Reviewerly/exHarmony huggingface dataset:
PENGOLAHAN DATA PEGAWAI PADA SDN 16 BUMI AYU MENGGUNAKAN BAHASA PEMROGRAMAN VISUAL BASIC 6.0

State Elementary School 16 Bumi Ayu, is one of the primary schools in the city of Bengkulu is located at the highway Bumi Ayu Bengkulu city, the number of students reached 800 people. And teachers and employees 31 people. To improve human resources especially in the field of education, SD Negeri 16 Bumi Ayu has some routines one of which is the processing of employee data is done every semesterThe purpose of this research is to create a Data Processing Information Systems Officer On 16 SDN Bumi Ayu Using Visual Basic 6.0 programming language, so as to assist in data processing at SDN 16 Bumi Ayu. Where the research was conducted at the State Elementary School 16 Bumi Ayu is located at Jalan Raya Bumi Ayu. The timing of this research was conducted on 26 February to dengn March 26, 2016. In this study, data were collected by using observation, interviews, and literature. From the results of this study show that the presence of Employee Data Processing Information Systems using the programming language Visual Basic 6.0, can, provide significant benefits for the school
A SURVEY PAPER ON MACHINE LEARNING METHODS FOR DIGITAL SECURITY OPERATIONS

Inside the ever-developing and rapidly expanding field of digital security, it is about difficult to measure or legitimizethe clarifications why digital security has such an outsized effect. Allowing malevolent dangers to run wherever,whenever or in any setting is far from being adequate, and may cause strong injury. It especially applies to the Byzantinetrap of buyers and utilizing the net and friends data that digital security bunches are thinking that its difficult to shieldand contain. Digital security might be a fundamental idea for individuals and families the same, additionally fororganizations, governments, and scholarly foundations that work inside the compass of world system or on the otherhand net. With the office of Machine Learning, we will progress digital security scene. Organizations nowadays aregathering monstrous measures of client data. Data is at the heartbeat of any business-basic framework you'll have theoption to think about. This co-mutually incorporates foundation frameworks that are being executed nowadays. Thepresent innovative framework, that has arrange and digital security frameworks, is gathering enormous measures ofinformation and investigation on practically all the key parts of strategic frameworks. While individuals despiteeverything give the key operational oversight and astute bits of knowledge into the present framework, AI and AI arepicking up pace and gathering colossal force in a large portion of the territories of the present frameworks, regardless ofwhether it's situated on premise or inside the digital security house.
Students' Speaking Performance of English Department: Anthropolinguistic Approach

This research was aimed to find out the students' speaking performance of the English Department. This research used an anthropolinguistic approach with three components, namely: text, co-text, and context. This research applied two steps in collecting data, namely: in the dept-open-ended interview and direct-participatory observation. The research method employed the qualitative paradigm with an interactive model in some steps of analyzing data, namely: data display, data condensation, and drawing a conclusion/verifying. The research parameters were interconnection, evaluability, and sustainability. The research finding showed that the students' speaking performance was classified into speaking expression and students' speaking patterns consist of opening, core, and closing. The speaking component was a small group, it can be two, three, and four students in a group. The students' speaking function is a reference to be polite, respectful, and study hard. Generally, students' speaking performance is to encourage the students to speak politely, respectful, and keep them studying hard for improving their speaking capability in the education field.
No more rage against the machine: how the corpus-based identification of machine-translationese can lead to student empowerment

The aim of this article is to show how a linguistic analysis of a corpus of machine-translated texts, both quantitative and qualitative, can empower translation trainees by helping them define their added value over machine translation (MT) systems. In particular the aim is to show that MT, even when providing grammatically correct output, does not comply with linguistic usage, thus failing to provide natural-sounding translations as expected in today’s market for specialised translation. Following two avenues left open for future research in Loock (2018), this article provides the results of a corpus analysis of EN-FR machine-translated texts using 3 MT systems: DeepL (NMT) and the European Commission’s eTranslation in both its SMT and NMT versions. The quantitative results show that the linguistic characteristics of machine-translated texts differ from French original texts, with an almost systematic over-representation of a series of linguistic features, possibly but partially due to source language interference, while the qualitative analysis of a sample reveals finer-grained results (e.g. variability of results depending on (N)MT tool, frequency of adverb deletion). It is then explained how such results, leading to the identification of ‘machine-translationese’, are meant to be used in an educational setting to improve translator education, by (i) making students aware of the gap between machine-translated texts and original texts, and (ii) providing them with information on what to focus on during the post-editing process.
The Application of Computer Network Technology in Mathematics Teaching

Mathematics teaching can not only exercise students’ thinking, but also effectively improve their comprehensive quality, which is an important part of modern curriculum. Driven by the new curriculum reform, mathematics has become one of the most important subjects in education. However, due to the outstanding abstractedness of this course, the difficulty of mathematics education affects students’ effective learning of this knowledge to some extent. In order to improve the effectiveness of mathematics teaching, more and more teachers use multimedia network to assist teaching and create teaching culture scene with “picture, text, sound and image”. This paper analyzes the characteristics and teaching mode of computer network mathematics teaching, and discusses the computer network aided mathematics teacher’s classroom teaching by combining specific examples, which improves the teaching quality, optimizes the teaching process and enhances the teaching effect.
Moving from emergency remote teaching to digital education in times of pandemic

The evolution of technologies and communication networks have brought changes in society, driving the birth of new paradigms, models, educational communication processes and new teaching and learning scenarios But no one, not even teachers who already adopted online environments in their practices, imagined that such a rapid and emergency change would be necessary, almost mandatory, due to the expansion of the coronavirus In fact, with the abrupt arrival of the virus, educational institutions and teachers were forced to adopt distance learning practices, emergency remote teaching practices, very different from the practices of a quality digital education Therefore, the goal of this paper is to present, in a resume way, the basic principles for the design of an online environment, whether with regard to its organization, communication, selection of resources, preparation and evaluation of e-learning activities
A Little Bird Told Me: Discovering KPIs from Twitter Data

The goal of our research and experiments is to find the definitions and values of key performance indicators (KPIs) in unstructured text. The direct access to opinions of customers served as a motivating factor for us to choose Twitter data for our experiments. For our case study, we have chosen the restaurant business domain. As in the other business domains, KPIs often serve as a solution for identification of current problems. Therefore, it is essential to learn which criteria are important to restaurant guests. The mission of our Proof-of-Concept KPI discovery tool presented in this paper is to facilitate the explorative analysis taking Twitter user posts as a data source. After processing tweets with Stanford CoreNLP toolkit, aggregated values are computed and presented as visual graphs. We see our tool as an instrument for data discovery applicable, for example, to define new qualitative and quantitative KPIs based on the values found in the graph. The graph represents a complete view of aggregated data that corresponds to the search results according to the user-defined keywords, and gives easy access to detailed data (tweets) that, in its turn, leads to better understanding of the post context and its emotional coloring.
Image Compression and Reconstruction Using Encoder–Decoder Convolutional Neural Network

Image compression has always been an important aspect in computer vision problems where image transmission is involved. Hence, many different image compression techniques have been developed over time. Image compression algorithms using neural network have already been implemented but is quite complex and difficult to run on conventional hardware. Most neural compression algorithms are used in general to input encoding and reconstruction, but the proposed algorithm takes advantage of over-fitting on the train dataset to improve reconstruction accuracy. In this paper, we propose a convolution neural network for lossy image compression and another neural network for the reconstruction of the image which uses encoder–decoder architecture. This kind of architecture enables us to process a large number of images in parallel, thus decreasing the overall time taken to compress a large number of images. This algorithm is easy to run on highly parallel hardware architectures, and it can process large number of images at a time. This has a good application in data centers with a large amount of computing resources and parallel hardware.
Histogram Based Method for Unsupervised Meeting Speech Summarization

The appearance of various platforms such as YouTube, Dailymotion and Google Video has a major role in the increasing of the number of videos available on the Internet. For example, more than 15000 video sequences are seen every day on Dailymotion. Consequently, the huge gathered amount of data constitutes a big scientific challenge for managing the underlying knowledge. Particularly, data summarization aims to extract concise abstracts from different types of documents. In the context of this paper, we are interested in summarizing meetings’ data. As the quality of video analyzing’s output highly depends on the type of data, we propose to establish our own framework for this end. The main goal of our study is to use textual data extracted from Automatic Speech Recognition (ASR) transcriptions of the AMI corpus to give a fully unsupervised summarized version of meeting sequences. Our contribution, called Weighted Histogram for ASR Transcriptions (WHASRT), adopts an extractive, free of annotations and dictionary-based approach. An exhaustive comparative study demonstrates that our method ensured competitive results with the ranking-based methods. The experimental results showed an enhanced performance over the existing clustering-based methods.
Research Guides: SJU Library Streaming Video Resources: AVON (Academic Video Online)

Overview of the streaming video content providers currently available for SJU campus members through the library.
Predictive Filter for Estimating the Target Acceleration

In this paper, in the background of studying the terminal guidance of an air-to-air missile, a real time predictive filter is derived for target acceleration estimation without the utilization of angular measurements from gyros, which will reduce the overload and the miss distance of missiles attacking maneuverable targets. To compare to Kalman filter, the result of simulation shows this algorithm has more accuracy, availability and robustness.
Determining External Factors Analysis Summary (EFAS) Metric for Company External Factors Using Online News Titles

Companies want their business to run well and can survive in competition. To achieve these desires, the right strategy is needed in carrying out the business process. One of the initial approaches to implementing this strategy is by analyzing the company's environmental factors. There are two types of corporate environmental factors, namely internal and external factors. The stage that is difficult to do in the analysis of the company's environment lies in the analysis of the external environment because to conduct an external environment analysis is required to collect data that is not owned by the company. Strategies for the company's external factors can use existing methods in competitive intelligence, namely PEST and SWOT analysis. The method that focuses on external factors in SWOT analysis is EFAS metric. To overcome the problem of analyzing external environment, this study focus on determining EFAS metrics by using data from online news titles as a source of determining the company's external factors. The determination of EFAS metrics is carried out with two main modules, namely the text mining module and priority module using the AHP or TOPSIS algorithm. Titles obtained from online sites are classified using text mining and then given priority weight using the AHP or TOPSIS algorithm. The results show that text mining modules and priority modules applied to EFAS metrics produce 10 errors in classification testing data from a total of 40 data in the EFAS metric. So the results of the questionnaire showed an accuracy value of 75%. While the experimental results of the text mining module produce the SVM algorithm as the best algorithm compared to the Naïve Bayes and J48 algorithms with F-Measure values of 0.782.
Modified adaptive group lasso for high-dimensional varying coefficient models

This article focuses on variable selection for varying coefficient models in the case of the number of covariates being larger than the sample size. Combining B-spline basis function approximations with the modified adaptive group lasso, we establish selection consistency, convergence rate and asymptotic normality. Our contribution is that the marginal nonparametric estimates are used as weights of the adaptive group lasso. Simulation studies and two real data applications show that our method performs better than the method of Wei, Huang, and Li (2011 Wei, F., J. Huang, and H. Li. 2011. Variable selection and estimation in high-dimensional varying-coefficient models. Statistica Sinica 21 (4):1515–40. doi:10.5705/ss.2009.316.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]).
Multi-view classification via Multi-view Partially Common Feature Latent Factor Learning

In the real world, multi-view data usually consists of different representations or views. There are two key factors in multi-view data: consistency and complementarity. Unlike most multi-view learning algorithms based on nonnegative matrix factorization (NMF), the proposed method can make full use of the consistency and complementarity of data. This paper presents a new semi-supervised multi-view learning algorithm, called Multi-View Partially Common Feature Latent Factor (MVPCFLF) Learning. MVPCFLF is an extended learning form based on Partially Shared Latent Factor (PSLF) learning, which can make full use of common and special features to obtain latent representation. The key idea of MVPCFLF is to increase the constraints on common feature matrix so as to maintain the consistency of common features. The experimental results show that MVPCFLF is more effective than the existing multi-view learning algorithms.
Traffic Detection using Sentimental Analysis

Traffic is a major issue in many cities. There are no existing ways which are both cost effective and efficient to measure the traffic. Sentiment analysis is a contextual mining of text, which identifies and extracts subjective information in source material. The Social sites have a huge amount of information because of its vast users. In this project we will use twitter as the source. It is an active site which has many users and also tweets regarding the traffic. Sentiment Analysis is the mechanized procedure of breaking down content information and arranging it into sentiments positive, negative or impartial. Performing Sentiment Analysis on information from Twitter utilizing AI can assist organizations with seeing how individuals are discussing their image. Within excess of 321 million dynamic clients, sending a day by day normal of 500 million Tweets, Twitter permits organizations to contact an expansive crowd and associate with clients without middle people.
VIGENERE CIPHER DENGAN MODIFIKASI PLAINTEXT

Cryptography is knowledge of encoding messages by observe to security aspects. Cryptography uses two types of keys, namely symmetric keys and asymmetric keys. Vigenere cipher is a technique to encrypt messages by symmetric key. Vigenere cipher can be combined by several patterns and ASCII code. The pattern used can vary as long as the text can be returned to original message (can be decrypted). On this paper, we will modified plaintext before encrypt using vigenere cipher. The way to modified the plaintext are flip and shift rows of bit. The effect of the algorithm changes can be seen based on the renewal value obtained. If the correlation value gets smaller, it means the algorithm is better. The results of this study is the correlation value using vigenere cipher with modified plaintext is better compared to vigenere cipher with original plaintext. Keywords: ASCII, Patterned, Bits, Vigenere Cipher
Modernizing the Academic Library

The digital age has been presented as a stark contrast to everything that libraries have had to deal with prior to now. While it is true that academic libraries have had to change to stay relevant in the digital age, the changes are not as severe as was once thought. When libraries embrace a few simple changes and start thinking outside the box when it comes to their employees, their resources, and their mission statements, they begin to meet the challenges that the digital age presents, and will continue to thrive in the new world presented by electronic materials, while remaining true to their spirit of information exchange and knowledge sharing.
Embedded Implementation of HDR Image Algorithm

In the autonomous vehicle, an image taken by the vehicle can’t be exposed properly due to vehicle movements and road constraints. Fortunately, an efficient and accurate system of multiple exposure fusion technique for creating images of high dynamic-range (HDR image) has come to solve this problem and provide a technical average to recover the lost information and add it via specialized software processing, but HDR has many disadvantage which include high calculation and increase in operational time. In this paper, the algorithm of HDR image is described and implemented in embedded platform to identify the more complex functions and aspect from a profiling analysis of the HDR software implementation.
PERBAIKAN MODEL SEASONAL ARIMA DENGAN METODE ENSEMBLE KALMAN FILTER PADA HASIL PREDIKSI CURAH HUJAN

Forecasting is a time series analytic that used to find out upcoming improvement in the next event using past events as a reference. One of the forecasting models that can be used to predict a time series is Kalman Filter method. The modification of the estimation method of Kalman Filter is Ensemble Kalman Filter (EnKF). This research aims to find the result of EnKF algorithm implementation on SARIMA model. To start with, preticipation forecast data is changed in the form of SARIMA model to obtain some SARIMA model candidates. Next, this best model of SARIMA applied to Kalman Filter models. After Kalman Filter models created, forecasting could be done by applying pass rainfall data to the models. It can be used to predict rainfall intensity for next year. The quality of this forecasting can be assessed by looking at MAPE’s value and RMSE’s value. This research shows that enkf method relative can fix sarima method’s model, proved by mape and rmse values which are smaller and indicate a more accurate prediction.&#x0D; Keywords: Ensemble Kalman Filter, Forecast, SARIMA
Software for Integral Security

This paper presents a software that can encrypt and decrypt files or encrypt character sequences. It could be used in a source code in order to secure it from being accessed by the third parties, messages, photos, video files, voice messages, documents, status updates and calls. The program is free, portable, tiny, and consists of a single exe file. The main advantage of the software, compared to other software currently available on the market, is that it encrypts character sequences and generates a code that can be used in a source code. The program developed by our team is an efficient and easy-to-use encryption and decrypt one.
The Effect of Plyometrics Exercise to Increase the Ability of Rebound

The problem expressed in this research is still low explosive power of his leg muscle, and the effect of lower leg muscle explosive power is impacting the ability of the athlete to rebound the basketball club.The purpose of this study was to determine whether there is any effect on the ability plyometrics exercises rebound IKABAS club basketball athletes.This type of research is a kind of quasi-experimental research, which is forecast to information obtained by actual experiment in a state that does not allow to control and manipulate all relevant variables.Moreover, the population in this study were athletes from basketball club IKABAS, and the population sampled in this study, which numbered 20 people.In the sampling, the researchers used purposive sampling techniques that determine the sample with specific considerations that are thought to provide the data to the fullest.Based on the research that has been done, there is the influence of plyometrics exercises to increase the ability of Rebound that of the average gained 119.56 at 122.45 pre-tests be obtained at post-test (t = 4.59> t table = 1.729).
A Case Study of AR Technology and Engineering Students: Is There a Gender Gap?

Determining the factors influencing students’ intention to use Augmented Reality (AR) allows a deeper understanding on how students react to the use of such technologies in their training as engineers. This study aims to identify the emotional and cognitive factors that influence the students’ intention of using AR in their future professional life and to access possible gender differences. A group of about 150 undergraduate students from an Engineering and Industrial Management program had the opportunity to explore AR applications related to contents addressed in Sensors and Actuators course. A survey was designed and used with those students. Principal component analysis resulted in three components named interest, ease of use and attitude. Logistic regression analysis was conducted with these three components together with gender, as predictors of intention of using AR in later professional life. Attitude turned out to be the strongest predictor. This analysis has also shown that gender has no significant effect.