Reviewerly/exHarmony huggingface dataset:
A Smart Arabic Signboard Image Reader for Mobile Devices

We propose in this paper A Smart Transparent Neural Network Arabic Signboard Image Reader for Mobile Devices. A Transparent Neural Network (TNN) based on human behavior reading is already implemented for the automatic reading of IFN/ENIT database vocabulary. Because the TNN was evaluated to support more than 80 million words with high performance of time and memory consumption, it is suitable to continue Arabic script research on it. The fields that we are exploring are: First, improve the TNN to recognize Sentences (STNN) because signboards contain more than one word. Second, transform the STNN to be suitable not only for desk/lap top machines but mainly for any Smart (SSTNN) for mobile devices . Third, prepare the database needed for the evaluation of such smart system on Arabic signboards images taken by a smartphone. Finally, evaluate the SSTNN on the new smartphone Arabic signboard images database. In this paper, we present firsthand the design steps of our SSTNN that can be used as an Arabic script Android mobile application tool. Secondly, we describe our Smartphone Signboard Database (SSSD) of more than ten thousand images taken by more than 5 mobile devices of over 500 of Saudi signboards. The vocabulary of the SSSD is used for the evaluation of the SSTNN. More than 70% of pilgrims in HAJJ are non-Arabic speakers, and many signboards used to direct pilgrims are translated only to English and sometimes to Urdu.
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models

Word embeddings are an essential component in a wide range of natural language processing applications.However, distributional semantic models are known to struggle when only a small number of context sentences are available.Several methods have been proposed to obtain higher-quality vectors for these words, leveraging both this context information and sometimes the word forms themselves through a hybrid approach.We show that the current tasks do not suffice to evaluate models that use word-form information, as such models can easily leverage word forms in the training data that are related to word forms in the test data.We introduce 3 new tasks, allowing for a more balanced comparison between models.Furthermore, we show that hyperparameters that have largely been ignored in previous work can consistently improve the performance of both baseline and advanced models, achieving a new state of the art on 4 out of 6 tasks.
Implementation of Design-Based Research in Software Engineering Education: A Case of Final Project Course

This study aims to carry out final projects that were designed and coded within the scope of Software Engineering Final Project course in accordance with design-based research and observe the process hereof. Students developed their final projects pursuant to design-based research within the framework of a study that lasted for 8 weeks in total in spring term 2017-2018. Having been developed by relevant researchers, observation and control form were used in the study as data gathering tools. Processes of these projects that were developed via these forms were recorded on a weekly basis. Throughout this study, in which totally 8 students participated and 5 projects were created, the whole process of “Which one is your choice?”, a mobile application development project, was described. All stages of design and realization of this project, in which Mobile-D software development method was used, were revealed in detail. This study describes the details of a design-based research process which was operated on a real software project. The result of the study casts light upon effective utility of this method in project-based courses particularly in software engineering education and generally in engineering education.
Towards an Autonomous Framework for HPC Optimization: Using Machine Learning for Energy and Performance Modeling

Performance and energy efficiency are now critical concerns in high performance scientific computing. It is expected that requirements of the scientific problem should guide the orchestration of different techniques of energy saving, in order to improve the balance between energy consumption and application performance. To enable this balance, we propose the development of an autonomous framework to make this orchestration and present the ongoing research to this development, more specifically, focusing in the characterization of the scientific applications and the performance modeling tasks using Machine Learning.
Information Security Using Hilbert With Hash Value

The vast growth of technology the speed of data transfer are increased.During data transmission through internet the data are prone to vulnerabilities.Intruders may gain access and modify the data.Cryptology is a science of securing data.So we propose a security approach is Hilbert with hash value.This future is confusing the intruders and generate a variety of cipher texts with hash value to the same plain text.We illustrate the range of property differ by algorithm such as RSA and show cryptanalysis of projected algorithm by resist a range of attacks and stronger than exist algorithms.
The perspective of using the educational and scientific crises management center of the Bauman Moscow State Technical University in the interests of the space industry

The article discusses Situational Awareness in relation to the space industry. A new scheme of the Situational Awareness model has been proposed. The necessity and possibility of implementing Situational Awareness in the interests of the space industry on the basis of situational centers is shown. The possibilities of educational and scientific crises management center of the Bauman Moscow State Technical University in training for the management of the space industry were analyzed.The article discusses Situational Awareness in relation to the space industry. A new scheme of the Situational Awareness model has been proposed. The necessity and possibility of implementing Situational Awareness in the interests of the space industry on the basis of situational centers is shown. The possibilities of educational and scientific crises management center of the Bauman Moscow State Technical University in training for the management of the space industry were analyzed.
SOAP and RESTful web service anti-patterns: A scoping review

Web services provide a uniform framework to achieve a high level of interaction between distributed heterogeneous software systems and data resources shared over the Internet.Producing a well-designed web service is significant because it leads to a more understandable service and a higher level of interaction and leads to effective software maintainability.However, web service is suffering from a poor design problem named anti-patterns.Analysis of the literature returned a plethora of studies on anti-patterns that caused difficulties for developers to synthesize and summarized the possible types of anti-patterns and further comprehend each of them.Due to this limitation, this paper aims to provide organized literature on the types of anti-patterns found in web services.A scoping review was conducted by searching scholarly documents, analyzing, and classified them based on their anti-pattern types.The review provided in this paper could be used as a guide for developers to identify the antipatterns that could be found in web services.
Resolución de problemas con tecnología en un ambiente de aprendizaje colaborativo wiki en la educación media

The purpose of the study is to characterize the procedure performed by high school students to solve problems using technology in a collaborative wiki-supported learning environment. Action research was used as the methodology. Ten eleventh-grade students from a public educational institution in the municipality of Ciudad Bolivar, Antioquia, Colombia, participated. The results show that recognition of the context is necessary to propose hypotheses for a solution. The conclusions affirm that when students apply the stages of problem identification, information research and selection, hypotheses formulation, hypothesis execution, and solution assessment, they are more likely to find a solution that responds to the initial problem.
A Novel Graph-Based Representation for Hadith Sanad

Hadith is the secondary source of Islam legislation that has three-part, i.e., Sanad, Matn, and Taraf.Sanad is an essential part of Hadith that represent the chain of Narrator who conveys the Hadith.Based on Hadith Science, the authentication of the Hadith also could be observed through the state of its Sanad.Most studies on the Hadith Sanad representation apply an ontology and XML.Thus, this study proposed a new model of Sanad Hadith representation exploits the Graph model.First, the candidate of Graph node and Graph relation were extracted automatically from raw Arabic Hadith text using Arabic Part of Speech (A-POS) and Arabic Named Entity Recognition (A-NER).Then, a novel machine learning model for the Hadith Sanad Graph Construction developed employs SVM and GBM algorithm.That model attained the best performance on 0.84 and 0.92 precision average, 0.83 and 0.91 recall average, 0.82 and 0.90 f1-score average.The final result of this study was a Hadith Sanad Graph that had been verified the correctness compare with the original Hadith text.
Perceptual Hashing based on Machine Learning for Blockchain and Digital Watermarking

In our previous study, we found three requirements for digital watermarking. The first is that to prevent watermark information of an image from being diverted to other images, this information must be generated based on the original image. The second is that after the original image is modified/edited, it should still be able to be used the same as the original image. The third is that multiple digital watermarks should be stored and managed without relying on trusted third parties. To meet these requirements, we proposed a digital-copyright-management system based on perceptual hashing and blockchain. However, because we used conventional perceptual hashing in that study, we could not draw sufficient conclusions about the first and second requirements. In this current study, to obtain a stable message digest, we propose a method of improving perceptual hashing based on machine learning. With this method, an image is first modified/edited using various methods to generate an image set. This image set is then input into a convolutional neural network (CNN) to calculate the features of the images, and the data of the CNN intermediate layer are output as machine learning data. Finally, through machine learning, latent stochastic variables are determined that can be used to calculate latent image features, and the perceptual hash value of this image set is calculated using these image features for the blockchain and digital watermarking. The method also records these latent stochastic variables on the blockchain to ensure copyright security by ensuring that these variables cannot be used by those other than the original author.
Linguistic and cultural aspects of relationship status on Facebook and Vkontakte

This paper focuses on the differences and similarities of relationship status lexical realization in two social networks Facebook (Fb, American) and Vkontakte (Vk, Russian). This cross-cultural work reveals the variety of lexical forms available to tag a relationship status in four languages (American English, German, French, and Russian) conditioned by cultural and social oriented values. It also discusses translation problems and mistakes caused by different cultural realities. The analyzed translation cases are divided into literal translation, borrowing, transposition, modulation, and adaptation. The authors compare the translations in the two social networks and suggest more adequate and culturally adapted options. In general, there is a prevalence of literal translation (58 % on Fb; 54% on Vk) over other techniques (42% on Fb; 46% on Vk). The results of the analysis show that Fb translations are more adequate and culturally adapted (with only one inaccuracy) than Vk translations (with seven inaccuracies). The findings can be used as recommendations for the social network translators and further linguistic research in cross-cultural issues involving language, culture and society.
A Dynamic Virtual Datacenter Selection Strategy for Integrated Cloud Service Platform Construction With Multiclouds

The pay-as-you-go model and network virtualization of cloud computing allow micro and small content businesses (MSCBs) who construct their integrated cloud service platforms (ICSPs) with virtual datacenters (VDCs) to serve their end users with low service latency and construction cost. However, designing a flexible VDC selection strategy to meet the demands of MSCBs is a challenging task. To address this problem, a dynamic VDC selection strategy is designed for MSCBs to construct their ICSPs flexibly with the VDCs from different clouds. To this end, three dynamic landmark selection metrics are proposed and applied to construct the network coordinates. Then, a dynamic VDC selection algorithm is presented to determine the locations and service resources of VDCs, which are purchased from different clouds by MSCBs to construct their ICSPs. Based on our VDC selection strategy, a simulator is developed based on our designed experimental framework to evaluate our VDC selection strategy. The experimental results show that compared with previous server placement strategies, our strategy can actively and effectively determine VDCs' locations and allocate service resources for each VDC with less computing consumption and is a practical VDC selection strategy for cloud construction in a multicloud environment.
Policy conflicts and factors affecting conflict resolution in South Korea

Purpose This paper aims to identify and explore the overall frequency and characteristics of policy conflicts, with a focus upon those factors affecting conflict resolutions in South Korea. Design/methodology/approach The study uses data from cases of conflict from the Korean Public Policy Conflict Database (KPPCDB) (1948-2014). For the analysis of data, chi-squared test and multinomial logistic regression are used. Findings The findings show a total of 2,030 policy conflicts in 1948-2014, most of which were conflicts of interest (47.9 per cent). More than 70 per cent (71.2 per cent) were policy conflicts between the government and the private sector; the field with the most policy conflicts was regional development (21.0 per cent), and 84.1 per cent of all policy conflicts were resolved. The factors that affected conflict resolution by interest were conflicts between the government and private sector, authoritarian government, national regions and capital areas. Practical implications This paper suggests reforming the current procedures of conflict management, adopting alternative dispute resolutions, and developing a social-consensus-building process for efficiently resolving conflicts. Originality/value This study built a database (KPPCDB) examining 66 years of conflict cases that took place between 1948, the year the Korean Government was established, and 2014. This database covers all cases of policy conflicts that occurred in Korea and provides a comprehensive understanding of the phenomena of policy conflicts and conflict resolution.
The use Instagram with visiting interest: The correlation between the use @littlecollins.bdg Instagram with visiting interest

Abstract In this research the aim was to determine how far of the influence of using social media Instagram with visiting interest. As done by one Instagram of one of the new restaurants in Bandung that is Cafe Little Collins and have Instagram that is @littlecollins.bdg. In this research, researchers used a quantitative approach and using correlational studies. The results indicate the correlation between the use of Instagram with visiting followers’ interest is a significant correlation. Because the data collection technique used was a questionnaire with 95 respondents and a literature study relevant for this research. After the data collected then performed analysis and hypothesis test using Spearman Rho ranks correlation.
Attention and Dopamine Hits

Abstract In January 2017, George Orwell’s futuristic dystopian novel 1984 was brought back to life. The reason this 70-year-old classic all of a sudden became a no. 1 bestseller on Amazon is likely to be found in the White House. But in focusing too much on the dangers forecast in 1984 , we should not forget an older and less famous vision, Aldous Huxley’s Brave New World (1932). It is at least as relevant as the Orwellian dystopia. Its content easily translates to today’s criticisms of technology, as it describes how people will love the very same technology that deprives them of their ability to think clearly and critically. Both visions of the future have been put face-to-face by cultural and media critic Neil Postman: “What Orwell feared were those who would ban books. What Huxley feared was that there would be no reason to ban a book, for there would be no one who wanted to read one. Orwell feared those who would deprive us of information. Huxley feared those who would give us so much that we would be reduced to passivity and egoism. Orwell feared that the truth would be concealed from us. Huxley feared the truth would be drowned in a sea of irrelevance. Orwell feared we would become a captive culture. Huxley feared we would become a trivial culture, preoccupied with some equivalent of the feelies, the orgy porgy, and the centrifugal bumble puppy.” Postman’s presumption, that Huxley’s future vision was not the least relevant of the two, rings truer today than ever before. In the Information Age, there is an abundance of information and competition over our attention. This has created an attention economy in which tech giants compete for harvesting the most attention and reselling it to third party advertisers. But this war over attention has its victims. First, it has led tech giants to develop still smarter designs whose purpose it is to create dependence. The idea is for users to spend as much time as possible on the platforms and click, like, share as often as possible—to engage. Second, companies add targeted, usually secret, ingredients to their algorithms, which then reward the content that attracts the most attention and traffic. This has led to a knowledge deficit in an online world dominated by emotions. We have long been blind to the negative consequences of this attention-based infrastructure and have come to love a technology that gobbles up our ability to think reflectively.
THE EFFECT OF THE USE OF VIDEO AS A MEDIUM OF LEARNING ENGLISH TO IMPROVE STUDENTS' SKILLS IN WRITING PROCEDURE TEXTS

The purpose of this study is to obtain evidence that there is an influence on the use of video on students' skills in writing procedure texts. This research was conducted in July 2019 in class IX of SMP Negeri 27 Banjarmasin. The method used is a quantitative method with a quasi-experiment design. The sampling technique used in this study is a purposive sampling method in which the researcher has had special consideration in choosing a research sample based on data provided by an English subject teacher at the school. The instrument used in this study is a test sheet that has been tested for validity and reliability. The two classes used as subjects in this study are called the experimental class and the control class which for the experimental class is taught through video and the control class is taught using audio media only by using speakers without image or visual display. The class that is an experimental group is a class that has a lower value than the pre-test results, so the researcher wants to prove whether video media can increase that value. The data obtained from the two experimental and control groups were tested for normality and homogeneity first and then the independent sample t-test was conducted. The results show that the significance value (2-tailed) from the analysis of independent sample t-test shows that the value is smaller than 0.05 where the value obtained is 0.00 so based on statistical decision in making provisions if the value is lower than 0.05 then there is a significant difference between the post-test results of students taught by using video and audio media only through speaker facilities in the ninth grade at SMPN 27 Banjarmasin.
Correction of Color Saturation for Tone Mapping Operator

This article reviews the problems of color saturation correction during dynamic range compression of images. Special attention is drawn to emulation of human vision and photo cameras effects during processing of the images which have very bright areas (with brightness more than an order of magnitude greater than average brightness of the rest of the image). A method of desaturation of the brightest parts of images is suggested. This method provides lowering of the color saturation of each pixel depending on the pixel brightness. The improved method lowers saturation only of the brightest parts of an image. The size of the desaturated part of the image should not exceed 25% of the size of an entire image.
Rotation Angle Estimation of JPEG Compressed Image by Cyclic Spectrum Analysis

Image rotation is a common auxiliary method of image tampering, which can make the forged image more realistic from the geometric perspective. Most algorithms of image rotation angle estimation employ the peak value on the Fourier spectrum; however, JPEG post-processing brings additional peak interferences to the spectrum, which has a great impact on algorithm performance. In this paper, angle estimation is carried out for images compressed by JPEG. Firstly, the Fourier cyclic spectrum of image covariance is calculated, followed by semi-soft threshold wavelet transform to eliminate the block artefacts brought by JPEG compression. According to the shortest distance principle in the range of the limited amplitude, the processed cyclic spectral data are sorted to select the peak points. Finally, according to the selected peak point, the corresponding position coordinates of the theoretical peak point are found, and the rotation angle of the image is estimated by the theoretical peak point. Experimental results demonstrate that the average absolute error of the proposed algorithm is significantly lower than that of the state-of-the-art methods investigated, which highlights the promising potential of the proposed method as an image resampling detector in practical forensics applications.
Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-of-the-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries.