Reviewerly/exHarmony huggingface dataset:
Adobe AMPS’s Submission for Very Low Resource Supervised Translation Task at WMT20

In this paper, we describe our systems submitted to the very low resource supervised translation task at WMT20. We participate in both translation directions for Upper Sorbian-German language pair. Our primary submission is a subword-level Transformer-based neural machine translation model trained on original training bitext. We also conduct several experiments with backtranslation using limited monolingual data in our post-submission work and include our results for the same. In one such experiment, we observe jumps of up to 2.6 BLEU points over the primary system by pretraining on a synthetic, backtranslated corpus followed by fine-tuning on the original parallel training data.
A Garment Suggestion Engine Based on Visual Similarity

In this study, a product suggestion engine has been developed for an e-commerce site portfolio focusing on garment items. Traditional collaborative filtering methods usually lack applicability due to high turnover rates in product lists. Thus, by focusing on visual similarity using deep learning technique successful results were obtained and it has been concluded that application of this technique to real live ecommerce garment site will be suitable.
Analysis of National Cybersecurity Strategies

Nowadays the use of information and communication technology has been incorporated in a general way in the daily life of a nation allowing the optimization in its processes.However, with it comes serious risks and threats that can affect cyber security because of the vulnerability they show.In addition, there are several factors that contribute to the proliferation of criminal actions in cyber security, the profitability offered by its exploitation in economic, political or other terms, the ease and low cost of the tools used to carry out attacks and the ease of hiding the attacker, make it possible for these activities to be carried out anonymously, from anywhere in the world and with impunity.The main objective of the research is to analyze and design National Cybersecurity Strategies to counter attacks.The methodology of this research was conducted in an exploratory and descriptive manner.As a result of the research work, a design of National Cybersecurity Strategies was obtained after an in-depth analysis of the appropriate strategies and thus minimizing the different attacks that can be carried out.
Measurement and Evaluation of Collaborative Development Level of Higher Education

With the aid of grey correlation theory and technique for order of preference by similarity to ideal solution (TOPSIS), this paper sets up a multi-dimensional evaluation index system (EIS) for the comprehensive development level (CDL) of China’s higher education, and quantifies the state of collaborative development in 2005-2017 with an improved distance collaboration model. The results show that: Being the premises and bases of higher education, the supply subsystem develops much slower than the other subsystems (i.e. participation, output, and environment), and clearly pulls the development of the other subsystems. From 2012 onwards, the subsystems conformed to basically the same trend for the degree of collaboration: the degree of collaboration tended to be stable. There was even a slight dent in overall degree of collaboration between 2012 and 2016. Hence, the overall degree of collaborative development was far slower than the overall degree of development. After more than a decade of development, the degree of development, degree of collaboration, and degree of collaborative development reached basically the same level in 2017. The empirical results shed new lights on the focal points of higher education development in China.
Using Classification with K-means Clustering to Investigate Transaction Anomaly

Applications of machine learning and related algorithms in Electronic Commerce (hereafter E-Commerce) have the potential to build robust analytical models that help examine transaction data and successfully detect and predict anomalies. Nonetheless, the robustness of such models can be undermined in the case of highly unbalanced data set. This paper presents a classification method built on K-means Clustering that addresses the issue of highly unbalanced data. In this method, we first pre-process our E-Commerce data and then apply clustering and classifying procedures to create a number of clusters where each resulting cluster includes similar transaction records. Next, four classifiers including Logistic Regression, Naive Bayes, RBFNetwork and NBtree classifiers are used to assess the resulting solution. Findings based on real-word data show that this method provides a better solution for transaction anomaly detection and prediction than traditional approaches. They also show that it straightforwardly resolves classification problems with data imbalance.
Classification of Imbalanced Dataset using Generative Adversarial Nets

One of the most encountered problems in the training of artificial neural networks is imbalanced datasets. It is common to apply oversampling algorithms to overcome the adverse affects of imbalanced datasets in classification performance. In this study, a new oversampling method based on Generative Adversarial Nets (GAN) and Edited K-Nearest Neighbor (KNN) is proposed. It is observed that the proposed method increases the classification performance more than that of the oversampling techniques based on the conventional SMOTE algorithm.
Combination of Neural Machine Translation Systems at WMT20

This paper presents neural machine translation systems and their combination built for the WMT20 English-Polish and Japanese->English translation tasks. We show that using a Transformer Big architecture, additional training data synthesized from monolingual data, and combining many NMT systems through n-best list reranking improve translation quality. However, while we observed such improvements on the validation data, we did not observed similar improvements on the test data. Our analysis reveals that the presence of translationese texts in the validation data led us to take decisions in building NMT systems that were not optimal to obtain the best results on the test data.
PATQUEST: Papago Translation Quality Estimation

This paper describes the system submitted by Papago team for the quality estimation task at WMT 2020. It proposes two key strategies for quality estimation: (1) task-specific pretraining scheme, and (2) task-specific data augmentation. The former focuses on devising learning signals for pretraining that are closely related to the downstream task. We also present data augmentation techniques that simulate the varying levels of errors that the downstream dataset may contain. Thus, our PATQUEST models are exposed to erroneous translations in both stages of task-specific pretraining and finetuning, effectively enhancing their generalization capability. Our submitted models achieve significant improvement over the baselines for Task 1 (Sentence-Level Direct Assessment; EN-DE only), and Task 3 (Document-Level Score).
RETRACTED: Embedded Gpsgsm based on cloud computing data processing in soldiers' physical fitness training

National security has become a significant force in the current state. In war, a strategy is the basic unit of defense in any country. There are many concerns about these personnel fighters' fitness using smart sensors to be familiar with GPS (Global Positioning System). Used to solve and log in range, of GPS bearer can get to know well without any problems. The worker at the base station will give an association using a single worker telecommunication system. Each cavalry has a GSMC (Global System Mobile Communications) module for the base station during injury and disaster, and its recognition applies. During the war that put forward the concept of component, soldiers are now a GPS lighthouse-like soldier's updated state. As the discovery progressed, images were taken to monitor the human body's physiological boundaries from various wearable senses. Such physiological and biomedical sensor centers can use the Body Sensor Network (BSN), close to or inside the human body, for periodic welfare surveys. Will design a series of efforts to establish a framework for field studies of relentless welfare fighters, including BSNs. Talk about the basics of this joint venture application. Propose an application that affects the initial model characterization and source boundary of the framework. A high mental quality employee should have an average product volume (P <0.05) of 66% higher than the average of low mental quality players. The 'administrative law enforcement' for military veterans are between real arrangements in different areas.
Reduction of JPEG2000 Compression Artifacts using Very Deep Super Resolution Approach for Multitemporal Multispectral Images

Nowadays, multispectral images are acquired by many different satellites. These satellites produce a large amount of data by traveling around the world. Efficient compression of these images are important issues. In order to solve this problem, the most used method is compression of the images with JPEG2000 method. However, distortion occurs after JPEG2000 compression, especially at low bit-rates. This situation affects the performance of some applications such as change detection, classification, anomaly detection etc. In this paper, in order to overcome compression artifacts, a very deep süper-resolution approach is proposed as post-processing after JPEG2000 decompression. In the experiments, scenes of the Onera dataset that is shared in the website of IEEE GRSS DASE are used. Experimental results show that the proposed method not only increases the image quality but also improves the change detection performance.
Spatio-Temporal Crime Prediction with Temporally Hierarchical Convolutional Neural Networks

In this paper, we propose a new deep learning based model that uses convolutional neural networks for spatiotemporal crime prediction. To learn the temporal pattern of crime events, we employ a temporally hierarchical structure that branches along the temporal dimension. In addition, channel projection is applied to capture the separate influences of crime events over future crime risk. In the results section, our model is compared with classical methods and the performance is analyzed on publicly available Chicago and Los Angeles crime datasets. The proposed model significantly improves the performance compared to the traditional methods.
INTERACTIVE LECTURES AND ROLE PLAYING IN TEACHING PHYSICS AND ASTRONOMY

Teaching is the process of shaping and developing student’s personality, knowledge, and business skills, in accordance with modern requirements. Therefore, also teachers should enrich their methods in accordance with these requirements. Game-like living situations, role playing, theatrical performances are possible tools in order to increase students’ interest and allow them to acquire the knowledge and skills which could be applied in practice in the future. The main requirements for these games are an incorporation of learners into the image of professional-pedagogical thinking. Triggering an active participation of pupils and students is a part of constructivistic teaching strategies.&nbsp; This article describes how to teach and explain physics and astronomy, using games, role-playing and theatric performances played by elementary school pupils. We report a long (15 years) experience developed in Poland at Nicolaus Copernicus University, and later applied in other places, including the National Science Center in Warsaw and also abroad.&nbsp; Methods of increasing the effectiveness of teaching using role-playing games as an element of competition at school of Kazakhstan were considered. Both experiences revealed to be very successful. Key words: teaching, game-based teaching, business games, role-playing games, constructivism.&nbsp
Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models.

In neural machine translation (NMT), sequence distillation (SD) through creation of distilled corpora leads to efficient (compact and fast) models. However, its effectiveness in extremely low-resource (ELR) settings has not been well-studied. On the other hand, transfer learning (TL) by leveraging larger helping corpora greatly improves translation quality in general. This paper investigates a combination of SD and TL for training efficient NMT models for ELR settings, where we utilize TL with helping corpora twice: once for distilling the ELR corpora and then during compact model training. We experimented with two ELR settings: Vietnamese–English and Hindi–English from the Asian Language Treebank dataset with 18k training sentence pairs. Using the compact models with 40% smaller parameters trained on the distilled ELR corpora, greedy search achieved 3.6 BLEU points improvement in average while reducing 40% of decoding time. We also confirmed that using both the distilled ELR and helping corpora in the second round of TL further improves translation quality. Our work highlights the importance of stage-wise application of SD and TL for efficient NMT modeling for ELR settings.
MULTIMODAL SEMIOTIC SYSTEM ANALYSIS ON ADVERTISING AZAN SHOLAT (Malaysia Version)

This research analized the multimodal of advertisement Azan Prayer Malaysia Version. Advertising both print media ads and electronic media ads is one text that has complexity of meaning. The complexity of the meaning that occurs because to convey the message in the ad not only used elements of language but also elements of nonverbal language and other visual means. Therefore, to understand the complexity of meaning needs to be done multimodal analysis in the ad. The multimodal analysis discussed in this study uses functional systemic linguistic theory (LSF). Multimodal model of analysis was developed from a combination of multimodal theory (Anstey &amp;amp; Bull, 2010), and multimodal analysis by Kress and Van Leeuwen (1996-2006). From the analysis of the advertisement Azan Prayer Malaysia Version obtained that this ad has multimodal semiotic system which includes linguistic, visual, audio, gestural, and spastial elements.
Translating Sentimental Statements Using Deep Learning Techniques

Natural Language Processing (NLP) allows machines to know nature languages and helps us do tasks, such as retrieving information, answering questions, text summarization, categorizing text, and machine translation. To our understanding, no NLP was used to translate statements from negative sentiment to positive sentiment with resembling semantics, although human communication needs. The developments of translating sentimental statements using deep learning techniques are proposed in this paper. First, for a sentiment translation model, we create negative–positive sentimental statement datasets. Then using deep learning techniques, the sentiment translation model is developed. Perplexity, bilingual evaluation understudy, and human evaluations are used in the experiments to test the model, and the results are satisfactory. Finally, if the trained datasets can be constructed as planned, we believe the techniques used in translating sentimental statements are possible, and more sophisticated models can be developed.
Corporate Social Responsibility and Firm Performance: Evidence from Pakistan

The current study aims at exploring the relationship of CSR practices with firm performance (FP) in the non-financial sector of the Pakistan stock exchange (PSX). For this purpose, the study uses sample data of 231 companies listed at PSX. The study uses “donation amount to sales” as a proxy variable for CSR practices and return on equity (ROE), return on assets (ROA) as the proxies of firm performance. The models are tested using a panel regression estimation technique with a fixed effect method, as suggested by Hausman test. The results show that CSR is significantly positively related to ROE. Findings indicate that investment in CSR brings positive change in a firmâ€Ÿs profitability which ultimately leads to an increase in the shareholderâ€Ÿs wealth. Keywords: Corporate Social Responsibility, Pakistan Stock Exchange, Firm Performance, Fixed Effect Model, Non-financial Sector.
Biological Optical AI and Bionic Optical AI

We first found this inverted retina structure of human and vertebrates is biological optical AI, which is consisted of optical fiber couplers and saturable absorbers. In this paper, we study the strong fiber coupling effects among seven identical human foveal cones. We first found there are biological transistors and biological diodes on retinas and brains of human and other animals. We shall discuss bionic applications of optical AI as well.
Extracting Strong Policies for Robotics Tasks from zero-order trajectory optimizers

Solving high-dimensional, continuous robotic tasks is a challenging optimization problem. Model-based methods that rely on zero-order optimizers like the cross-entropy method (CEM) have so far shown strong performance and are considered state-of-the-art in the model-based reinforcement learning community. However, this success comes at the cost of high computational complexity, being therefore not suitable for real-time control. In this paper, we propose a technique to jointly optimize the trajectory and distill a policy, which is essential for fast execution in real robotic systems. Our method builds upon standard approaches, like guidance cost and dataset aggregation, and introduces a novel adaptive factor which prevents the optimizer from collapsing to the learner's behavior at the beginning of the training. The extracted policies reach unprecedented performance on challenging tasks as making a humanoid stand up and opening a door without reward shaping
Face Generation using Deep Convolutional Generative Adversarial Neural Network

Due to the huge availability of data, it is difficult to classify/process images at a higher speed and accuracy.The first technique was in the field of computer vision and it used image data for face recognition and detection of an object from the image but later Convolutional Neural Networks (CNNs) took place.CNNs are used for feature detections by looking at the image and try to check if certain features are present in the image and then it classifies the image accordingly.Acquiring and processing the dataset for the Machine learning technique is one of the time-consuming processes, so Generative Adversarial Neural Network ( GAN ) are introduced.GAN typically work with image dataset but they are difficult to train.This paper explores the potential of GAN to generate realistic images.Deep Convolutional Generative Adversarial Networks (DCGAN) is used to generate new images that are not in the dataset.DCGAN has a great success in generating the new images.MNIST (Modified National Institute of Standards and Technology dataset) contains images of handwritten digits dataset and CelebA dataset contains images of celebrities are used, performing the adversarial learning on it and try to generate new images as same as the MNIST and CelebA datasets.
Automatic Generation of Game Levels Based on Controllable Wave Function Collapse Algorithm

Procedural content generation automatically creates game content through methods such as pseudo-random numbers, which helps save labor or create games that can be played repeatedly and indefinitely. The wave function collapse algorithm is an effective procedural content generation algorithm newly proposed in recent years, but it has the problems of complicated rule writing and lack of non-local constraints. In this paper, based on the original wave function collapse algorithm, an automatic rule system is proposed, which can simplify the rule writing. Moreover, we use the three mechanisms, namely global constraint, multi-layer generation, and distance constraint, to establish non-local constraints. Through experiments, compared with the original wave function collapse algorithm, the results show that manual control has been enhanced, and the generated levels have a certain degree of similarity with human-designed levels. By making a real-time dynamic level game demo using this method, it turns out that the controllable wave function collapse algorithm we proposed has great potential in the game level generation field.
Covert Channel Detection Using Machine Learning

A covert channel is a communication method that misuses legitimate resources to bypass intrusion detection systems. They can be used to do illegal work like leaking classified (or sensitive) data or sending commands to malware bots. Network timing channels are a type of these channels that use inter-arrival times between network packets to encode the data to be sent. In this study, we worked with two types of network covert channels: Fixed Interval and Jitterbug. We were able to distinguish these channels from legitimate ones by using decision trees that use four statistical features (mean, variance, skewness, and kurtosis).
Future Job Market of Information Technology in the Kingdom of Bahrain

The college of Information Technology at University of Bahrain conducted a job market study to investigate the Information Technology job market needs at the Kingdom of Bahrain. Twelve fields have been identified to be the most needed by the global IT job market. The most important job roles have been identified for each field. An online survey has been distributed to employers at different key organizations in Bahrain. The study revealed that the fields of IT Systems and Projects Management, Security, and System Analysis, Design, and Development are the most fields in demand in Bahrain for employment. Required job roles for each of those fields have been also identified.
Turkish Issue Report Classification in Banking Domain

Users report the problems they encounter while using a software product with software issue reports. It is important that they are assigned to the correct software team or developer so that they are resolved quickly. Incorrect assignment may increase solution times, thus causing customer dissatisfaction. Past studies suggest to use text classification techniques to automatically assign issue reports. In this study, software issue reports written in Turkish, obtained from an industrial case in the banking sector are classified by applying deep learning techniques on word embedding representation, and the results are compared with our baseline model, which is applying Support Vector Machines (SVM) on top of the bag of words (BOW) model. In our study, best results are obtained when words are presented with BOW model and classes are predicted with the SVM algorithm.
BERT Model Applications: Other Tasks

In the last chapter, we learned about BERT and its usage in the design of a question answering system. This chapter discusses how BERT can be used for implementation of other NLP tasks such as text classification, named entity recognition, language translation, and more.
Economic Governance Reforms to Support Inclusive Growth in the Middle East, North Africa, and Central Asia

cial effects on economic growth.First, they improve macroeconomic stability by increasing government's effectiveness in using public resources and providing services (IMF 2019b; World Bank 2016; Tanzi and Davoodi 2002, Abed and Gupta 2002).Second, they improve investors' confidence and countries competitiveness in the global market (Busse and Hefeker 2007).Third, they can make growth more inclusive by spreading its benefits among all members or groups in society (Campos, Estrin,