Reviewerly/exHarmony huggingface dataset:
The Integration of Modern Information Technology and College English Teaching to Improve the Effectiveness of English Teaching

The introduction of multimedia assisted teaching into the English classroom has undoubtedly injected new vitality into the reform of English subject teaching.The expression form of both sound and image is perfect, and the activity and simulations are both appropriate.However, in the application of multimedia in English teaching, there are many problems.In this paper, teachers' application of multimedia, utilization and development of media resources are discussed.Proposed ways to solve such problems by using modern technology of informatization combined with traditional teaching methods are presented here.The main emphasis placed on the method is to incorporate modern information technology into teaching design.Compared with the traditional teaching methods, the new teaching mode can bring a new life to the college English class.And effective strategies to deal with such problems are also illustrated with stress on the main body of students, rich cultural background, situational establishment and diverse forms of homework.The abundant teaching content strengthened by modern teaching means, thus brings the English classroom person brand-new realm.
KELAYAKAN BUKU TEKS BAHASA INDONESIA TERBITAN BALITBANG KEMDIKBUD DENGAN KURIKULUM 2013 REVISI 2016

Media and learning resources are the core components of teaching activities. Learning resources is related to textbooks as learning materials for students and teachers to provide additional knowledge. This study aims to see the feasibility of buku teks Bahasa Indonesia Class VII published by Balitbang Kemdikbud with the kurikulum 2013 revision 2016, which is to determine whether the book is really qualified so worthy to be used as a source of learning. The feasibility is reviewed in terms of content and language . From the analsys the aspect of content, the material is in accordance with KI and KD in the curriculum 2013. While viewed from the aspect of language, textbooks Indonesian Class VII Curriculum 2013 Revision 2016 is good and not complicated language, careful choice of words, language which is used easily understood, but there is a vocabulary that does not conform to the general rules of Ejaan Bahasa Indonesia.
Implementasi Algoritma AES Untuk Pengamanan Login Dan Data Customer Pada E-Commerce Berbasis Web

In the field of E-Commerce website security is very necessary considering that many cyber crimes target commercial websites. Some that need to be secured are logins, because logins are confidential and important to access a website that has account access rights. In addition, customer data also needs to be secured data from people who do not have access rights, to prevent and avoid data changes and destruction. If customer data is known by parties who do not have access rights, the data can be misused and can lead to fraud cases. Therefore an algorithm is needed to secure the website. One of the algorithms that can be used to secure a website is cryptography. In cryptography messages or important and confidential data are encrypted and described using a symmetric key or asymmetric key that is only known by the authorities. There are several methods of cryptographic algorithm that can be applied to it, one of them is the AES (Advanced Encryption Standard) method. AES algorithm has a block length of 128 bits and is able to support key lengths of 128, 192, and 256bit, besides that AES algorithm is cheaper in cost and more easily implemented in small memory. The results of the application of the AES Algorithm make the website safer and it is hoped that customers will increase with increased website security and customer trust
A Tracking Method Based on Target Classification and Recognition

To solve the precise tracking problem of unknown targets in the air, the method based on target classification and recognition is to select different motion models for different type of targets. In this paper, there are three unknown air targets: fighters, civil aircraft and helicopters. Firstly, different accurate motion models are designed for these three targets. Bayesian inference is used to classify and identify the target with the target state and signal. With the classification result, the suited motion model is selected for tracking. The result shows that compared with the standard Interacting Multiple Model (IMM) algorithm, this method can reduce the error of target tracking by about 10%. It shows that making accurate models for the target motion can effectively improve the tracking accuracy of the unknown target with the accurate classification result.
The Intelligent Recognition of the Disaster Damage of Distribution Network Detected by UAV

Unmanned Aerial Vehicle (UAV) is used to reduce the difficulty in the collection of the disaster damage information of distribution network. However, the efficiency still needs to be improved because the judgment of disaster damage by human is inefficient and fallible. So intelligent recognition method is considered to increase the efficiency and accuracy. For the training of intelligent recognition model based on deep learning network requires a large number of samples, a novel method is proposed in this paper. The Self Organizing Map (SOM) is introduced between the convolutional neural network (CNN) and support vector machine (SVM), to reduce the number of training samples. Meanwhile, median filtering detection with Gaussian distribution is taken in the convolutional layer to decrease the influence of stochastic noise in the input picture. The dropout technique and multi winning neurons are introduced in the SOM to improve the clustering robust and reduce time consumption. Experimental results show that the proposed method shows good performance on the recognition of the damage images of the wire poles with complex backgrounds in the distribution network, and it is obviously superior to the traditional ways.
STA/LTA trigger algorithm implementation on a seismological dataset using Hadoop MapReduce

&lt;span&gt;In this work we implemented STA/LTA trigger algorithm, which is widely used in seismic detection, using Hadoop MapReduce. This&lt;br /&gt;implementation allows to ﬁnd out how effective it is in this type of tasks as well as to accelerate the detection process by reducing the processing time. We tested our implementation on a seismological dataset of 14 broadband seismic stations and compare it with the traditional one. The results show that MapReduce decreased the processing time by 34% compared to the traditional implementation.&lt;/span&gt;
Toward Easy Deposit: Lowering the Barriers of Green Open Access with Data Integration and Automation

This article describes the design and development of an interoperable application that supports green open access with long-term sustainability and improved user experience of article deposit. The lack of library resources and the unfriendly repository user interface are two significant barriers that hinder green open access. Tasked to implement the open access mandate, librarians at an American research university developed a comprehensive system called Easy Deposit 2 to automate the support workflow of green open access. Easy Deposit 2 is a web application that is able to harvest new publications, to source manuscripts on behalf of the library, and to facilitate self-archiving to a university’s institutional repository. The article deposit rate increased from 7.40% to 25.60% with the launch of Easy Deposit 2. The results show that a computer system can implement routine tasks to support green open access with success. Recent developments in digital repository provide new opportunities for innovation, such as Easy Deposit 2, in supporting open access. Academic librarians are vital in promoting “openness” in scholarly communication, such as transparency and diversity in the sharing of publication data.
Regional Seismic Networks Operating along the West Coast of the United States of America

Abstract The Pacific coast of the contiguous United States hosts the highest seismic risk in the country due to the intersection of high-seismic hazard and the high densities of population and infrastructure. The regional seismic networks in Washington, Oregon, Nevada, and California have operated for many years and have collected long catalogs and large amounts of seismic waveform data in a variety of formats, including digital records. These data are available for engineering purposes and research into earthquakes, other natural and man-made seismic sources, and the Earth’s structure. The West Coast networks are closely coordinating as they embark on the implementation of West Coast ShakeAlert, an earthquake early warning system.
Introduction

This special issue moves beyond an understanding of diagrams as mere inscriptions of objects and processes, proposing instead to re-evaluate diagrammatic reasoning as the work that is carried out with, on, and beyond diagrams. The introduction presents this issue’s focus on ‘working with diagrams’ in a way that goes beyond semiotic, cognitive, epistemic, or symbolic readings of diagrams. It discusses recent research on diagrams and diagrammatic reasoning across disciplines and approaches diagrams as suspended between imagination and perception—as objects with which work is done and as objects that do work. Contributions to this issue probe diagrams for the work they do in the development of disciplinary theories, investigate their reworking of questions of time and scale, and ask how some diagrams work across fields and disciplines. Other authors shift the perspective to their own work with diagrams, reflecting on the practice and performative nature of diagrammatic reasoning in their respective fields and disciplines.
Software Development Website Inventaris Pada Pusat Perbelanjaan XYZ

Currently the development of information technology is developing fast and demands to create an application that can solve an existing problem but has not found a solution. In the process of knowing the amount of stock, sometimes it will be difficult if you have to check manually. The application in XYZ shopping center warehouse is based on the need for accurate and accurate information management in managing the status of goods in the warehouse. The methodology used is the waterfall method, this model describes the development of software such as the flow of a waterfall, starting analysis requirements as the beginning of the process up to coding and testing at the end of the process. This application was designed using the SDLC method, by applying the Waterfall model and built using web programming languages, namely WordPress and MySQL for data storage. The result of the application is that the system can display the status of goods and provide the latest reports needed by the car shop manager
Tool-Supported Requirement Engineering: A Categorization of the State of the Art and Research Trends

The multiple activities that are part of Requirements Engineering (RE) are benefited from the support of software tools that assists the application of diverse techniques. Given the importance of this phase in software development and specifically in product safety, this article aims to compile the current status of software tools used in the different RE activities to direct future development efforts in this type of tools and identify the activities of the RE, the implemented techniques and which tools focus on the requirements associated with safety. We used systematic literature review method for reviewing the literature on RE tools. We used automatic search strategies for searching the relevant papers published between 1 January 2013 and 31 December 2018 and Meta-aggregation to synthesize the data extracted from the primary studies and to obtain a categorization of findings. We selected 50 articles, 33 conference and 17 journal papers, from the initially retrieved 10,191 articles. The results from the data analysis 248 findings enabled us to classify RE tools in 15 categories and 29 subcategories. This review has enabled us to identify the following areas for further research in software tool assisted RE: 1) is necessary to perform more research on the development of RE tools that specialize in software requirements associated to a quality attribute of interest as safety; 2) it is important to development RE tools activities different areas from requirement specification, validation and management; 3) in is important to conduct evaluation the RE tools founds in non-academic environment.
Sign Language Recognition Based on EMG Signals through a Hibrid Intelligent System

Non-verbal communication is an important part of everyday interactions and human-computer interaction.Vision techniques and instrumented gloves for sign language recognition are commonly used, but these are often expensive and considered invasive to the user.This research proposes the recognition of words from the American Sign Language (ASL) using the SCEPTRE database acquired by two Myoelectrical bracelets.Computational intelligence techniques were used to optimize the number of attributes using Principal Component Analysis (PCA) and a classifier based on Neural Networks (NN).The results suggest that it is possible to reduce the attributes using PCA without significantly losing the quality in classification.This allows faster processing, a convenient feature for classifiers for real-time SL recognition.
Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud

Cloud computing is a growing service computing trend that offers users a range of on-demand variety of services from applications, processing capability, and storage based on the concept of the "Pay-As-Per-Use" model.Organizations from every sector are now realizing the benefits offered by cloud computing technology and moving towards the cloud.Cloud computing offers numerous advantages over conventional computing.However, it still faces a few challenges such as resource utilization in a cloud data centre and quality of service to the end-users due to improper workload balance among available resources.Heterogeneous cloud resources also impact cloud systems overall performance.We proposed an enhanced load balancing algorithm in this research paper for efficient VM allocation in a heterogeneous cloud.Our proposed algorithm allocates independent user tasks or requests to available virtual machines in cloud datacentre efficiently to manage proper load balancing.This algorithm is aimed at minimizing user request response time and the time required for data centre processing.The results obtained showed a significant reduction in user request response time and data centre processing time as compared to "Throttled" and "Round Robin (RR)" algorithms.
Effective Target Extraction of Automatic Target-Scoring System

We analyze the edge information of the input image, and then extract the maximum communication area as mask to reduce the calculations. Thirdly, we can get effective target extraction according to the feature of target area's four sides. After using the perspective transform, the area can be converted to a standard rectangular. Finally, the flood fill algorithm can remove the interference information that may exist on the target area. After the experiment, this algorithm works well and the counters of the target area are clear.
The Effect of Gender Diversity on Company Financial Performance

This study aims to examine the effect of gender diversity on the board of commissioners and board of directors on the financial performance of non-financial companies listed on the Indonesia Stock Exchange over the period of 2013-2017.The analytical method used was multiple linear regressions with the classical assumption test.As a result, the presence of women on the board of directors and board of commissioners measured by dummy and the proportion of women councils did not affect financial performance as measured by return on asset and return on equity.Meanwhile, control variables of company size and leverage are proven to influence financial performance significantly.
An anomaly detection algorithm based on K-means and BP neural network in wireless sensor networks

In recent years, with the development of wireless sensor networks(WSN), it has been applied in more and more areas. However, anomaly detection has been always the hot topic in WSN. In order to solve the above problem, this paper proposes an anomaly detection algorithm which is based on the K-means clustering and BP neural network algorithms. This algorithm firstly employs the K-means clustering algorithm classify and mark the collected original sample data as anomaly and normal. Based on the above tagged data, it then uses the BP neural network algorithm train the classification model and realize the on-line detection of anomaly data. Finally, relevant experiments on virtual and actual sensor databases show that our algorithm can achieve a high outlier detection rate while the false alarm rate is low. In addition, because K-means clustering algorithm is an unsupervised classification method, our algorithm is suitable for different WSN applications scene.
High-quality face image super-resolution based on Generative Adversarial Networks

Face image super-resolution has received increasing attention. However, since the face has a lot of fine textures, it is very difficult to rebuild for large upscaling factors. We propose a new method for face image SR, using residul dense block(RDB) as the basic unit and the Inception architecture is combined in the low layers. We use the relativistic GAN and the improved perceptual loss defined by the features before activation.For the large scaling factors, our GAN is progressive both in architecture and training. The network proposed achieves excellent performance in the reconstruction of low-resolution face images, especially under large scaling factors such as 4x and 8x.
Reconstructing Scanned Documents for Full-Text Indexing to Empower Digital Library Services

The digital era raises new challenges for traditional library services in which information has to be delivered and supported by technology-enhanced systems. The increasing need for rapid access to information requires librarians to re-evaluate the way they develop, manage and deliver resources, as well as services. However, most information extraction systems are not designed to work with PDF files generated after Optical Character Recognition, and several problems are encountered while trying to properly restructure the recognized text, for example: disruption of paragraphs, improper page breaks, or loss of content structure. This paper introduces a pre-processing pipeline designed to support university libraries to adequately index old document collections. The extracted text is indexed into Elasticsearch which facilitates the search for relevant documents, based on keywords. The information extraction system is designed to assist librarians in the digitization process by enabling a systematic review of documents, which leads to more accurate representations of the indexed files.
Implementation of Intelligent Question Answering System Based on Basketball Knowledge Graph

Currently most search engines query based on keywords or question-template matching. But for the retrieval about basketball or NBA, there are always too many feedback results, low accuracy and lack of intelligence. In this paper, an intelligent question answering system based on NBA basketball knowledge graph is implemented. Some methods are used in the question analysis module in the system, including question similarity calculation, named entity recognition, entity similarity calculation, and question-to-entity attribute mapping. In answer generation module of the system, a multi-strategy answer generation method is proposed. The experimental results show that our approach by combined with natural language processing technology and domain knowledge graph can well identify the input questions by user, and accurately feedback the answers to user queries.
The Effectiveness of Pre-Reading Activities on Gifted Students: A Case on Low Achievement Students

The main purpose of this study was to find out the effectiveness of using Pre-reading activities on students’ reading comprehension and their preference of Pre-reading activities. There were 55 first year college-aged students participating this study. The first experimental group was treated using Pre Reading Questioning, while the second experimental group using Pre Reading Vocabulary List. The primary data were collected from the students’ post-test, while the supporting data was gained through questionnaire. The primary data were collected, then analyzed by using independent t-test formula. The result of the t-test analysis was 0.041 (0.041&amp;lt;0.05), which means students who were taught using Pre-reading Questioning outperformed those who were taught using Pre-reading Vocabulary List in reading comprehension. From the questionnaire, Pre-reading Questioning gained higher preference from Pre-reading Vocabulary List. Pre-reading Questioning can make the students understand the text better and give them opportunity to practice thinking and analyzing than Pre-reading Vocabulary List. It means that applying Pre-reading Questioning is an effective way to teach reading comprehension.
Correction to: Groundwater geochemical facie: implications of rock-water interaction at the Chamba city (HP), northwest Himalaya, India

The correct presentation of Eq. 10 is shown in this paper.
An EM algorithm for singular Gaussian mixture models

In this paper, we introduce finite mixture models with singular multivariate normal components. These models are useful when the observed data involves collinearities, that is when the covariance matrices are singular. They are also useful when the covariance matrices are ill-conditioned. In the latter case, the classical approaches may lead to numerical instabilities and give inaccurate estimations. Hence, an extension of the Expectation Maximization algorithm, with complete proof, is proposed to derive the maximum likelihood estimators and cluster the data instances for mixtures of singular multivariate normal distributions. The accuracy of the proposed algorithm is then demonstrated on the grounds of several numerical experiments. Finally, we discuss the application of the proposed distribution to financial asset returns modeling and portfolio selection.
Faktor-Faktor Yang Mempengaruhi Kemampuan Membaca Siswa (Studi Kasus Di SDN 105 Pekanbaru)

Reading is very important for life because reading can make us know all the information we need. Students are able to read not by chance or by inspiration, but because they are taught. Reading is not a natural activity, but a set of components that are mastered privately and gradually, which are then integrated and become automatic. However, at present there are still problems with reading in primary schools, namely the lack of students' reading skills in high grades. This research is motivated by the low reading ability of fifth grade students at SDN 105 Pekanbaru. There are 2 students who became the object of this study because of their lack of reading ability compared to their other friends. In connection with these problems, researchers conducted a study of fifth grade students at Pekanbaru Elementary School 105 with the aim of knowing what factors influenced students' reading ability. This type of research is descriptive qualitative with the method used is the survey and observation methods. Data collection conducted in this study was by interview and documentation as supporting material. Based on the results of data analysis obtained from the triangulation of data collection observations and interviews conducted by researchers of this study concluded that the factors affecting reading ability of students in SD Negeri 105 Pekanbaru are intellectual factors and psychological factors.