Reviewerly/exHarmony huggingface dataset:
TEACHING SPEAKING AND YOUNG LEARNER: IN PERSPECTIVE OF COMMUNICATIVE LANGUAGE TEACHING

English is a need for this millennial era. As an official international language, English is spoken by people in international forum.  Noticing the importance of English, some countries have placed it in their primary curriculum school, even introduced it to young language learner. This paper discusses teaching speaking for young language learner by applying communicative approach. Teaching speaking for young language learner is not an easy activity. Teachers need more creativity, design enjoyable activity and present fun way for them. Young learners, especially in this millennial era should familiar with English. Mastering English since early age, especially speaking becomes a basic preparation for them to face globalization. It is worth to teach them English since young age, the more they age the more chance they have. To realize that hopes, this paper provides several communicative activities in teaching speaking for young language learner.
Response to: Mobile devices for educational support on clinical placements: Medical students’ perspective

"Response to: Mobile devices for educational support on clinical placements: Medical students’ perspective." Medical Teacher, 41(11), p. 1330
Study and Research on the Six-Year Process of Bitcoin Price and Return

The purpose of this study, is create a challenge and discussion concerning the existence of information about the Bitcoin price and return, which suggests the relationship of information and the strong performance it. The information trends are available at different time periods and the summary data related to the statistical descriptions for the price and return index are also discussed. In this paper we show a significant correlation between the price trend and return in the Bitcoin that has been confirmed by various statistical methodology. Using statistical tests and reviewing trends and relationships between the variables, planning can be done to invest in it and its performance or inefficiency can be tested. The results of this research shows a significant and positive relationship between the price and return of Bitcoin.
ИМИТАЦИОННАЯ МОДЕЛЬ ГРАФИЧЕСКОГО МУЛЬТИПРОЦЕССОРА НА ОСНОВЕ ТЕОРИИ СЕТЕЙ ПЕТРИ

This work addresses the problem of creating the simulation models of a graphical multiprocessor for carrying out computational experiments to determine the efficiency of using the parallel computing based on GPGPU (General-purpose computing for graphics processing units, non-specialized computing on graphics processors) in tasks of structural-parametric synthesis of big discrete systems based on evolutionary procedures. We propose to use the Petri netstheory as a mathematical tool.It has the parallelism property and allows describing discrete processes occurring both in the genetic algorithm and in the computing system itself.The development of a simulation model is carried out on the basis of the graphic multiprocessor module memory architecture taking into account the specifics of its work related to the ability to read, write and transmit data.In addition, we describe the feature of the arithmetic logic devices work, which are able to simultaneously execute one command over a set of data.When building the model we take into account a feature of graphic multiprocessors which allows to get a greater effect from usingthe parallel computing avoiding the branching and control blocks operation that slow down the multiprocessor (since their number is less than the calculators number), thereby forming the “narrow” places.The proposed simulation model of a multiprocessor unit based on the selected tool is implemented using the specialized software for simulation based on the Petri nets theory – PIPE 5.This software is distributed free of charge and has a wide range of instrumental and analytical tools, which greatly simplifies both the modeling process and the process of analyzing the obtainedmodels.The resulting model will provide an opportunity to evaluate the efficiency of using parallel computing based on GPGPU technology in solving the task of improving the performance of intelligent information decision support systems based on genetic algorithms adapted to the subject area.
Combined with the residual and multi-scale method for Chinese thermal power system record text recognition

Aiming at the problem that the recognition accuracy based on convolutional neural network of thermal power system record text is not high, a method of thermal power system record text recognition based on residual and multi-scale feature combination was proposed and implemented. Combined with the residual, a new network is designed to replace the traditional convolutional neural network and improve the feature extraction ability of the network. The 1 ? 1 convolution core was used to increase the network depth and reduce the parameters instead of the 3 ? 3 convolution core. The network order of each layer in residual block was adjusted so that the network representation ability can be improved. Combining feature information of different scales and retaining more vertical feature information, the classification accuracy of the network is improved. Experiments on the self-built image data set of thermal power system records show that the proposed network model improves the accuracy by 11% compared with convolutional recurrent neural network, and has better robustness to image distortion and blurring.
The social studies learning outcomes in reciprocal teaching and direct instruction at primary teacher education student

the study examines the effect of reciprocal and direct instruction models on the learning outcomes at halu oleo university students. The results showes that the reciprocal instruction models was more effective in improving students social studies learning outcomes than direct learning. this study uses quasi-experiment with post-test control group design. the result of the study used descriptive and inferential analysis. The hypothesis testing use anova with LSD test. normality test using kolmogorov smirnov test and homogeneity test uses the levene test.
Poster: Attention-based Spatio-Temporal Model for HAR Using Multivariate Time Series

Recognizing human activities in a sequence is a challenging area of research in ubiquitous computing. Modeling spatiotemporal dynamics from multivariate time series plays a key role in recognizing human activities, unfortunately, the existing approaches could not extract spatial and temporal dynamics simultaneously. This poster presents ABST, a deep learning framework that extracts spatiotemporal dynamics for discriminating human activities. We demonstrate how to utilize two GRUs along two dimensions of inputs to automatically learn features. In addition, considering that each time step pays different levels of attention to the final prediction, on the topmost of framework, we apply an attention-gated recurrent layer. Experimental results show a promising recognition performance, and outperforms the state-of-the-art methods.
APPROACHING DYNAMIC TEACHING IN REALITY OF VOCATIONAL TRAINING ORGANIZATIONS WHE\\RE ETHNIC MINORITY STUDENTS STUDY IN VIETNAM

This theory is not new to vocational training in Vietnam, but the nature of Dynamic Teaching is not well understood and applied by all the teachers in teaching process. More specifically, for vocational training institutions with ethnic minority students studying, Vietnamese language is also considered as a foreign language for them to integrate their study with the majority of students of Kinh ethnic group. At the same time, they try to master Vietnamese to integrate their study and learn English, so they have to face with much more difficulties than students of Kinh ethnic group. They need far more teachers who have the most effective teaching methods to help them overcome the difficulties in acquiring knowledge. At school, they not only need knowledge and related professional skills, but they also expect teachers to be equipped essential soft skills so that after graduation they can confidently integrate into labor market to have a stable job.
A WEB BASED SYSTEM FOR ONLINE STUDENT TRANSACTION AMONG SULAIMANIYAH CITY SCHOOL APPLICATION

Nowadays, many schools provide a different level of education for their students. The objective is to change the place of the school where the students study and change it to another college in different place. However, it is obvious that in Kurdistan Iraqi region, any documentation related to education process will need to go through the Ministry of education, and this process takes a lot of time because of the distance between the offices in the city. Some paper works need to be done in order to accomplish any kind of task related to education processing including student transition among the schools and colleges either inside the city itself or in the city border such as towns and villages. It also takes a lot of time. This research tries to find out the newest methodology in Kurdistan cities that leads to provide a transformation of the old and classical system to computerized system, which can help the education sector over all the Kurdistan cities to become electronic. This can be done by providing online webpages to perform all these tasks. In addition, this kind of system will be considered as the first system for the Sulaimaniyah education center including all the schools in the cities. These changes may reduce the amount of possible traffic on the streets, as well as huge load on the organizations in education sector. Here student data can also be archived for the future use.&nbsp
An Efficient Complex Event Processing Algorithm based on Multipattern Sharing for Massive Manufacturing Event Streams

Quickly picking up some valuable information from massive manufacturing event stream usually faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of large volume, high velocity, many variety and small value.Aiming to solve the problem above for the current complex event processing methods because of not sharing detection during the detecting process for massive manufacturing event streams, an efficient complex event processing method based on multipattern sharing is presented in this paper.The achievement of this paper lies that a multipattern sharing technology is successfully used to realize the quick detection of complex event for massive manufacturing event streams.Specially, in our scheme, we firstly use pattern sharing technology to merge all the same prefix, suffix, or subpattern that existed in single pattern complex event detection models into a multiple pattern complex event detection model, then we use the new detection model to realize the quick detection for complex events from massive manufacturing event streams, as a result, our scheme can effectively solve the problems above by reducing lots of redundant building, storing, searching and calculating operations with pattern sharing technology.At the end of this paper, we use some simulation experiments to prove that our proposed multiple pattern processing scheme outperforms some general processing methods in current as a whole.
Linking Observations and Modeling of Flat-Slab Subduction

The Center for Tectonics and Tomography: Workshop on Flat Slab Subduction; Houston, Texas, 18–20 January 2019
Third International Conference on Statistical Language and Speech Processing, SLSP 2015 Preface

The lack of multi-document based models and the inaccuracy in representing multiple long documents into a fixed size vector inspired us to solve abstractive multi-document summarization. Also, there is lack of good multi-document based human-authored datasets to train any encoder-decoder models. To overcome this, we have designed complementary models for two different tasks such as sentence clustering and neural sentence fusion. In this work, we minimize the risk of producing incorrect fact by encoding a related set of sentences as an input to the encoder. We have applied our complementary models to implement a full abstractive multi-document summarization system which simultaneously considers importance, coverage, and diversity under a desired length limit. We conduct extensive experiments for all the proposed models which bring significant improvements over the state-of-the-art methods across different evaluation metrics.
AncDE with Gaussian Distribution for Numerical Optimization Problem

An enhancement of Differential Evolution (DE) is proposed in this paper. A second population is introduced using ancestor vector that keep the history of the previous population to provide diversity in the search space during mutation stage. Two additional controllers are being used to control the frequency of the ancestor vector called arp and aup for age of the ancestor vector. This work is using Normal distribution (Gaussian) to minimize the burden to decide the value for both controllers. Additional with MDV (magnitude of difference vector) as constraint for arp parameter controller. We test this proposed algorithm on CEC2015 Numerical Optimization Problem.
New Ways to Analyze and Use Images from Space

Many things become clearer when seen from above, and Earth is no exception. Images of Earth from space provide information that cannot be obtained any other way, and these images continue to make important contributions to science and commerce.
Disruptive Behavior and Students’ performance in 9 and 12 years Basic Education in Kanjongo sector, Nyamasheke District.

P rovision of quality education and training is the ultimate goal of any educational system. The success of teaching is reflected by academic performance of students. This goal can never be achieved without school discipline. How students are disciplined has been an issue since the inception of public education. Not only the students but also other adult people in their everyday works, they need some control/ supervision or some pressure so that they can do what they are supposed to do. The study employed descriptive Survey design. population was 562 people from whom 152 respondents (teachers, students, parents and school leaders) were selected as sample size according to the formula of Alain BOUCHARD, the questionnaire was used to collect data T he study revealed that disruptive behavior were at extremely high level (44.7%) before 2015 and it has fallen to a very low level (2%) in 2016.
Developing and Evolving a DSL-based Approach for Runtime Monitoring of Systems of Systems.

Complex software-intensive systems are often described as systems of systems (SoS) due to their heterogeneous architectural elements. As SoS behavior is often only understandable during operation, runtime monitoring is needed to detect deviations from requirements. Today, while diverse monitoring approaches exist, most do not provide what is needed to monitor SoS, e.g., support for dynamically defining and deploying diverse checks across multiple systems. In this paper we report on our experiences of developing, applying, and evolving an approach for monitoring an SoS in the domain of industrial automation software, that is based on a domain-specific language (DSL). We first describe our initial approach to dynamically define and check constraints in SoS at runtime and then motivate and describe its evolution based on requirements elicited in an industry collaboration project. We furthermore describe solutions we have developed to support the evolution of our approach, i.e., a code generation approach and a framework to automate testing the DSL after changes. We evaluate the expressiveness and scalability of our new DSL-based approach using an industrial SoS. We also discuss lessons we learned. Our results show that while developing a DSL-based approach is a good solution to support industrial users, one must prepare the approach for evolution, by making it extensible and adaptable to future scenarios. Particularly, support for automated (re-)generation of tools and code after changes and automated testing are essential.
Programs, packages, and apps: Some guidance for investment in technology

This is the first article in a series of two that offers some excellent advice for schools and teachers about how thoughtful and purposeful integration of technology can benefit mathematics classrooms. Several important principles to be considered are examined.
TRAINING OF FACULTY FOR WORK IN ELECTRONIC ENVIRONMENT

Helping the improvement of digital competences and more widespread usage of technologies in education are prioritized aims of European commission for 2018 as well. Each next action in support of improvement of those skills would improve the academic teaching and the quality of educational services. This article presents the organization of the conducted training with teachers about working with electronical resources for education in Medical University `Prof. D r Paraskev Stoyanov` - Varna. The trainings were with intention to increase the knowledge of academic teachers about functional opportunities in the platform `Blackboard Learn + ` for the purposes of electronical teaching and her more profound usage like helpful technology in education.
SIGMOD 2018 Program Committee Chair's Report

This paper reports on the program committee process for SIGMOD 2018, including statistics, trends, and changes from previous years. Some highlights are: Submissions to SIGMOD 2018 were down 6% from 2017. The acceptance rate of research papers was 20%, which is in line with recent years. Reviewers showed a strong bias to reject borderline papers rather than giving authors the opportunity to revise. By being biased in the second round in favor of offering authors the opportunity to submit a revision, we increased the acceptance rate significantly from the first to second round. We strongly recommend that future PC chairs adopt this bias. To help ensure high quality reviews, we gave PC members a light reviewing load and ensured that 95% of review assignments went to PC members who bid Eager or Willing to review the paper. Nevertheless, approximately 20-25% of reviews are unacceptably shallow. We need to do better. The main changes in 2018 were (i) to reduce the number of parallel sessions, (ii) include tutorials during the main conference (Tuesday-Thursday), and (iii) return to clustering industry presentations into separate industry sessions rather than grouping them with research papers on the same topic. To enable (i) and (ii), we shortened the standard presentation time to 20 minutes and offered only 10 minutes to 40% of the papers. Anecdotal evidence is that attendees were happy with these changes. The paper closes with comments about my previous experiences as PC chair for SIGMOD 1979 and VLDB 2002 and with the evolution of PC processes.
Semantics Consistent Adversarial Cross-Modal Retrieval.

Cross-modal retrieval returns the relevant results from the other modalities given a query from one modality. The main challenge of cross-modal retrieval is the “heterogeneity gap” amongst modalities, because different modalities have different distributions and representations. Therefore, the similarity of different modalities can not be measured directly. In this paper, we propose a semantics consistent adversarial cross-modal retrieval approach, which learns a semantics consistent representation for different modalities with same semantic category. Specifically, we encourage the class center of different modalities with same semantic label to be as close as possible, and also minimize the distances between the samples and the class center with same semantic label from different modalities. Comprehensive experiments on Wikipedia dataset are conducted and the experimental results show the efficiency and effectiveness of our approach in cross-modal retrieval.
Wearable Sensors for Nurses: Which Requirements Have to Be Considered?

To measure and compare the workload of nurses in a clinical setting raises up some questions. On the one hand we worked out which criteria can represent workload and how it can be measured. On the other hand we compile different requirements for wearable sensors. These requirements can be categorized in four groups: data, robustness, hygiene and usability. These results can support the selection of wearable sensors for a survey of the workload of nurses in a clinical setting.
Dynamic Detection of Software Defects Using Supervised Learning Techniques

Software testing is the main step of detecting the faults in Software through executing it. Therefore, it is substantial to predict the faults that may happen while executing the software to maintain the existence of the software. There are different techniques of artificial intelligence that are utilized to predict future defects. The Machine learning is one of the most significant technique that used to build predicting models. In this paper, conducted a systematic review of the supervised machine learning techniques which are used for software defect prediction and evaluated the performance. Thus, using five state-of-the-art supervised machine learning (classifiers), for the evaluation, several of the data are used to predict software fault. In addition to, compared the performance of these classifiers with various parameters. After that, proceeds many experiments to improve the efficiency of the prediction of the defect through modifying the default parameters of the classifier. The results showed the ability of supervised machine learning algorithms to classify classes as bugs or not bugs. Thus, using supervised machine learning models for predicting software bugs is better than the traditional statistical models. Additionally, using PCA never noticeable impact on prediction systems performance while modifying the default parameters positively impact classifier values, especially with Artificial Neural Network (ANN).The main finding of this paper is gained through the application of Ensemble Learning methods, whereas Bagging achieves 95.1% accuracy with Mozilla dataset and Voting achieves 93.79% accuracy with kc1 dataset.
Future Trends in Utilization of Deep Learning Techniques for Disease Recognition and Classification

In today’s era, due to the emerging growth of computation and automatic disease recognition, capability of deep learning facilitates human life easier. Variety of deep learning models like convolutional neural network (CNN), autoencoders (AE), deep belief networks (DBN) etc. facilitates automated machine health monitoring, and provides better diagnostic results than clinical practitioners. These techniques have developed for a variety of applications like computer vision, document analysis, pattern recognition, image synthesis and syntactic recognition, to mention a few. In this editorial article, we aims at introducing some future trends regarding the utilization of such these deep learning techniques for the recognition and classification of various disease, syndrome and distortions.