Reviewerly/exHarmony huggingface dataset:
대학교 영문법 수업에 대한 학습자 인식 연구

This study examines how English major students at a college perceive grammar instruction and learning. The study analyzed two types of data: (1) students’ reflection papers and (2) survey data. In the first experiment, the participants were 41 Korean university students who took a one-semester course, Practical English Grammar, offered by their institution’s English department. At the semester’s end, the students produced reflection papers responding to three questions. In the second experiment, 40 Korean university students completed a questionnaire about Practical English Grammar instruction and grammar learning. The results indicate that most of the participants perceived the necessity of grammar instruction in order to facilitate their ability to speak and write better English and that they believed that using the target grammar in productive activities would help them improve their grammatical skills.
Analysis and Prediction of Alerts in Perimeter Intrusion Detection System

Perimeter surveillance systems play an important role in the safety and security of the armed forces. These systems tend to generate alerts in advent of anomalous situations, which require human intervention. The challenge is the generation of false alerts or alert flooding which makes these systems inefficient. In this paper, we focus on short-term as well as long-term prediction of alerts in the perimeter intrusion detection system. We have explored the dependent and independent aspects of the alert data generated over a period of time. Short-term prediction is realized by exploiting the independent aspect of data by narrowing it down to a time-series problem. Time-series analysis is performed by extracting the statistical information from the historical alert data. A dual-stage approach is employed for analyzing the time-series data and support vector regression is used as the regression technique. It is helpful to predict the number of alerts for the nth hour. Additionally, to understand the dependent aspect, we have investigated that the deployment environment has an impact on the alerts generated. Long-term predictions are made by extracting the features based on the deployment environment and training the dataset using different regression models. Also, we have compared the predicted and expected alerts to recognize anomalous behaviour. This will help in realizing the situations of alert flooding over the potential threat.
Identifikasi Bawang Merah dan Bombay dengan Pendekatan Radial Basis Function Neural Network (RBFNN)

A fact that identification in a human knowledge is very important role. Identification is able to summarize the knowledge, so it is easy to understand. Therefore, the classification of leek or red onion is an interesting study because the similarity of physical appearance of morphology between these two commodities is difficult to distinguish directly. This research tries to detect the difference between Bombay onion and red onion by using neural network with RGB color feature extraction method and gabor filter. The results of the detection are able to classify the types of onions, whether the onion is included in the type of leek or red onion. Many methods can be used to perform classification, one of which is Radial Basis Function Neural Network (RBFNN). After performing simulation with Matlab program, 100 images that become test data can be recognized perfectly.
The Correlation Between Vocabulary Mastery and Translation Accuracy of Students at English Language as Foreign Application Standard (Elfast) Pare Kediri

Translation is one of important knowledge which has to be mastered by the language learners, especially English Learners. The English learners will need some English books to enrich their knowledge. By having a good translation, the learners can transfer the content of the book correctly. Mastering vocabulary is very important in doing translation. The translator cannot always depend on the lexical meaning which were provided in the dictionary. The translator have to find the equivalence meaning in the translation in order the readers can read the target language as like they read the original. There are two problem statements in this study. The first one is whether the more vocabulary students have, the more their translation accuracy or not. The second is how significant is the correlation between vocabulary mastery and translation accuracy of students translation class at English Language as Foreign Application standard at  ELFAST Pare Kediri. Population of this research is a whole number of the students who join translation (practice) subject which consist of one class with only 17 students of 2015 academic year. The instruments that were used in this research are documentation and tests (vocabulary mastery test and translation accuracy test). The obtained data of tests was analyzed by using statistic corelation product moment formula. The result showed that the more vocabulary of students have, the more their translation accuracy; and there is a significant correlation between vocabulary mastery and translation accuracy of students translation class at  English Language as Foreign Application standart  (ELFAST) PARE KEDIRI  with high interpretation either in significant level of 5% or 1%. It was proven by the result of the obtained data, rxy is greater than rtable (0,805>0,482 or 0,606). It can be drown a conclusion that the students’ translation accuracy is very much influence by the amount of vocabularies they have. So, if the students want to increase their translation accuracy, the first step that should be done is increasing their vocabulary mastery. Then, increase the other factors that can influence their translation accuracy, such as grammar or syntax, morphology, semantic, etc.
Penerapan Data Mining Dalam Prediksi Penjualan Prabot Rumah Tangga Menggunakan Metode Apriori Pada Toko Hasanah Mart

The application of data mining is very well done, especially in predicting transaction patterns that are very often done in the sale and purchase of household furniture, household furniture is goods that are very much in demand by many groups, especially mothers, the higher the amount of demand, the more data transactions that must be processed, dug up to obtain the desired information and needed a method approach to processing transaction patterns that are the most desirable so that a store or company can sort out the inventory of goods that must be met based on the level of market needs, appropriate methods and has been widely used in one of the sales prediction is the Apriori method is a method that uses association rules that aim to see the associative relationship between a combination of items with other items, in data mining using the a priori method can also be done with very much data from and processed with a fast time and a very good level of data prediction accuracy.
Team Up! Cohesive Text Summarization Scoring Sentence Coalitions

According to Aggarwal [] the extractive summarization is solely about scoring sentences to maximize the topical coverage and minimize redundancy, while coherence and fluency are to be considered only in the case of abstractive summaries. It is a rather strong opinion that this paper aims to argue with by introducing different functions of text summarization, the notion of text coherence and cohesion, and last but not least by offering new methods allowing for both sentence extraction and text fluency (to a certain level). This article aims at offering a model that will be as simple as possible (but no simpler, as would Einstein put that) to satisfy the goal of informative extractive summary with a certain level of cohesion determined by sentence connectives. This abstract has been generated with the algorithm proposed in the paper.
Neural Network Subgraphs Correlation with Trained Model Accuracy

Neural Architecture Search (NAS) is a computationally demanding process of finding optimal neural network architecture for a given task. Conceptually, NAS comprises applying a search strategy on a predefined search space accompanied by a performance evaluation method. The design of search space alone is expected to substantially impact NAS efficiency. We consider neural networks as graphs and find a correlation between the presence of subgraphs and the network’s final test accuracy by analyzing a dataset of convolutional neural networks trained for image recognition. We also consider a subgraph based network distance measure and suggest opportunities for improved NAS algorithms that could benefit from our observations.
Sistem Pendukung Keputusan dalam Menentukan Judul Skripsi Mahasiswa dengan Metode WASPAS, COPRAS dan EDAS berdasarkan Penilaian Dosen

The title of the thesis is the student's scientific exposure. Students are confused about determining the title of the thesis even though the rejected title can be used as learning by other students from the title that has been accepted or has not submitted the title, they do not understand the rules and assessments of all titles when submitted. In addition to this, lecturers related to fields who have researched or studied at a particular Faculty have not been utilized. One student proposes that there are three or more titles, this is very difficult if it is not explained in detail, then if it is rejected, they must submit it again until it is accepted so that it is ineffective and efficient that it must be overcome. This research produces a new technique in submitting a thesis title that is assessed automatically based on the lecturer's assessment, all resolved with a decision support system. Submission of a long thesis title is caused by many factors. Thesis title assessment must be understood by all groups. By adjusting the title with the vision and mission according to the study program. A decision support system is a technique for presenting a decision. In this case the settlement was done by using the WASPAS, COPRAS and EDAS methods. The WASPAS method is to use the compensation method, the attributes are independent, the qualitative attributes are converted into quantitative attributes. The COPRAS method is a normalized decision matrix, a weighted normalized decision matrix, a maximizing and minimizing index, a relative significance value. Then the EDAS method is very practical in conditions with contradictory attributes, and the best alternative is selected by calculating the distance from each alternative from the optimal value. Three methods of filtering and determining the title of the thesis. The criteria for receiving the thesis title are based on the feasibility assessment, renewal, conformity with the vision and mission and academic values associated with the submission of the thesis title. The assessment is also integrated with each lecturer who teaches subjects related to the thesis title. The research method in determining the title of the thesis with quantitative methods. Examining every relationship starting from students, lecturers, thesis titles, criteria related to determining thesis titles. Test all related theories in a measured manner and take test results based on hypotheses. This research produces a complex thesis title determination information system software. comparing the three thesis titles by one of the students, each thesis title has criteria, namely research renewal, vision suitability, mission suitability, student self-grade scores and similarities in existing research, from the results based on the three processed methods obtained  Algoritma Boyer Moore Untuk Penyaringan Pesan Becomes an accepted title submission.
Sistem Pakar Mendiagnosa Penyakit Flu Babi Menerapkan Metode Hybrid Case Based

Expert system is an Artificial Intelligence program that combines the knowledge base with inference machines. This is a part of high-level specialization software, which seeks to duplicate the functions of an expert in a particular area of expertise. The expert system for diagnosing peanut plant disease aims to diagnose swine flu in humans, so that appropriate treatment can be carried out and does not cause outbreaks in the community. Swine flu is a disease that is very dangerous to humans, because it can cause death. For this disease, a very fast diagnosis is needed because it can be transmitted between humans to animals or vice versa and this disease can spread to an age group. Medical implementers, especially doctors, are still very limited when compared to the population that continues to increase. This makes people suffering from swine flu difficult to consult with doctors, as a result of this swine flu disease is increasingly widespread due to slow handling. Of these problems raises the idea of making an application that can diagnose swine flu disease and can help people affected by swine flu to get fast treatment. The methodology used in making this application is the Hybrid Case Based method. which is a combination of case based reasoning (CBR) with rule based reasoning (RBR). Basically CBR is one method that uses the previous case solution to solve a new case. While the RBR technique checks the input data based on the rules that have been stored in the rule base, both of which have their advantages and disadvantages.
Численная оценка вероятностного распределения перетоков мощности

Background. Active power flow capacities are used to monitor the compliance with power system stability requirements while configuring and running the system. The grid is modeled mathematically for such design. The projected daily load curve and its associated magnitudes are calculated per rules, i.e. set specifically for specific time intervals. However, actual loads are not constant and can generally be represented by an interval that contains the actual values. Grid parameters are also seasonal, as they are affected by accidents, weather, etc. In other words, a grid model uses a number of assumptions. This paper proposes Monto-Carlo simulation to simulate the parameters of a power system as affected by changing the initial loads at grid nodes as well as the line resistance values per Gaussian distribution. The method can find how these values affect power flows in grid branches as well as determine the margin of static stability. Materials and methods: Monte Carlo Method (method of statistical testing). Results. This methodology has been applied to a base-generator/base-load two-node system as well as to a base-load-generator three-node system; besides, it has been applied to the model of Radkino-Arlansky District, Republic of Bashkortosan’s power system. For these models, the team calculated the active and reactive power flows for each successfully computed operating mode that would not compromise static stability. The obtained values were used to plot distributions and to find the range of their variation. Then the static stability factors were calculated and their dependences on the interval length of initial grid parameters changing were constructed. Conclusions. Changing the range of power flows in the branches of the analyzed grid helped researchers find which of them had the greatest fluctuation range and which nodes would affect the flows the most if altered in capacity. Thus, the capacities of these nodes are subject to more accurate adjustment.
Tutorial on scaling of the discrete fourier transform and the implied physical units of the spectra of time-discrete signals

The combination of the time-discrete property of digital signals together with the commonly employed definition of the discrete Fourier transform (DFT) can cause ambiguity when interpreting magnitude spectra with respect to the physical unit of the signal under consideration. Standardized scaling of spectra increases the comparability of frequency-domain data that are published in scientific articles or data sheets of commercial products. We present and discuss in this tutorial a collection of the most relevant scaling options for DFT spectra to yield amplitude spectra, power spectra, and power density spectra, and we illustrate how an implied physical unit of the underlying signal is reflected by the magnitude of the spectrum. The tutorial is accompanied by Matlab/Octave scripts that demonstrate the different cases.
ANALISIS DAN BENCHMARKING DISK PERFORMANCE PADA WINDOWS AZURE VIRTUAL MACHINE (VM) DAN AMAZON WEB SERVICE EC2

Abstrak Pada era teknologi ini peran dari cloud computing sangat membantu penggunanya baik manusia secara individu maupun perusahaan. Cloud Computing sendiri adalah suatu layanan teknologi informasi dimana resource diambil dari internet melalui tool dan aplikasi berbasis website dan tidak terkoneksi lansung ke server. Terdapat dua contoh layanan cloud computing yaitu Windows Azure, dan Amazon Web Services. Pada analisis ini membahas perbandingan kinerja dan layanan disk performance dari dua platform Cloud Computing yaitu Amazon Web Services dan Microsoft Azure. Platform diuji menggunakan Virtual Machine yang sudah di sediakan dari masing-masing platform. Pengukuran performa dilakukan dengan menggunakan Phoronix Test Suite pada size b1ms, b2s, dan b2ms pada Windows Azure dan t2.small, t2.medium, dan t2 large pada Amazon Web Services dan hasil yang disajikan berupa parameter IOzone Write, IOzone Read, Dbench, dan UnpackKernel. Kata Kunci: Windows Azure, Amazon Web Services, disk performance Abstract In this technological era the role of cloud computing is very helpful for users both individuals and corporate individuals. Cloud Computing itself is an information technology service where resources are taken from the internet through web-based tools and applications and not connected to direct servers. There are three examples of cloud computing services namely Windows Azure, and Amazon Web Services. This analysis discusses the application of disk performance and service performance from the Cloud Computing platform, namely Amazon Web Services and Microsoft Azure. Platforms value using Instances that have been provided from each platform. Performance measurements are carried out using the Phoronix Test Suite at b1ms, b2s, and b2ms in Windows Azure and t2.small, t2.medium, and large t2 at Amazon Web Services and the results contain the parameters IOzone Write, IOzone Read, Dbench, and Unpack-Kernel. Keywords: Windows Azure, Amazon Web Services, disk performance
Transaction processing monitors on real-time databases

The development of distributed database systems has given rise to the transfer of data from one connected terminal to another. During the transmission of the data among various connected terminals, data can be corrupted, infrastructures can fail, hardware can malfunction and network can disconnect; all of these can cause failure in data transmission. This research focuses on Transaction Processing Monitor (TPM) on a real-time database and the standard middleware applications it provides for use in different e-commerce systems from large enterprises to medium and small businesses available on the internet to monitor the transfer of data between multiple terminals. The researchers reviewed many articles that provided detailed information on TPM because of the significant growth and popularity that it has gained. The paper also reviewed various concepts of Transaction Processing Monitors on real-time databases, different areas of TPM functionalities and transactional properties as well as the architectures of Transaction Processing Monitors. This paper also recommended high-end benchmark platform for Transaction Processing Monitor which is very vital to meet the high performance needs of online transactions and to determine their operational status either for success or failure.
Prevention of Denial-of-Service Attacks in 5GD2D Wireless Communication Networks Employing Double Auction Game Based Resource Trading

The commercial implementation of 5G networks in the recent past has contributed to the advent of an enhanced digital connective experience for users around the world. Among the various services offered by 5G networks, Device-to-Device (D2D) communication has emerged as an important strategy, allowing efficient resource utilization with the balancing of network load, both of which are critical issues. 5GD2D networks are however vulnerable to malicious users perpetrating attacks such as Denial-of-Service (DoS). The present work examines a probable DoS attack strategy for double auction game based resource trading in 5GD2D networks. The auction game based DoS attack dealt with in the present work degrades service through disincentivization of buyers and sellers to participate in the double auction. The paper then presents a strategy to mitigate such a DoS attack. The mathematical proof of the proposed method is presented along with corresponding simulation results confirming the veracity of the presented approach.
Prediction Of Coffee Prices With Backpropagation Neural Networks

Robusta coffee is a type of coffee plant in Indonesian, sepcialy at west lampung.lampung province have much spices of herbs plant.one of that spices is Robusta Coffe. Robusta coffee is the fourth most reliable source of Robusta coffee in the world. Robusta coffee prices in Lampung are influenced by the price of coffee from Vietnam and Brazil. This research was conducted to determine the predication of coffee prices. the price of this coffee can be used for government reference. This prediction is used for farmers to get more leverage. The training data is data for 2010-2014. Testing data used in 2015-2017. 2018 is used as an output target the algorithm used is backpropagationof neural network. the study uses five architectural models. They are 3-9-1,3-12-1,3-15-1 dan 3-19-1. the best model is3-15-1 with mse value is 0.00904753.therefor, this model is good for predicting prices, especially this result is good for the Lampung Keywords :Robusta, coffe, prices, prediction, neural network, backpropagation
Time Series Data Cleaning Based on Dynamic Speed Constraints

Errors are ubiquitous in time series data as sensors are often unstable. Existing approaches based on constraints can achieve good data repair effect on abnormal values. The constraint typically refers to the speed range of data changes. If the speed of data changes is not in the range, it is identified as abnormal data violating the constraint and needs repair, like if the oil consumption per hour of a sedan is negative or greater than 15 gallons, it is probably abnormal data. However, existing methods are only limited to specific type of data whose value change speed is stable. They will be inefficient when handling the data stream with sharp fluctuation because their constraints based on priori, fixed speed range might miss most abnormal data. To make up the gap in this scenario, an online cleaning approach based on dynamic speed constraints is proposed for time series data with fluctuating value change speed. The dynamic constraints proposed is not determined in advance but self-adaptive as data changes over time. A dual window mechanism is devised to transform the global optimum of data repair problem to local optimum problem. The classic minimum change principle and median principle are introduced for data repair. With respect to repair invalidation of minimum change principle facing consecutive data points violating constraints, we propose to use the boundary of the corresponding candidate repair set as repair strategy. Extensive experiments on real datasets demonstrate that the proposed approach can achieve higher repair accuracy than traditional approaches.