Reviewerly/exHarmony huggingface dataset:
Layer-Wise De-Training and Re-Training for ConvS2S Machine Translation

The convolutional sequence-to-sequence (ConvS2S) machine translation system is one of the typical neural machine translation (NMT) systems. Training the ConvS2S model tends to get stuck in a local optimum in our pre-studies. To overcome this inferior behavior, we propose to de-train a trained ConvS2S model in a mild way and retrain to find a better solution globally. In particular, the trained parameters of one layer of the NMT network are abandoned by re-initialization while other layers’ parameters are kept at the same time to kick off re-optimization from a new start point and safeguard the new start point not too far from the previous optimum. This procedure is executed layer by layer until all layers of the ConvS2S model are explored. Experiments show that when compared to various measures for escaping from the local optimum, including initialization with random seeds, adding perturbations to the baseline parameters, and continuing training (con-training) with the baseline models, our method consistently improves the ConvS2S translation quality across various language pairs and achieves better performance.
PHP modernization approach generating KDM models from PHP legacy code

With the rise of new web technologies such as web 2.0, Jquery, Bootstrap. Modernizing legacy web systems to benefit from the advantages of the new technologies is more and more relevant. The migration of a system from an environment to another is a time and effort consuming process, it involves a complete rewrite of the application adapted to the target platform. To realize this migration in an automated and standardized way, many approaches have tried to define standardized engineering processes. Architecture Driven Modernization (ADM) defines an approach to standardize and automate the reengineering process. We defined an ADM approach to represent PHP web applications in the highest level of abstraction models. To do this, we have used software artifacts as a entry point . This paper describes the extraction process, which permits discovering and understanding of the legacy system. And generate models to represent the system in an abstract way.
Merge-and-simplify operation for compact combinatorial pyramid definition

Image pyramids are employed for years in digital image processing. They permit to store and use different scales/levels of details of an image. To represent all the topological information of the different levels, combinatorial pyramids have proved having many interests. But, when using an explicit representation, one drawback of this structure is the memory space required to store such a pyramid. In this paper, this drawback is solved by defining a compact version of combinatorial pyramids. This definition is based on the definition of a new operation, called “merge-and-simplify”, which simultaneously merges regions and simplifies their boundaries. Our experiments show that the memory space of our solution is much smaller than the one of the original version. Moreover, the computation time of our solution is faster, because there are less levels in our pyramid than in the original one.
Retrieval of Images using Combination of Features as Color, Color Moments and Hu Moments

In today’s digital era, several of the image retrieval systems focus on retrieving images using features from images themselves such as color, shape and textures and are referred as low-level features. In this proposed work, the features like color with HSV color space, color moments and Hu moments are employed to retrieve similar images. Various experimentations were conducted on Wang’s database images to test the combination of features for higher performance using precision, recall, accuracy and f-score. The results obtained are compared with one another and also with existing works. The retrieval performance is found to be high for proposed system against existing works.
Color Recognition for Rubik's Cube Robot

In this paper, we proposed three methods to solve color recognition of Rubik's cube, which includes one offline method and two online methods. Scatter balance & extreme learning machine (SB-ELM), an offline method, is proposed to illustrate the efficiency of training based method. We also put forward a conception of color drifting which indicates offline methods are always ineffectiveness and can not work well in continuous change circumstance. By contrast, weak label hierarchic propagation is proposed for unknown all color information but only utilizes weak label of center block in color recognition. Furthermore, dynamic weight label propagation, another online method, is also proposed for labeling blocks color by known center blocks color of Rubik's cube. We finally design a Rubik's cube robot and construct a dataset to illustrate the efficiency and effectiveness of our online methods and to indicate the ineffectiveness of offline method by color drifting in our dataset.
The Role of Otoritas Jasa Keuangan (Financial Services Authority) in Supervising Sharia Banking Products in Indonesia

This research was funded by BOPTN funding. This research is focused on the Development of Basic Volleyball Learning Media Based on Web Learning Materials Towards KKNI at Fik Medan State University. This research is a research and development. After researching the results of volleyball basic technical skills based on learning groups, namely the posttest experimental group (new model, web learning) and the pretest experimental group (old model), it is found that the results of basic ball technical skills volleyball in the posttest experimental group ie the lowest score of 80 and the highest score of 90. Whereas in the pretest experimental group, the results of volleyball basic technique ability obtained the lowest score of 70 and the highest score of 90. The average score and standard deviation in the posttest experimental group were 82.00 and 4.14, while in the pretest experimental group amounted to 78.00 and 8.62. When viewed from the distribution of questionnaires to see new model learning is more effective than old model learning. The average effectiveness of the old model learning outcomes indicator is 86.57% and the learning model is new or uses web learning 98.77%. So it can be concluded that learning new models is more effective than learning models.
Prediction of Breast Tumor Malignancy Using Neural Network and Whale Optimization Algorithms (WOA)

Introduction: Breast cancer is the most prevalent cause of cancer mortality among women. Early diagnosis of breast cancer gives patients greater survival time. The present study aims to provide an algorithm for more accurate prediction and more effective decision-making in the treatment of patients with breast cancer. Methods: The present study was applied, descriptive-analytical, based on the use of computerized methods. We obtained 699 independent records containing nine clinical variables from the UCI machine learning. The EM algorithm was used to analyze the data before normalizing them. Following that, a combination of neural network model based on multilayer perceptron structure with the Whale Optimization Algorithm (WOA) was used to predict the breast tumor malignancy. Results: After preprocessing the disease data set and reducing data dimensions, the accuracy of the proposed algorithm for training and testing data was 99.6% and 99%, respectively. The prediction accuracy of the proposed model was 99.4%, which would be a satisfying result compared to different methods of machine learning in other studies. Conclusion: Considering the importance of early diagnosis of breast cancer, the results of this study may have highly useful implications for health care providers and planners so as to achieve the early diagnosis of the disease.
Comparing Top-Down and Bottom-Up Neural Generative Dependency Models

Recurrent neural network grammars (RNNGs) generate sentences using phrase-structure syntax and perform very well in terms of both language modeling and parsing performance.However, since dependency annotations are much more readily available than phrase structure annotations, we propose two new generative models of projective dependency syntax, so as to explore whether generative dependency models are similarly effective.Both models use RNNs to represent the derivation history with making any explicit independence assumptions, but they differ in how they construct the trees: one builds the tree bottom up and the other top down, which profoundly changes the estimation problem faced by the learner.We evaluate the two models on three typologically different languages: English, Arabic, and Japanese.We find that both generative models improve parsing performance over a discriminative baseline, but, in contrast to RNNGs, they are significantly less effective than non-syntactic LSTM language models.Little difference between the tree construction orders is observed for either parsing or language modeling.
BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions

This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019.Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from genomics to behaviour.Non-availability of RDoC labelled dataset and tedious labelling process hinders the use of RDoC framework to reach its full potential in Biomedical research community and Healthcare industry.Therefore, Task-1 aims at retrieval and ranking of PubMed abstracts relevant to a given RDoC construct and Task-2 aims at extraction of the most relevant sentence from a given PubMed abstract.We investigate (1) attention based supervised neural topic model and SVM for retrieval and ranking of PubMed abstracts and, further utilize BM25 and other relevance measures for re-ranking, (2) supervised and unsupervised sentence ranking models utilizing multi-view representations comprising of query-aware attention-based sentence representation (QAR), bag-of-words (BoW) and TF-IDF.Our best systems achieved 1st rank and scored 0.86 mAP and 0.58 macro average accuracy in Task-1 and Task-2 respectively.
PEMBANGUNAN HELPDESK TICKETING SYSTEM BERBASIS WEB (STUDI KASUS: UNIVERSITAS YARSI)

Web has evolved into a tool that is not only able to provide information but also to process information. Processing information by utilizing web technology turns web becoming dynamic information media. We could do many things by deploying web, such as building the Helpdesk Ticketing System for a company or institution. Every company has vision, mission, and also programs by utilizing the infrastructure that has been provided by the company. By using a Web-based Helpdesk Ticketing System, solving problems in a company will be faster, precise, accurate, and more effective. The development of a Web-based Helpdesk Ticketing System gives a better impact and efficient than manual monitoring. We use the usability method to test the user satisfaction level toward the system that has results 87.5%. Keywords: Helpdesk, Ticketing System, Information System, Web.
Special Features on Intelligent Imaging and Analysis

Intelligent imaging and analysis have been studied in various research fields, including medical imaging, biomedical applications, computer vision, visual inspection and robot systems [...]
ARABIC SPEECH RECOGNITION BASED ON KNN, J48, AND LVQ

Abstract Most systems of speaker recognition work on speech feature primarily classified of being a low level which considerably relies on speaker physical characteristics and, to the lower extent, the acquired speaking habits. In this paper present a system to recognition and identification in Arabic speaker. It includes two phases (training phase and testing phase) each phase includes the using of audio features (Mean, Standard Division, Zero Crossing, Amplitude). after get the feature, the recognition step is using (J48, KNN, LVQ),) where the Nearest Neighbor (KNN) applied o get the similarity of the data training and data testing , LVQ neural network used for Speech Recognition and Arabic language Identification. This sentence contains words especially kidnappings and kidnappers are ten sentences and pronounce these sentences by 10 people, five men and five women of different ages and each of the ten pronunciation of all sentences, so a total of 100 samples and the samples were recorded on audio and wave. The results of the sentences pronounced by women are higher than the results of the same sentences pronounced by men. They achieved better recognition rate 85, 93, 96.4%
The optimization of material utilization efficiency in fractal flow channels of the microreactor

流体处于层流主导状态, 具有直径大、流速高的 特点, 能够将物质快速运送至目标区域; 随着向下级流 道发展, 流道逐级收窄流速降低, 近末端流道中流体处 于扩散主导状态, 流体携带的溶质能够通过扩散迅速 抵达反应表面, 使得输运物质被充分利用
Use of the Hardware and Software Complex “Moscow Electronic School” in Training Teachers Working Under the International Baccalaureate Programmes

The results of a study of the influence quantity of the educational resources of the Moscow Electronic School project on the professional qualities of teachers working under the International Baccalaureate programmes are presented in the article. A comparison of the effectiveness of teachers’ work at Moscow schools of the International Baccalaureate system, who are enrolled in master’s degree programmes at Moscow City University and use these resources as an object and a tool for education, was used as the main research method. The proposed changes to the teacher training system, applied in connection with the possibility of their participation in the development and assessment of the Moscow Electronic School project’s educational resources effectiveness are described in the article, an example of students’ creative projects is provided. In conclusion, based on the data available from the experiments, the trends of the described electronic resources’ influence on the effectiveness of collective, creative and result-oriented educational activities, typical for the International Baccalaureate’s approaches are identified. Conclusions about improving teachers’ professional qualities in the field of educational, extracurricular and scientific-methodological activities are made
Impact Analysis of Incident Angle Factor on High-Resolution Sar Image Ship Classification Based on Deep Learning

In this paper, a ship classification framework based on deep learning is proposed. We focus on the influence of the incident angle factor for the classification results on deep learning-based methods. A representative SAR ship dataset containing three types of ship and the coverage of incidence angle is approximately from 20° to 60° is created. We evaluated the training-test performance of four deep learning models on the dataset. Taking cargo ship as the example, the experimental results show that when using data with different range of incident angles for training, the classification performance on test set with different range of incident angles varies greatly. The first analysis of the incident angle factor in SAR ship classification using deep learning methods allowed researchers to select appropriate data when using the deep learning method to classify ships in SAR images, and may suggest satellite parameters based on the classification results.
Study of Javanese Script Writing Learning in Senior High Schools in Boyolali Regency

The implementation of Javanese language learning is one of the activities to guide and preserve Javanese language, literature and scripts.However, there are still problems such as understanding the rules of writing Javanese script, the number of vocabulary words needed to be able to read Javanese script, the presence of students from outside Central Java, East Java, and Yogyakarta.The purpose of this study was to describe the implementation of learning to write Javanese script at the high school level along with solving related problems.This study uses a qualitative descriptive method with a case study approach.The location of this study took place at SMA N 1 Ngemplak.The results of this study indicate that the problem of implementing Javanese script writing learning is overcome by the existence of good planning and evaluation from the teacher and the active role of students in learning.The implications of this study are useful as material for evaluation in the implementation of Javanese language learning in senior high school.
The Role of Feedback When Training Pre-Service Native Language Teachers

The normatively determined competences of teachers in Bulgaria (Decree 12/01.09.2016 concerning the status and professional development of teachers, head teachers and other pedagogical specialists) prerequisite the necessity for re-evaluating the content and technological models of the pedagogic-psychological, methodical and specialized subject matter for students, who are trained to become teachers of their native language. Within the context of searching for ways of modernizing the university education of future pedagogues, the current article refers to the wide spectrum of the term professional pedagogic competence, summarizes the interdependence in forming the competences of the future native language teachers – academic, pedagogical (including assessment), communicative; presents reasoning for the significance of the content and technological models in competency-oriented university education, aimed at forming a certain aspect of the professional pedagogical competence, namely expert assessment. Based of studied/analyzed student’s approach, with pedagogic specialties, towards the problems of feedback (in primary, secondary and higher education), an interactive model for the development of future teacher’s docimological skills is offered, based on a methodical game, theatrical pedagogic situations as forms of feedback; functionality of the model is analyzed; criteria, for researching the students abilities to grade texts written by pupils, are drawn; in this regard, strategies, concerning the deficit of future teachers, are discussed. The research presents the prerequisites, stages characteristics and results of practice-tested theoretical-pragmatic model for the formation of expert assessment competence in the student, a future teacher, in the form of skills in analysing, assessing and reviewing oral and written texts by pupils, and skills in analysing pedagogic articles, concerning such assessment. The article presents and analyses several types of activities in this process, in relation to (conditionally divided) two stages, respectively: theoretical-observational – questionnaire survey of 182 students from Konstantin Preslavsky University of Shumen, concerning the role of feedback; empirically-effective – realization of interactive activities with 50 students for determining and improving their docimological abilities. The overall conclusions are to identify intersections between the dynamic activities (verbal and games) and the effective results in the process of interactive-reflective university training (with its essential cognitive, dynamic, communicative and affective aspects); giving reasons for the advantages of the experimental model: motivation for accomplishing results, active personal, dialogue and praxiological reflectivity, acquired confidence during assessment activities without the guidance of a university teacher, personal development in the direction of tolerance and ethics within pedagogical communication.
Performance Comparison of Collaborative-Filtering Approach with Implicit and Explicit Data

Challenge in developing a collaborative filtering (CF)-based recommendation system is the problem of coldstarting of items that causes the data to sparse and reduces the accuracy of the recommendations.Therefore, to produce high accuracy a match is needed between the types of data and the approach used.Two approaches in CF include user-based and item-based CFs, both of which can process two types of data; implicit and explicit data.This work aims to find a combination of approaches and data types that produce high accuracy.Cosine-similarity is used to measure the similarity between users and also between items.Mean Absolute Error is also measured to discover the accuracy of a recommendation.Testing of three groups of data based on sparseness results in the best accuracy in an explicit data-based approach that has the smallest MAE value.The result is that the average MAE value for user based (implicit data) is 0.1032, user based (explicit data) is 0.2320, item based (implicit data) is 0.3495, and item based (explicit data) is 0.0926.The best accuracy is in the item-based (explicit-data) approach which is the smallest average MAE value.
Transparency in empirical economic research

Open science and research transparency can lead to improved credibility within empirical economic research, which represents a key input in economic policy design.Nonetheless, there remain concerns surrounding the costs associated with open science and the lack of incentives for transparent research.Despite these concerns, the potential benefits justify the efforts.Researchers and policymakers should thus pay close attention to recent developments in open research that may alleviate some of the main drawbacks, such as encouraging registered reports and editorial policies to promote transparent practices. ELEVATOR PITCHThe open science and research transparency movement aims to make the research process more visible and to strengthen the credibility of results.Examples of open research practices include open data, pre-registration, and replication.Open science proponents argue that making data and codes publicly available enables researchers to evaluate the truth of a claim and improve its credibility.Opponents often counter that replications are costly and that open science efforts are not always rewarded with publication of results. KEY FINDINGS ConsSharing data in a usable format requires considerable time and effort by knowledgeable people.There is a lack of funding for, and to some extent interest in, replication studies.Transparent practices such as pre-analysis plans may stifle researchers' creativity and possibly prevent important breakthroughs arising from exploratory analysis.There may be substantial upfront costs to transparency and openness and open science efforts are often not rewarded with publication of results. ProsOpen science and research transparency offer the potential to improve empirical economic research's credibility.Sharing data and codes may allow other researchers to detect false-positive findings and increase the visibility and prominence of academic publications.A growing number of free data repositories allow researchers to share information more effectively, thus eliminating the monetary cost of storing data and code.A growing number of simple, low-cost, editorial policies may easily decrease the extent of publication bias.