Reviewerly/exHarmony huggingface dataset:
Automatic Correction of Human Translations

We introduce translation error correction (TEC), the task of automatically correcting human-generated translations.Imperfections in machine translations (MT) have long motivated systems for improving translations posthoc with automatic post-editing.In contrast, little attention has been devoted to the problem of automatically correcting human translations, despite the intuition that humans make distinct errors that machines would be well-suited to assist with, from typos to inconsistencies in translation conventions.To investigate this, we build and release the ACED corpus with three TEC datasets 1 .We show that human errors in TEC exhibit a more diverse range of errors and far fewer translation fluency errors than the MT errors in automatic post-editing datasets, suggesting the need for dedicated TEC models that are specialized to correct human errors.We show that pre-training instead on synthetic errors based on human errors improves TEC F-score by as much as 5.1 points.We conducted a human-in-the-loop user study with nine professional translation editors and found that the assistance of our TEC system led them to produce significantly higher quality revised translations.
AN AGGRANDIZED FRAMEWORK FOR ENRICHING BOOK RECOMMENDATION SYSTEM

In this era of information overload, Recommender Systems have become increasingly important to assist internet users in finding the right choice from umpteen numbers of choices. Especially, in the case of book recommender systems, suggesting an appropriate book by considering user preferences can increase the number of book readers in turn having an aftereffect on the users’ lifestyle by reducing stress, stimulating imagination, improving vocabulary, and making readers smarter. The majority of book recommender systems in the literature have used Collaborative Filtering (CF) and Content-Based Filtering (CBF) methods. Even though CBF methods have shown better performance than CF methods, they are mostly confined to shallow linguistic features. The present work proposed an aggrandized framework having three concurrent modules to improve the recommendation process. NER module extracts the Named Entities from the entire book content which are the key semantic units in providing clues on the possible choices of reading other related books. The Visual feature extraction module analyzes the book front cover to detect objects and text on the cover as well as the description of the cover which can bestow a clue for the genre of that book. The Stylometry module enhances the feature set used in the literature to analyze the author’s literary style for identifying similar authors to the present author of the book. These three modules conjointly improved the overall recommendation accuracy by 18% over the baseline CBF method that indicates the effectiveness of the present framework.
Create a Book Recommendation System using Collaborative Filtering

One of the most important applications of data science is the recommendation system. Every organization requires a good recommendation system to express a large range of items. This research focuses on the creation of a book recommender system using collaborative filtering.
Flexible Multi-Objective Particle Swarm Optimization Clustering with Game Theory to Address Human Activity Recognition Fully Unsupervised

Most research in human activity recognition is supervised, while non-supervised approaches are not completely unsupervised. In this paper, we provide a novel flexible multi-objective particle swarm optimization (PSO) clustering method based on game theory (FMOPG) to discover human activities fully unsupervised. Unlike conventional clustering methods that estimate the number of clusters and are very time-consuming and inaccurate, an incremental technique is introduced which makes the proposed method flexible in dealing with the number of clusters. Using this technique, clusters that have a better connectedness and good separation from other clusters are gradually selected. To improve the convergence speed of PSO in achieving the best solution and dealing with spherical shape clusters, updating of particles' velocity is modified using the concept of mean-shift vector. To solve multi-objective optimization problems, Nash equilibrium in game theory is used to select the optimal solution on the pareto front. Gaussian mutation is also employed on the pareto front to generate diverse solutions and create a balance between exploitation and exploration. The proposed method is compared with state-of-the-art methods on five challenging datasets. FMOPG has improved clustering accuracy by 3.65% compared to automated methods. Moreover, the incremental technique has improved the clustering time by 71.18%.
Effect of Trade Openness and Real Exchange Rate on Economic Growth in Tanzania

The purpose of this study was to assess the effect of trade openness and real exchange rate on economic growth in Tanzania. Secondary time series data collected annually for consecutive 47 years since 1970 to 2016 were analyzed. The study used the Autoregressive Distributed Lag (ARDL) to assess the long-run and short-run effects of trade openness, real exchange rate, and foreign direct investment on Real Gross Domestic Product. The results from analysis reveal the evidence on one hand that trade openness has a positive significant effect on economic growth in both the short and long-run, but real exchange rate and foreign direct investment have a positive significant effect in the long run on the other. As it was estimated that with trade openness more trade is developed in terms of exports and imports which in turn boosts the economy. The study recommends that, there is the need for country to support the domestic industrial development to produce more goods and services.
Gaya Bahasa dalam Cerpen Pilihan Kompas 2018 “Doa Yang Terapung” dan Pemanfataannya dalam Pembelajaran Prosa Fiksi di Sekolah Menengah Atas

This study aims to describe and explain the use of language style in two short stories "Aroma Doa Bilal Jawad" and "Kapotjes and Batu Terapung" in Kompas Choice Short Story 2018 "Floating Prayer". The results of language style analysis in both short stories are also intended to find out its relevance as a teaching material of fictional prose for high school students. This research uses a descriptive qualitative approach. Taking research subjects using purposive sampling technique. The data validity test used source triangulation and theory triangulation. This study uses flow data analysis techniques (flow model of analysis). The results showed that there were 12 types of language style with the frequency of data use in the two short stories as many as 54 data. Mesodiplosys and anaphora were the most dominant styles used, with percentages of 22.2% and 20.37%. Anadiplosys style, metonimies style, and hyperbolic language style are the least used language styles, with a percentage of 1%. The results of the analysis concluded that the Kompas 2018 Choice of Short Story Collection (Floating Prayer), especially in the short story titled "Aroma Doa Bilal Jawad", and "Kapotjes and Batu Yang Terapung" are said to be relevant and can be used as literary learning materials in grade XI high school students
A viable framework for semi-supervised learning on realistic dataset

Semi-supervised Fine-Grained Recognition is a challenging task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch. Recently, this field has witnessed giant leap and many methods have gained great performance. We discover that these existing Semi-supervised Learning (SSL) methods achieve satisfactory performance owe to the exploration of unlabeled data. However, on the realistic large-scale datasets, due to the abovementioned challenges, the improvement of the quality of pseudo-labels requires further research. In this work, we propose Bilateral-Branch Self-Training Framework (BiSTF), a simple yet effective framework to improve existing semi-supervised learning methods on class-imbalanced and domain-shifted fine-grained data. By adjusting stochastic epoch update frequency, BiSTF iteratively retrains a baseline SSL model with a labeled set expanded by selectively adding pseudo-labeled samples from an unlabeled set, where the distribution of pseudo-labeled samples is the same as the labeled data. We show that BiSTF outperforms the existing state-of-the-art SSL algorithm on Semi-iNat dataset. Our code is available at https://github.com/HowieChangchn/BiSTF .
Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Purpose In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well. Design/methodology/approach In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust. Findings The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification. Research limitations/implications Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency. Originality/value The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.
Continuous Integration and Deployment Automation in AWS Cloud Infrastructure

Abstract-The primary objective of this project is to figure out how an application is automatically deployed. To automate the deployment, we'll require a container orchestration platform. Kubernetes is now a widely used container orchestration tool. Users can deploy, scale, and manage containerized applications thanks to Kubernetes' robustness. Continuous Integration and Continuous Delivery (CI/CD) of a project is required in modern development, and the focus is on developing and executing tests on every commit to ensure your test environment is constantly up-to-date. From the integration and testing phases through the delivery and deployment phases, CI/CD brings automation and continuous monitoring of programmers throughout their lifecycle. GitHub Actions is one of the CI/CD technology available in the market. By using GitHub as a source code management tool, it's best to use GitHub Actions as a continuous integration and delivery solution because it's supplied by GitHub. This does not require any setting or setup. As a result, incorporating these tools will speed up the deployment of an application. Keywords- GitHub Actions, Amazon Web Services, Continuous Integration and Continuous Deployment, Kubernetes, Workflows.
An Encryption Scheme using Dynamic Keys and Stream Ciphers for Embedded Devices

Security in embedded devices can be challenging, due to limited available resources, including processing power, memory and energy autonomy. Embedded security solutions should have a low computational complexity and minimum memory requirements, while at the same time these must not reflect in the mechanism's efficiency. This paper describes a lightweight scheme for Data in Transit Encryption (DiTE) using dynamic keys, designed for embedded devices. The scheme is based on the RC4 and ChaCha ciphers and uses communication channel characteristics, and especially the RSSi, to generate the encryption keys. The proposed algorithm provides an enhanced security level, due to the non static encryption keys, in both physical and transport level.
Research on the Design of English Comprehensive Learning Activities Based on Deep Learning

To explore the application ways and effects of in-depth learning in English Teaching for Non-English Majors in Colleges and universities, 419 students are selected for the experiment, 205 students from grade 2018 were used as the reference group and 214 students from grade 2019 were used as the observation group to observe the effect of examination-oriented education and the impact of quality through in-depth learning. The performance of students of grade 2019 in examination-oriented education performance data and quality education performance data was significantly better than that of students of grade 2018, T &lt; 10.000, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.01</mn> </math> , which was statistically significant, Finally, it is found that the intelligent deep learning algorithm is applied to the analysis of the knowledge structure of non-English students’ English papers, and the application scenarios of the analysis results are widely developed, which is helpful to improve the English teaching effect of non-English students, improve students’ learning efficiency and reduce students’ pressure.
Improving the quality of mathematical education of pupils: diagnostics and analytics

Abstract The article considers the problem of the quality of mathematical school education in the context of pupils’ ability to apply the acquired knowledge of mathematics to solve practical problems. It is established that this ability is a clear understanding and awareness of the role of mathematical knowledge in the modern world. It is manifested in the ability to explain natural and scientific phenomena, to draw sound conclusions, to understand the impact of science on human life. The results of a study in which 1849 pupils of Kyiv schools took part are presented. Their ability to apply mathematical knowledge to solve life problems has been established. Examples of mathematical problems developed for testing by specialists of the Ukrainian Center for Educational Quality Assessment are given. The typical pupils’ mistakes, as well as the most successful solutions are analyzed. Pedagogical recommendations for improving the quality of mathematics education of pupils in the context of the reform of the “New Ukrainian School” are given.
Evaluation of Mobile Augmented Reality Hearing-Impaired Museum Visitors Engagement Instrument

Mobile Augmented Reality has come a long way since its inception as a multimedia system during the last few decades. From the concept of an Augmented Reality experience to actual smartphone applications, it has come a long way. Researchers have chosen to leverage the concept of engagement in the construction of Mobile Augmented Reality applications in order to boost museum visitors' engagement and provide a more effective learning environment. The majority of Mobile Augmented Reality applications, on the other hand, were designed with normal hearing visitors in mind, while hearing-impaired visitors are given less attention. Those with hearing difficulties have an unfavourable experience as a result of this, and are unsatisfied with their visit. The elements of Mobile Augmented Reality aimed at engaging hearing-impaired museum visitors are determined in this study. The next step was to construct a conceptual model, which was then validated through an expert review. In investigating any flaws of the instrument among hearing-impaired museum visitors, a pilot study was conducted to improve the items and determine their level of reliability. Participants in the study were hearing-impaired who visited one of Iraq's ancient museums. This study will focus on evaluating the prototype to see how effective it is at engaging hearing-impaired museum visitors in the near future.
Scalable and Easy-to-Use NIME Networking

The \Omega protocol extends the real-time messaging features of Open Sound Control with new capabilities including named services, discovery, clock synchronization and timed messages, reliable message transmission, and publish/subscribe capabilities. Recent work has extended \Omega with a light-weight protocol to extend \Omega capabilities to devices that lack a full implementation of TCP/IP. The new protocol, \Omega-lite enables connectivity with small microcontrollers over WiFi, web browsers over WebSockets, and even with threads that communicate through shared memory. \Omega-lite makes a direct connection to a single \Omega host process and uses the host to provide service discovery and message routing. By off-loading functions to the host, \Omega-lite is much simpler to implement and adaptable to many different transports and languages.
North and Standard Estonian

Abstract Estonian belongs to the southern branch of the Finnic languages. Standard Estonian, now the sole official state language of Estonia, was developed mainly on the basis of North Estonian dialects. Estonian has been in contact with many languages and has especially been influenced by German. It has several features that are spread in the Circum-Baltic area. Estonian phonetics and phonology are characterized by three quantity degrees, palatalization, and lack of vowel harmony. The morphology combines agglutinative features with fusional. Existential, possessive, experiential, and resultative sentences display non-canonical subjects. Differential subject and object marking are conditioned by various semantic and syntactic features of the sentence. In addition to inherited and borrowed stems, Estonian language planning has created many completely new word stems.
Neural network adaption for depth sensor replication

Abstract In recent years, various depth sensors that are small enough to be used with mobile hardware have been introduced. They provide important information for use cases like 3D reconstruction or in the context of augmented reality where tracking and camera data alone would be insufficient. However, depth sensors may not always be available due to hardware limitations or when simulating augmented reality applications for prototyping purposes. In these cases, different approaches like stereo matching or depth estimation using neural networks may provide a viable alternative. In this paper, we therefore explore the imitation of depth sensors using deep neural networks. For this, we use a state-of-the-art network for depth estimation and adapt it in order to mimic a Structure Sensor as well as an iPad LiDAR sensor. We evaluate the network which was pre-trained on NYU V2 directly as well as several variations where transfer learning is applied in order to adapt the network to different depth sensors while using various data preprocessing and augmentation techniques. We show that a transfer learning approach together with appropriate data processing can enable an accurate modeling of the respective depth sensors.
Do students have the means to learn during the coronavirus pandemic? Student demands for distance learning in a suddenly digital landscape

Across the globe, 2020 terms began under conditions incited by the corona pandemic. Within a relatively short amount of time, universities started to develop and implement online courses for distance learning. The current study is about an online survey at a German university investigating the unique circumstances under which students began the digital 2020 summer term. Of approximate-ly 38 500 students, N = 5563 students from across all institutional faculties took part in the survey. Results indicate how well students are equipped with devices for digital learning, what kind of experiences they have already made with online learning, and how competent they reported feeling regarding digital learning. The study provides important insights into digital higher education during the exceptional pandemic situation. The results are intended to feed into student counselling systems via support by way of access to devices or courses regarding digital skills, or through counselling for students with special social burdens.
Research on the Fusion of Hybrid Fuzzy Clustering Algorithm and Computer Automatic Test Paper Composition Algorithm

In order to improve the effect of intelligent automatic test paper composition, this paper combines the hybrid fuzzy clustering algorithm to study the computer automatic test paper composition algorithm. In this paper, a computer automatic test paper composition system based on hybrid fuzzy clustering algorithm is constructed. Moreover, the hybrid fuzzy clustering method used in this paper is used as the basic algorithm of the system, and the algorithm is improved according to the actual needs of intelligent paper composition. In addition, this paper uses an intelligent algorithm to input the relevant constraint parameters and combines the original parameters to select the most suitable test questions from the database and combine them into test papers. Finally, this paper constructs the system structure based on the requirements of intelligent test paper composition. The experimental research shows that the computer automatic test paper composition system based on the hybrid fuzzy clustering algorithm proposed in this paper has a good test paper composition function, which can effectively promote the progress of the intelligent examination mode in colleges and universities.