Reviewerly/exHarmony huggingface dataset:
The Use of Social Media in Designing The Writing Assessment for EFL Students

This research investigates social media as the assessment for EFL students at State University of Medan (UNIMED) of second semester English educational program. This research focuses on how social media influences the students’ competences in writing skills. The problems are how it influences their ability to develop their ideas in writing; develop the reading &amp; writing materials; change the students’ opinion on social media in writing class. Questionnaire was designed to get the students’ thought on social media matter, their intention of social media for study, particularly in reading and writing topic and the influence of social media on their ability in developing the idea in writing class. The instruments used in this research were social media as Facebook, WhatsApp and Instagram as a text in reading &amp; writing class. The population consisted of 80 students from two classes of English educational program. Data was analyzed using Research &amp; Development Method (R&amp;D) and the Linkert Scale to get the percentage of students’ perception. The findings of this paper indicate that students more enjoy and engage in writing class using social media as their assessments; using social media to develop ideas on a topic; and being more creative in writing skills. However, control and development are still needed on this topic.
Protection of Ultrasound Image Sequence: Employing Motion Vector Reversible Watermarking

In healthcare information systems, medical data is very important for diagnosis.Most of the health institutions store their patients' data on third-party servers.Therefore, its security is very important, since the advent of advanced multimedia and communication technology, whereby digital contents are manipulated, copied, and duplicated without leaving any trace.In this paper, a reversible watermarking technique is applied to the patients' data (ultrasound image sequence).Since the traditional watermarking schemes can experience some permanent distortions that are not acceptable in the medical application.Thus, a reversible watermarking technique has been used, which can not only secure the ultrasound image sequence but also restore the original sequence back.For watermark embedding, the magnitude and phase angles of motion vectors of the image sequence are used that are obtained by using Full-Search block-based motion estimation algorithm.Before applying the motion estimation algorithm and watermark embedding, the histogram pre-processing is performed to avoid underflow/overflow.Unlike other state-of-the-art watermarking schemes that are reported in the last decades, the experimental results show that the proposed algorithm is simple, provides a much larger embedding capacity and better quality of the watermarked image sequence.
Global and Local Characterization of Rock Classification by Gabor and DCT Filters with a Color Texture Descriptor

In the automatic classification of colored natural textures, the idea of proposing methods that reflect human perception arouses the enthusiasm of researchers in the field of image processing and computer vision.Therefore, the color space and the methods of analysis of color and texture, must be discriminating to correspond to the human vision.Rock images are a typical example of natural images and their analysis is of major importance in the rock industry.In this paper, we combine the statistical (Local Binary Pattern (LBP) with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion) and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptors named respectively Gabor Adjacent Local Binary Pattern Color Space Fusion (G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion (D-ALBPCSF) for the extraction of visual textural and colorimetric features from direct view images of rocks.The textural images from the two G-ALBPCSF and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2 and the intersection of histograms that we have adapted to color histograms.The results obtained allowed us to highlight the discrimination of the rock classes.The proposed extraction method provides better classification results for various direct view rock texture images.Then it is validated by a confusion matrix giving a low error rate of 0.8% of classification.
Object Detection Algorithm Based on Multiheaded Attention

This study proposes a multiheaded object detection algorithm referred to as MANet. The main purpose of the study is to integrate feature layers of different scales based on the attention mechanism and to enhance contextual connections. To achieve this, we first replaced the feed-forward base network of the single-shot detector with the ResNet–101 (inspired by the Deconvolutional Single-Shot Detector) and then applied linear interpolation and the attention mechanism. The information of the feature layers at different scales was fused to improve the accuracy of target detection. The primary contributions of this study are the propositions of (a) a fusion attention mechanism, and (b) a multiheaded attention fusion method. Our final MANet detector model effectively unifies the feature information among the feature layers at different scales, thus enabling it to detect objects with different sizes and with higher precision. We used the 512 × 512 input MANet (the backbone is ResNet–101) to obtain a mean accuracy of 82.7% based on the PASCAL visual object class 2007 test. These results demonstrated that our proposed method yielded better accuracy than those provided by the conventional Single-shot detector (SSD) and other advanced detectors.
Teaching the Basic Commands of NoSQL Databases Using Neo4j in Vocational Education and Training (VET)

This paper presents a new pedagogical approach to the teaching of the Databases course in a Vocational Education and Training (VET) environment. We propose an exemplary teaching scenario for basic database commands using Neo4j. This approach is necessitated by the fact that many online/network environments are taught in VET without any special preparation in the corresponding laboratories. Furthermore, our article describes various technical aspects of the NoSQL software family in general and of Neo4j in particular.
Students’ Satisfaction Index on Chemistry Learning Process

This study was aimed at describing the index of studentsâ€™ satisfaction in the chemistry learning process in SMAN 4 Singaraja Bali Indonesia and the factors that influenced it. For that, the survey research was conducted. The population was all students of the tenth and eleventh-grade students of groups of mathematics and natural sciences in academic year 2016/2017 in SMAN 4 Singaraja Bali consisting of 478 people. All population members became samples. Data were collected using a questionnaire. The numbers of respondents which returned the questionnaire were 431 people. Data the were analyzed by descriptive statistics. The results of the study showed that the index of studentsâ€™ satisfaction viewed from a dimension of tangible, reliability, responsiveness, assurance, and empathy was 86.24%, 85.67%, 87.42%, 88.11%, and 85.18%, respectively, and all dimensions were quite high. Overall the index of studentsâ€™ satisfaction in the chemistry teaching and learning process was 86.19% and it was high.
User Personalization with KNN for Recommender System

Following the increase in of the information available on the Web, it is important to diversity of its users and the complexity of Web applications. One web application that has a diversity of users is a news website. Customizing a website with the characteristics of each user is called personalization. The purpose of this study is to study the methods used in giving news recommendations using user personalization. Collaborative filtering method (CF) is one method that groups users based on the nature of the user. This CF method can be applied using the k-nearest neighbor (KNN) algorithm. The proximity between users in this algorithm is sought using the Pearson correlation technique and cosine correlation. The best technique by considering the smallest value of prediction error evaluation will be applied to giving recommendations. Evaluation of these errors was tested by applying the formula Root Mean Square Error. The best evaluation results obtained in this study are the k-nearest neighbor algorithm with cosine correlation similiarity.
School improvement plans, a tool to improve the quality of education

Much has been said about school improvement plans (SIPs), which have been implemented in many countries, with different characteristics and with different results in each of them. The purpose of this article is to demonstrate, through the research carried out, the impact of SIPs on studentsâ€™ learning and, from that perspective, to determine what characteristics the research should have in order to have a direct impact on studentsâ€™ learning. To this end, an exhaustive bibliographic review will be carried out to show the effects of the improvement plans in the communities, counties or countries that have implemented them. The result of the above will be to determine, according to the few existing investigations, the elements that SIPs must have in order to have an impact on studentâ€™s learning.&#x0D; Keywords: School improvement plans, improving the quality of education, educational improvement, accountability.
Injecting Differential Privacy in Rules Extraction of Rough Set

Data mining plays a very important role in various database applications. Medical data mining has been a popular data mining application with a vital role in improving the quality of medical services and promoting the development of the medical industry. There has been extensive research in rough set theory (RST) to mine potential patterns in medical data, which has important implications for clinical decision support and online medical diagnosis. Although medical data mining is very promising, the rapid development of this field still faces many challenges, such as information security and privacy issues. Under the assumption that data miners cannot be trusted, this paper combines the differential privacy and rough set rules’ extraction for the first time and proposes a new method to mine hidden patterns in medical data and ensure patient privacy. This algorithm uses the Laplacian mechanism to add noise to the credibility in the process of data mining while maximizing the utility of the data. Experiments show that our algorithm can effectively preserve the accuracy of data while protecting patient privacy.
Old film image enhancements based on sub-pixel convolutional network algorithm

Due to the underdeveloped scanning technology, some old movie films are scanned in digital format with lower resolution, which does not meet the viewing needs of contemporary viewers. Therefore, it is necessary to superresolution processing them to improve the image quality. However, some old movies will appear blurred after scanning. In this case, the existing algorithm super-resolution reconstruction results are often not ideal. This paper adds image deblurring pre-processing before the super-resolution processing. First, the old movie is deblurred according to the deblurring generation training model against the network, and then the image is super-resolution processed by the sub-pixel convolution network. The method aims to improve the problem that the repair effect caused by the image blur caused by the old film in the super-resolution reconstruction is not ideal.
THE ANALYSIS OF REPETITION AS PART OF LEXICAL COHESION IN TALK SHOWS

The aim of this paper is to define the lexical cohesion in the dialogue. The focus of this paper is the repetition that happens frequently in the dialogue. The dialogue is taken from the talk shows downloaded from youtube. The analysis is given based on the data from the dialogue of the speakers and draw the conclusion. This study brings benefits in building the perception of the audiences. From the data that has been analyzed, it has been found that the speakers tend to repeat the word, phrase, or sentence because they want to strengthen their opinion, eventhough from the sentence structure the repetition is not good and eficient. The result of the study also show that there are some reasons why do the speakers repeat their word. phrase or sentence such as to strengthen their opinion.
Promoting smart homes

Over the past decade, there has been increasing interest in using ‘smart’ technologies: to can gather information about a building and its inhabitants, and automatically make decisions about which appliances and services should be powered. Promoting smart homes is frequently seen as a means of reducing energy demand. However, this chapter shows that smart home technologies are also positioned and sold as means of enhancing convenience or creating a cosy atmosphere. In increasing expectations of service, and in relying on energy-intensive infrastructures like network technology and data centres, smart home technologies might not reduce demand at all.
The Impact of Digital Currency on the Financial System: Universal Decentralized Digital Currency, Is It Possible?

With the continuous development of computer and blockchain technology, digital currency has gradually replaced some functions of legal tender. This paper investigated the impact and the feasibility of digital currency on the financial market. Combining the money demand theory of Karl Heinrich Marx and Milton Friedman respectively, we discussed the impact of electronic and cryptocurrencies on the amount of money in circulation. Then, through further empirical analysis, we conclude that in China, digital currency has a substitution effect on current deposits in the long term. Furthermore, the welfare effect level of different countries adopting different policies on digital currency is analyzed by using the local equilibrium model of tariff effect in small countries, and the policy choice of maximizing the total welfare level is discussed based on game theory. Finally, we put forward some suggestions on establishing the global financial supervision system.
Data-Driven Control of Ground-Granulated Blast-Furnace Slag Production Based on IOEM-ELM

An improved online error minimized- extreme learning machine (IOEM-ELM) adaptive control method is proposed by introducing the adding and pruning mechanism of hidden nodes to realize the control of a kind of MIMO system with multiple operating modes. The strategy to handle the multiple-operating-modes problem is analyzed by the idea of multiple model adaptive control. Further, considering that the ground-granulated blast-furnace slag (GGBS) production process is a complex system with the characteristics of multiple operating modes, high nonlinearity, strong coupling, and high uncertainty, a data-driven intelligent control scheme is designed based on the proposed IOEM-ELM neural network. By analyzing the numerous production data produced in normal and abnormal situations, three typical operating modes are extracted to fully depict the actual production process as a testing platform. As the network structure adjusts dynamically using the IOEM-ELM method, model, and controller are designed to deal with the GGBS production process operating among multiple modes. The example shows that the proposed method can handle changing modes, and reduce the computation of GGBS production process effectively.
PERANCANGAN VIRTUAL REALITY DALAM MENGETAHUI GEJALA ACROPHOBIA

The most common phobia in the entire world is Acrophobia (fearing heights), there are about 23% of the population stating that they are afraid of heights. Acrophobia sufferers will always look anxious and in a panic where the closest people need to be around to calm down. Excessive fear of anything is generally not good because it can hinder daily activities.&#x0D; So from that it is necessary to know the symptoms of acrophobia sufferers for the surrounding community to be able to take action to calm sufferers when sufferers of acrophobia relapse. The results of this study are expected to create a virtual reality-based software prototype to help people better recognize the symptoms of acrophobia sufferers. Researchers sincerely hope that the results of this study more or less provide benefits specifically for researchers and generally for all communities. This study is using the Multimedia Development Life Cycle (MDLC) method in software development
Efficient Mining of Association Rules based on Clustering from Distributed Data

Data analysis techniques need to be improved to allow the processing of data.One of the most commonly used techniques is the Association Rule Mining.These rules are used to detect facts that often occur together within a dataset.Unfortunately, existing methods generate a large number of association rules, without accentuation on the relevance and utility of these rules, and hence, complicating the results interpretation task.In this paper, we propose a new approach for mining association rules with an emphasis on easiness of assimilation and exploitation of the carried knowledge.Our approach addresses these shortcomings, while efficiently and intelligently minimizing the rules size.In fact, we propose to optimize the size of the extraction contexts taking advantages of the Clustering techniques.We then extract frequent itemsets and rules in the form of Meta-itemsets and Meta-rules, respectively.Experiments on benchmarking datasets show that our approach leads to a significant reduction of the number of generated rules thereby speeding up the execution time.
Introducing a Personal Semantics in the User Profile Modeling for a Personalized Information Retrieval

The objective of personalized information retrieval systems is to infer the real user information need hidden behind his query, and then tailor the search results to this need.The user information need may be relative to one of the user's multiple search contexts, such as user current location, user social context or an important event that occupies the public opinion of the user community.In this paper we present a new personalized information retrieval approach in which we design and use a user profile.This profile takes into account the different social, situational and temporal contexts of the user previous searches, and we introduced a personal semantics in the representation of these search contexts.The introduced personal semantics is interested by the identification and the valorization of the different cooccurrence relationships between the different relevant components of the user search history.The user profile, boasting its semantic vision, is used to infer the user current information need by the enrichment of the initial user query without going beyond the user current interest.
Secure Outsourcing in Discrete-Logarithm-Based and Pairing-Based Cryptography (Invited Talk)

Cryptographic operations are performed everywhere, from standard laptop to smart cards. Some devices computational resources can be very limited and it is natural to delegate costly operations to another device capable of carrying out cryptographic algorithms. In this setting, it is obviously important to ensure the limited device that the computation is carried out correctly and that the powerful device does not learn anything about what is actually computing (including the secret inputs and outputs). We briefly review the recent advances on secure outsourcing of group exponentiation (in groups of known prime order as well as in groups of unknown order) and pairing computation.
A New Data Preparation Methodology in Machine Learning-based Haze Removal Algorithms

Haze removal is an intellectually challenging object of scientific study. There is a myriad of methods has been proposed hitherto, ranging from histogram-based, contrast-based to machine learning-based. Haze removal approaches founded upon machine learning require a large and reliable training database. Researchers are currently using the synthetic database due to the complexity of real database acquisition. To introduce the synthetic haze into the clear images, they assume that the depth map is drawn from the standard uniform distribution. In this paper, we present a new methodology for preparing the synthetic training database, in which the proposed equidistribution is employed instead of standard uniform distribution. The effectiveness of the proposed method has been verified by a large number of experiments.
Comparative Analysis of Cow Disease Diagnosis Expert System using Bayesian Network and Dempster-Shafer Method

Livestock is a source of animal protein that contains essential acids that improve human intelligence and health.Popular livestock in Indonesia is cow.Consumption of meat per capita is increased by 0.1% kg / capita / year.The high demand for beef in Indonesia is due to the increasing of population in Indonesia by 1.49% per year.More than 90% of cows are reared by rural communities with less of knowledge about livestock and have low economic capabilities.In addition, the number of experts or veterinarians are also limited.One of the solutions that can be done to socialize the knowledge of experts or veterinarians is by using expert system.Some methods that can be used in expert systems are Bayesian network and Dempster-Shafer method.The purpose of this research is to analyze the comparison of cow disease diagnosis with bayesian network and Dempster-Shafer method.In order to know which method is better in diagnosing cow disease.The data used is 21 cow diseases with 77 symptoms.Each method is tested with the same 10 cases.The conclusions obtained by Bayesian network and Dempster-Shafer method.Both of methods give the same diagnosis results but with different percentage.The mean value of diagnosis percentage by Dempster-Shafer method is 87,2% while bayesian network method is 75,3%.Thus, it can be said that the Dempster-Shafer method is better at diagnosing cow disease.
POTENSI TINGGALAN ARKEOLOGI DAN PARIWISATA DI KEPULAUAN SANGIHE, PROVINSI SULAWESI UTARA (THE POTENCY OF ARCHAEOLOGICAL REMAINS AND TOURISM IN ISLAND SANGIHE, NORTH SULAWESI PROVINCE)

As the frontier region of Indonesia, Sangihe presents archaeological resources that have not yet known by the public.Such circumstance is understandable since researchers who focus on culture rarely pay attention on archaeological remains discovered in the border regions.This paper describes the potential of archaeological remains in Kabupaten Kepulauan Sangihe on the northern region of Island Sulawesi that borders with the southern region of the Philippines.This research was conducted using an archaeological approach by examining material remains such as shipwrecks, ancient houses, tombs, and ancient ceramics, and supported by an ethnistorical approach emphasising on the study of ethnography and historical archives.This is an explorative research and data collection is carried out by diving in Sangihe waters.The results of this investigation are identifications and descriptions of archaeological remains in the Sangihe archipelago that suggest the region as the northern gate in the context of culture distribution into Nusantara, as well as the use of potential archaeological remains for tourism.Further, this present study is expected to be a reference for future projections.