ability to participate in the prevention of religious extremism, racial and interethnic hatred in young people. The authors of the article present the experience of including an ethnocultural component in the educational process of students of the Institute of social communications of Udmurt State University, which significantly increases the degree of development of ethno-cultural and professional competencies by future specialists in working with young people.
Neural reuse in multifunctional neural networks for control tasks

Living organisms perform multiple tasks, often using the same or shared neural networks. Such multifunctional neural networks are composed of neurons that contribute to different degrees in the dif...
TECHGENICS’ DATA SECURITY COMPLIANCE

Techgenics had a decision to make. Their vulnerability management program needed to be compliant with federal regulations and simultaneously they were being hit with budget reductions. Jay Santos had to decide what was the best path forward that met the requirements and kept cost under control.
A Method for Constructing Knowledge Graph of Ethnic Cultural Information Resources

In order to construct the knowledge graph of ethnic cultural information resources, we first use the Chinese word segmentation system and user-defined thesaurus to segment and part-of-speech tagging the data of ethnic cultural dictionary, and remove the punctuation. Then the text data is detected. If the number of continuous word segmentation is not less than the set threshold, then to perform manual word segmentation operation, and to add the result of manual word segmentation to the user-defined thesaurus of Chinese word segmentation system until there is no new word. Then we extract the attributes of the text data after the correct segmentation to build the domain knowledge graph. We detect the repeatability of the domain knowledge graph, delete the duplicate data, and store the knowledge graph. Finally, we link the stored domain knowledge graph with resources.
Intuitive Estimation of Speed using Motion and Monocular Depth Information

Advances in deep learning make monocular vision approaches attractive for the autonomous driving domain. This work investigates a method for estimating the speed of the ego-vehicle using state-of-the-art deep neural network based optical flow and single-view depth prediction models. Adopting a straightforward intuitive approach and approximating a single scale factor, several application schemes of the deep networks are evaluated and meaningful conclusions are formulated, such as: combining depth information with optical flow improves speed estimation accuracy as opposed to using optical flow alone; the quality of the deep neural network results influences speed estimation performance; using the depth and optical flow data from smaller crops of wide images degrades performance. With these observations in mind, a RMSE of less than 1 m/s for ego-speed estimation was achieved on the KITTI benchmark using monocular images as input. Limitations and possible future directions are discussed as well.
Near-real time estimation of tsunami sources using a classification of waveforms observed at dense ocean bottom pressure sensor

&amp;lt;p&amp;gt;A dense cabled observation network, called the seafloor observation network for earthquakes and tsunami along the Japan Trench (S-net), was installed in Japan. This study aimed to develop a near-real time tsunami source estimation technique using a simple classification of waveforms observed at the ocean bottom pressure sensors in S-net. To investigate the technique, synthetic pressure waveforms at those sensors were computed for 64 tsunami scenarios of large earthquakes with magnitude ranging between M8.0 and M8.8. The pressure waveforms within a time window of 500 s after an earthquake were classified into three types. Type 1 has the following pressure waveform characteristic: the pressure decreases and remains low; sensors exhibiting waveforms associated with Type 1 are located inside a co-seismic uplift area. The pressure waveform characteristic of Type 2 is that one up-pulse of a wave is within the time window; sensors exhibiting waveforms associated with Type 2 are located at the edge of the co-seismic uplift area. The other pressure waveforms are classified as Type 3.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Subsequently, we developed a method to estimate the uplift area using those three classifications of pressure waveforms at sensors in S-net and a method to estimate earthquake magnitude from the estimated uplift area using a regression line. We systematically applied those methods for two cases of previous large earthquakes: the 1952 Tokachi-oki earthquake (Mw8.2) and the 1968 Tokachi-oki earthquake (Mw8.1). The locations of the large computed uplift areas of the earthquakes were well defined by the estimated ones. The estimated magnitudes of the 1952 and 1968 Tokachi-oki earthquakes from the estimated uplift area were 8.2 and 7.9, respectively; they are consistent with the moment magnitudes derived from the source models. Those results indicate that the tsunami source estimation method developed in this study can be used for near-real time tsunami forecasts.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This method is so simple that we do not need any numerical tsunami simulation or other sophisticated techniques but only need the classification of observed pressure data into three types.&amp;lt;/p&amp;gt;
The Effectiveness Of Word Card As Media To Improve Students’ English Vocabulary

This research was aimed at finding out the effectiveness of word card to improve students’ english vocabulary. The subject of this study was 27 students at first grade of SMKN 1 Batulayar. In which consist of 16 males and 11 females. It was classroom action research, each cycle consist of four steps: planning, acting, observing and reflecting. The researcher used observation sheet to find out the activity during learning process and questionnaire to find out students improve in learning vocabulary. From this research, the students could improve their vocabulary by using word card. It proved in the first cycle, the students mean score was 72.7 there were 18 students passing KKM and the percentage was 66.6%. And it more improved in the second cycle, the students mean score 87.96 , there was all of students passing KKM and the percentage was 100%. Therefore, students become more enthusiastic and they were more active in learning English vocabulary. From those result the writer concludes that word card method is able to improve students’ English vocabulary of SMKN 1 Batulayar at first grade is successful.
L1‐norm based discriminant manifold learning for multi‐label image classification

Recently, L1-norm based robust discriminant feature extraction technique has been attracted much attention in dimensionality reduction. However, most existing approaches solve the column vectors of the optimal projection matrix one by one with a greedy strategy. Moreover, they are not suitable for solving the multi-label image classification. To solve these problems, the authors give a model named L1-norm based discriminant manifold learning in this study. An iterative non-greedy algorithm is proposed to solve the objective and the obtained optimal projection matrix necessarily best optimise the corresponding trace ratio objective function, which is the essential criterion function for general supervised dimensionality reduction. They also analyse the convergence of the authors’ proposed algorithm in detail. Extensive experiments on some databases illustrate the effectiveness of their proposed method.
Enhancing Phase Sensitivity in Mach–Zehnder Interferometers for Arbitrary Input States

To enhance the phase sensitivity of Mach–Zehnder interferometers, we use a tunable phase shift before the light beams are injected into the interferometer. The analytical result of the optimal phase shift is obtained, which only depends on the initial input states. For a non-zero optimal phase shift, the phase sensitivity of the interferometers in the output ports is always enhanced. We can achieve this enhancement for most states, including entangled and mixed states. The optimal phase shift is exhibited in three examples. Compared to previous methods, this scheme provides a general way to improve phase sensitivity and could find wide applications in optical phase estimations.
Time-resolved multiphoton effects in the fluorescence spectra of two-level systems at rest and in motion

We study the time-resolved fluorescence spectrum in two-level systems interacting with an incident coherent field, both in the weak and intermediate coupling regimes. For a single two-level system in the intermediate coupling case, as time flows, the spectrum develops distinct features, that are not captured by a semi-classical treatment of the incident field. Specifically, for a field on resonance with the atomic transition energy, the usual Mollow spectrum is replaced by a four peak structure, and for a frequency that is half of the atomic transition energy, the time-dependent spectrum develops a second harmonic peak with a superimposed Mollow triplet. In the long-time limit, our description recovers results previously found in the literature. After analyzing why a different behavior is observed in the quantum and classical dynamics, the reason for the occurrence of a second harmonic signal in a two-level system is explained via a symmetry analysis of the total (electron and photon) system, and in terms of a three level system operating in limiting regimes. We find an increased second harmonic signal in an array of two-level systems, suggesting a superradiance-like enhancement for multiple two-level systems in cavity setups. Finally, initial explorative results are presented for two-level model atoms entering and exiting a cavity, which hint at an interesting interplay between cavity-photon screening and atomic dynamics effects.
Application Prospect of Blockchain Technology in Power Field

In the past two years, “blockchain” has been a hot topic, and various industries are looking for a “blockchain +” model that suits them. The International Renewable Energy Agency (IRENA) released an innovation report in the field of renewable energy, introducing the application of blockchain in the field of electricity. The development of domestic blockchain has also attracted the attention of all walks of life. As one of the most cutting-edge and disruptive technologies in the “smart +” era, its technology form and the ubiquitous electric power Internet of Things construction of the State Grid Corporation Highly compatible, it can promote mutual trust between upstream and downstream industries, realize efficient data sharing, improve risk prevention capabilities, and effectively solve problems such as network security and multi-agent coordination faced by the ubiquitous construction of the power Internet of Things.
Effectiveness of Deep Learning in Real Time Object Detection

Deep learning based object detection has recently gained significant interest.This work focuses on real time object detection using two deep learning models named Faster Regional Convolution Neural Network (Faster-RCNN) and MobileNet Single Shot MultiBox Detector (MobileNet-SSD).An experiment is done using Python for programming, TensorFlow library for computing and OpenCV for computer vision.The Faster-RCNN and MobileNet-SSD models are trained using 400 images of four objects which are persons, watches, cell phones, and books.It is shown that for the images considered, Faster-RCNN can successfully detect these four objects with higher accuracy than MobileNet-SSD.Faster-RCNN also requires less time than MobilneNet-SSD for training the objects.However, Faster-RCNN model is slightly slower than MobileNet-SSD in real time object detection.
Deep Q-Network with Predictive State Models in Partially Observable Domains

While deep reinforcement learning (DRL) has achieved great success in some large domains, most of the related algorithms assume that the state of the underlying system is fully observable. However, many real-world problems are actually partially observable. For systems with continuous observation, most of the related algorithms, e.g., the deep Q-network (DQN) and deep recurrent Q-network (DRQN), use history observations to represent states; however, they often make computation-expensive and ignore the information of actions. Predictive state representations (PSRs) can offer a powerful framework for modelling partially observable dynamical systems with discrete or continuous state space, which represents the latent state using completely observable actions and observations. In this paper, we present a PSR model-based DQN approach which combines the strengths of the PSR model and DQN planning. We use a recurrent network to establish the recurrent PSR model, which can fully learn dynamics of the partially continuous observable environment. Then, the model is used for the state representation and update of DQN, which makes DQN no longer rely on a fixed number of history observations or recurrent neural network (RNN) to represent states in the case of partially observable environments. The strong performance of the proposed approach is demonstrated on a set of robotic control tasks from OpenAI Gym by comparing with the technique with the memory-based DRQN and the state-of-the-art recurrent predictive state policy (RPSP) networks. Source code is available at https://github.com/RPSR-DQN/paper-code.git.
Focus Group Discussions E-Learning based on Prior Knowledge and Problems in the Covid-19 Pandemic Period

The Covid-19 pandemic forced many campuses around the world to close. In Indonesia, the closure of the campus began in mid-March 2020. The closure of the campus for a long time will harm students. To prevent this, the Indonesian government encourages campuses to hold online lectures or E-Learning. One of the campuses implementing E-Learning is STIE Ekuitas Bandung. The change from traditional lectures to E-Learning is not as easy as the theory. There are technological limitations, especially connections that cause students to have problems when forced to attend college in the form of video streaming. Besides, the economic recession caused by the pandemic is also another obstacle. On the other hand, most lecturers do not know how to give lectures effectively via the web, because they cannot get feedback from students. One solution we propose in this paper is to revive group discussions on school E-Learning sites. Using the student's Prior knowledge about E-Learning as a parameter, we conclude that the learning design provided in e-learning has to be slower compared to face-to-face learning with the aim that respondents have time to read, understand and finally understand with their efforts.
THE INFLUENCE OF CONTEXTUAL TEACHING AND LEARNING APPROACH AND LEARNING MOTIVATION TOWARD WRITING SKILLS OF PANTUN IN THE FIFTH GRADE STUDENTS OF ELEMENTARY SCHOOL

This study was conducted to observe the effect of the Contextual teaching and learning (CTL) approach and learning motivation on the skills of writing Pantunof fifth graders in elementary school. This research was an experimental research with the subject of fifth grade students and carried out in Elementary School Cluster I Pariaman City. The research instruments were a learning motivation questionnaire and performance test. Data analysis was carried out using a two-way ANAVA and t-test. Learning outcomes obtained by using the CTL approach to students was higher than conventional methods, both low motivation and high motivation. CTL approach makes students more interested and enthusiastic in learning to write Pantun. As well as creating new experiences for students, it makes students active and learning more fun compared to conventional methods which make some students become passive
Robotic Path Planning and Fuzzy Neural Networks

Fuzzy logic has gained excessive attention due to its capacity of handling the data in a much simpler way. It is applied to decrease the intricacy of already existed solutions and to provide the solution of new problems also. On the other hand, neural networks are distinct because of their robust processing and adaptive capabilities in dynamic environment. This paper mainly reviews the primary ideas and contribution of neural network system and fuzzy logic in the field of robotic path planning. Several hybrid techniques, which are being utilized in bringing dream of mobile robots to reality are discussed
Implementasi Sistem Peramalan Persediaan Barang Menggunakan Metode Moving Average

PT. XYZ is a company engaged in the sale of drugs and vitamins for livestock, each period the number of requests for goods always changes. Problems faced by PT. XYZ, which is expired goods because in a certain period the amount of stock is piling up due to the small amount of demand that causes the company to lose. The purpose of this study is to forecast the inventory process in order to minimize the company's losses against the estimates made so far. In order to minimize these problems, the authors model the forecasting information system for procurement of goods using VB.Net and MYSQL and combine the Moving Average method. The method used is the data collection and processing, and continued with the data analysis process, to model the system requirements of the writer using UML. The final result of this study is that the value of accuracy reaches 88% so that the inventory forecasting system using the moving average method can help managers in making decisions to determine the process of inventory in the future
ANGULAR PHOTOGRAMMETRIC ANALYSIS OF FACIAL PROFILE OF IGBOS OF ANAM COMMUNITY OF NIGERIA

Beauty is most expressed by the face than any other part of the body. The beauty and uniqueness of the face is determined by the sizes and shapes of various parts of the face and various facial angles. The aim of this study was to characterize the angular facial profiles of the Indigenes of Anam, for application in aesthetics. It was a cross sectional study. Sample size was 500 (250 males and 250 females) and age range was between 18 to 40 years. Facial photographs were taken with a digital camera. A computer software was used to measure the following angles: nasofrontal (NF) angle, nasomental (NM) angle, nasofacial (NFa) angle, nasolabial (NL) angle and angle of facial convexity (AFC). Means of the angles were determined and test of significance was done using student t - test. The mean age for the sample was 22.09 ± 0.46 years (females) and 23. 87 ± 0.44 years (males). Females had significantly higher values ​​than males in NF angle (females-134.00 , males-131.0 0 ), NM angle (females-129.0 0 , males-127.0 0 ), NL angle (females-78.9 0 , males-73.4 0 ) and AFC (females-165.0 0 , males-163.0 0 ). There was no significant sex difference in the value of NFa angle (females-40.9 0 , males-40.4 0 ). Some of the facial angular parameters correlated with each other in males and females but the strength of the correlations were small. This study shows that there was significant sex difference in NF, NM, NL angles and AFC, but not in NFa angle. There was mild correlation between the various facial angles in both sexes.