Reviewerly/exHarmony huggingface dataset:
Presenting Simultaneous Translation in Limited Space.

Some methods of automatic simultaneous translation of a long-form speech allow revisions of outputs, trading accuracy for low latency. Deploying these systems for users faces the problem of presenting subtitles in a limited space, such as two lines on a television screen. The subtitles must be shown promptly, incrementally, and with adequate time for reading. We provide an algorithm for subtitling. Furthermore, we propose a way how to estimate the overall usability of the combination of automatic translation and subtitling by measuring the quality, latency, and stability on a test set, and propose an improved measure for translation latency.
Personalized Input-Output Hidden Markov Models for Disease Progression Modeling.

Disease progression models are important computational tools in healthcare and are used for tasks such as improving disease understanding, informing drug discovery, and aiding in patient management. Although many algorithms for time series modeling exist, healthcare applications face particular challenges such as small datasets, medication effects, disease heterogeneity, and a desire for personalized predictions. In this work, we present a disease progression model that addresses these needs by proposing a probabilistic time-series model that captures individualized disease states, personalized medication effects, disease-state medication effects, or any combination thereof. The model builds on the framework of an input-output hidden Markov model where the parameters are learned using a structured variational approximation. To demonstrate the utility of the algorithm, we apply it to both synthetic and real-world datasets. In the synthetic case, we demonstrate the benefits afforded by the proposed model as compared to standard techniques. In the real-world cases, we use two Parkinson9s disease datasets to show improved predictive performance when ground truth is available and clinically relevant insights that are not revealed via classic Markov models when ground truth is not available.
Parallelize Your Python

Let us say you want to empty a tank that is full of water. If you are given one water pipe, you can connected it to the bottom of the tank to let the water out. If you have another water pipe, using both pipes will empty the tank twice as fast. The more water pipes you can connect to the bottom of the tank, the faster you can empty it. A similar concept exists in computing as well. Most of our computers have a multicore processor design, which means that we can process tasks in parallel fashion, like using multiple pipes to drain the tank. This chapter explains how to run your Python program in parallel to reduce the time required to complete various tasks. For smaller projects, the benefit may not seem obvious, but for complicated projects, the gain can be significant. For example, you have a homework assignment that normally requires an hour to run until you can tell whether your code is correct or not. If you find something is wrong, you change your code accordingly, but it will take another hour to find out whether your modification worked. At the end, if you make change the code 24 times, it will take you an entire day to finish one assignment. But if you can run your algorithms in parallel, the running time of your code may shrink significantly.
Development of an App for Monitoring Heart Rate in People Who Practice Regular Physical Activity

During the review of the supporting literature, in the different databases such as Scielo and Scopus, we considered the updated information of the contributions that the authors make about the different mobile applications designed for the practice of physical activities. Therefore, a low-cost app was designed that allows the calculation of heart rate, using a sensor SEN-11574 and an Arduino plate that sends data in real time to the application through a Bluetooth module HC-05, the app has a database where you can record the user's results and export them to an Excel file. For the development, the ADDIE methodology was used, which allowed the structured development of the application. Finally, the implementation of the resource is evaluated using the TAM technology evaluation model. The results found show a high degree of interest in the users towards the developed mobile application, obtaining significant results against reliable and high quality instruments.
HEY!: Exploring Virtual Character Interaction for Immersive Storytelling via Electroencephalography

The Virtual Reality (VR) headset has become potential equipment for immersive storytelling. However, we know limited things about the users when they are experiencing the VR context. Sometimes, the users miss the narration because they are looking around, which makes designing a compelling VR story a challenge. With the advancement of electroencephalography (EEG) in VR, the story’s rhythm or structure could dynamically change based on the audience’s brain waves to create a personal dramatic moment. In this paper, we conduct a preliminary study to investigate the potential use of a consumer-level brainwave headset, and attempts to explore virtual character interaction to enhance immersive storytelling.
Improved Multi-Population Differential Evolution for Large-Scale Global Optimization

Differential evolution (DE) is an efficient population-based search algorithm with good robustness, however, it is challenged to deal with high-dimensional problems. In this paper, we propose an improved multi-population differential evolution with best-and-current mutation strategy (mDE-bcM). The population is divided into three subpopulations based on the fitness values, each of subpopulations uses different mutation strategy. After crossover, mutation and selection, all subpopulations are updated based on the new fitness values of their individuals. An improved mutation strategy is proposed, which uses a new approach to generate base vector that is composed of the best individual and current individual. The performance of mDE-bcM is evaluated on a set of 19 large-scale continuous optimization problems, a comparative study is carried out with other state-of-the-art optimization techniques. The results show that mDE-bcM has a competitive performance compared to the contestant algorithms and better efficiency for large-scale optimization problems.
Evaluation of Embeddings in Medication Domain for Spanish Language Using Joint Natural Language Understanding

Word embeddings have been widely used in Natural Language Processing as the input to neural networks. Such word embeddings can help in the understanding of the final objective and the keywords in a sentence. As such, in this work, we study the impact of different word embeddings trained with general and specific corpora using Joint Natural Language Understanding in a Spanish medication domain. We generate data using templates for training the model. The model is used for intent detection and slot-filling. We compare word2vec and fastText as word embeddings and ELMo and BERT as language models. We use three different corpora to train the embeddings: the training data generated for this scenario, the Spanish Wikipedia as general domain and the Spanish drug database as specialized data. The best result was obtained with word2vec continuous bag of words model learned with Spanish Wikipedia, obtaining a 71.77% F1-score for intent detection, an intent accuracy of 69.37% and a 74.36% F1-score for slot-filling.
Sample Size Recalculation Based on Conditional Power

This chapter describes methods for sample size recalculation in adaptive two-stage designs which are based on conditional power arguments. The cases of using for sample size recalculation the observed interim treatment group difference, the initially specified treatment group difference, or the observed interim treatment group difference together with prior information are considered. Operating characteristics of the resulting procedures are discussed.
Classification Between Machine Translated Text and Original Text By Part Of Speech Tagging Representation

Classification between machine-translated text and original text are often tokenized on vocabulary of the corpi. With N-grams larger than uni-gram, one can create a model that estimates a decision boundary based on word frequency probability distribution; however, this approach is exponentially expensive because of high dimensionality and sparsity. Instead, we let samples of the corpi be represented by part-of-speech tagging which is significantly less vocabulary. With less trigram permutations, we can create a model with its tri-gram frequency probability distribution. In this paper, we explore less conventional ways of approaching techniques for handling documents, dictionaries, and the likes.
Filling Algorithm for the Missing Value of Network Stability Monitoring Results Based on Big Data

Abstract In the research field of network stability monitoring based on big data, there are some disadvantages. For example, the processing method of the missing value of results influences execution effect, interfere the final filling effect and can only be applied in small data set. Therefore, a missing value filling algorithm for network stability monitoring results based on big data is proposed. The evaluation index of network stability monitoring based on big data is selected, and data missing mode and missing value filling mechanism are analyzed. The filling algorithm for the missing value of results based on big data is designed, the missing value filling parameter framework is constructed, the missing value filling parameters are processed, and the filling of the missing value of network stability detection results is completed. Compared with the traditional algorithm, the experimental results show that the efficiency of the designed missing value filling algorithm is 70.3% higher than that of the traditional filling algorithm, and the accuracy rate is far higher than the traditional method.
Robust Content-Based Recommendation Distribution System with Gaussian Mixture Model

Recommendation systems play an very important role in boosting purchasing consumption for many manufacturers by helping consumers find the most appropriate items. Furthermore, there is quite a range of recommendation algorithms that can be efficient; however, a content-based algorithm is always the most popular, powerful, and productive method taken at the begin time of any project. In the negative aspect, somehow content-based algorithm results accuracy is still a concern that correlates to probabilistic similarity. In addition, the similarity calculation method is another crucial that affect the accuracy of content-based recommendation in probabilistic problems. Face with these problems, we propose a new content-based recommendation based on the Gaussian mixture model to improve the accuracy with more sensitive results for probabilistic recommendation problems. Our proposed method experimented in a liquor dataset including six main flavor taste, liquor main taste tags, and some other criteria. The method clusters n liquor records relied on n vectors of six dimensions into k group ( $$k < n$$ ) before applying a formula to sort the results. Compared our proposed algorithm with two other popular models on the above dataset, the accuracy of the experimental results not only outweighs the comparison to those of two other models but also attain a very speedy response time in real-life applications.
Authentication on Interpolated Subsampled Based Image Steganography Exploiting Secret Sharing

In this paper, we developed a secure secret sharing technique with authentication through steganographic scheme using the interpolated sub-sampling methodology. Based on a (k, n)-threshold method, a secret image is shared by n shadow images which are embedded within n subsampled interpolated cover images. On the other hand, k shadow images $$(k\le n)$$ can be used to recover the secret image. The proposed scheme uses a secure hash algorithm (SHA 12) to enhance authentication that prevents dishonest participants from cheating.
A Review on Mobile Game Learning Applications Trends

Technology has become the most priority in everyday life for everybody.It has been used for variety of uses such as for military, manufacturing, medical and education.Nowadays, young generation or youth has been exposed drastically with the latest technology and most of them are very high-tech students.Besides that, mobile technology has been grown rapidly and from this experiencing of mobile technology, the prices for mobile devices has been dropped or reduced which make the most people become capable to own a mobile device.As we known, the mobile device has provided various function and application.One of them is a games application that allows the user to have a learning experience.The game application has been used for learning purposes in education which included in various types of games and different learning content.This paper aim to systematically review in this topic of mobile game application for learning.A total of 42 papers were identified and 10 papers were included for this review.The review will be focusing on the acceptance of the mobile game application for learning.The reviewed papers have been selected using different search engines and the papers have been filtered to meet the objective of this paper.Most of the reviewed paper show positive effect on the usage of the game application for learning and improve their learning process.Mobile game application which has been used for learning is not a new field as the paper reviewed from this study is from 2012 until 2019.It will keep continue as mobile technology has become necessity in everyday life.
Analysis of Articles Related to Electronic Books: A Descriptive Content Analysis Study in Turkey Context

The present study aimed to review the articles published in Turkey on electronic books. In the study, descriptive content analysis method was employed. In the study, national databases such as Ulakbim-UVT, Asos Index, Turkish Education Index (TEI) and international databases such as ERIC, DOAJ, EBSCO, Google Scholar and past issues of educational and social sciences journals in Web of Science (WOS) as of 2017 were searched with “e-book, electronic book, z-book, digital book" keywords both in English and Turkish. Forty-three articles that were accessed in the search were analyzed using the article classification form. The data were analyzed using frequencies and percentages. In the present study research, it was determined that the majority of the articles on e-books were on the "determination of perception, attitude and views" in e-books, the highest number of publications was published in 2013 and 2014, and the studies were mostly authored by a single scientist. Furthermore, it was determined that the authors preferred the “literature review” method, most articles lacked a “research question” in most studies where a sample was set, the data were collected from “undergraduate” students and “random sampling” was preferred. It was also determined that the "frequency" and "percentage" techniques were the most commonly used techniques in data analysis.
TRANSFORMATION OF SOCIAL-PEDAGOGICAL PARTNERSHIP IN PREPARATION OF FUTURE TEACHERS

The article updates the problems related to the vocational training of future teachers through the example of social-pedagogical partnership. The urgency and necessity of introducing partnership forms of organization of the educational process are emphasized. A theoretical analysis of the views of scientists on the problem of quality of education, which is conditioned by the pedagogy of partnership, is given, the most important elements of the phenomenon under study are highlighted. The experience of European countries in the formation and formation of social-pedagogical partnership as a factor of improving the quality of vocational-pedagogical training on the example of future teachers' training is presented in detail. On the basis of elements of comparative analysis with the experience of foreign countries, the introduction of their own ideas in the preparation of future teachers is presented. It has been found that through the use of one's own ideas of social-pedagogical partnership in education it is possible to overcome many problems in this important sphere and to require the creation of a new model of functioning of organizations providing training. The effectiveness of social-pedagogical partnerships with organizations of different levels will ensure the quality of training of pedagogical staff. The article determines that social-pedagogical partnership in the preparation of future teachers envisages ensuring a high degree of competitiveness of graduates, their professional mobility, readiness for innovation. The study does not exhaust all aspects of the problem of pedagogy of social-pedagogical partnership in the educational environment when preparing teachers. Key words: quality of education; teacher; pedagogical partnership; vocational training; future teacher; labor market; social partnership.
Semi-Supervised Learning of Mutually Accelerated Multi-Contrast MRI Synthesis without Fully-Sampled Ground-Truths.

This study proposes a novel semi-supervised learning framework for mutually accelerated multi-contrast MRI synthesis that recovers high-quality images without demanding large training sets of costly fully-sampled source or ground-truth target images. The proposed method presents a selective loss function expressed only on a subset of the acquired k-space coefficients and further leverages randomized sampling patterns across training subjects to effectively learn relationships among acquired and nonacquired k-space coefficients at all locations. Comprehensive experiments performed on multi-contrast brain images clearly demonstrate that the proposed method maintains equivalent performance to the gold-standard method based on fully-supervised training while alleviating undesirable reliance of the current synthesis methods on large-scale fully-sampled MRI acquisitions.
TAKING THE ROLE OF THE OTHER: HOW PERSONAS IN A DESIGN-THINKING BASED WORKSHOP HELP TO DEVELOP REQUIREMENTS FOR A DATA-BASED STUDENT ASSISTANT

Information about the paper titled "TAKING THE ROLE OF THE OTHER: HOW PERSONAS IN A DESIGN-THINKING BASED WORKSHOP HELP TO DEVELOP REQUIREMENTS FOR A DATA-BASED STUDENT ASSISTANT" at IATED Digital Library
PROMOTING ICT AND DIGITAL EDUCATION IN PUBLIC SCHOOLS IN LAGOS, NIGERIA USING AFFORDABLE CLEAN ENERGY SOLUTIONS

Goal 4 of the United Nations Sustainable Development Goals (SDGs) aims to ensure inclusive and quality education for all including the promotion of lifelong learning. Hence the goal seeks to promote the development of the human person through education. This development is best driven by technology through ICT and digital education. The challenges posed by COVID-19 have further made virtual education which is dependent on the acquisition of ICT and digital skills very imperative. Incidentally, technology-based education is dependent on the availability of energy which cannot be guaranteed in a developing country context such as Nigeria. Hence, this study is aimed at examining the energy supply situation in selected public secondary schools in Lagos, Nigeria, and their coping strategies for ICT and digital education. The study adopted the qualitative approach with data obtained from observation and interviews. The study found that the schools relied mostly on conventional electricity supply from the national grid which was generally epileptic and unreliable. The schools had standby electricity generators to support mains supply and this is a major source of pollution and carbon emissions. The study thus recommended the use of renewable clean energy systems such as solar electricity through photovoltaic cells to power the equipment used for ICT and digital education.
Current way of using mobile touch technologies in schools

Modern means of information and communication technology are nowadays an integral part of the learning process inside and outside the school. In most cases, however, this technique is considered to be a set of classical computer assemblies located in a specialized classroom, or a computerized teaching center with central projection. Over the past few years, many new types of devices have appeared on the market that have significantly affected the usual use of computing. One of these devices is mobile touch devices, for which we usually consider a tablet and a mobile phone equipped with an operating system with the possibility of installing third-party applications. If we combine ICT with education, we dare say that the use of ICT resources is an essential part of the teacher's work. The ability to use ICT by a teacher is referred to as competence, sometimes digital competence, competence in using ICT in teaching, and so on. Although these competencies are described in general terms in the currently used documents, the focus can be on the perception of ICT as a classical computer. However, using a tablet has its own specifics and is in many ways different than the use of conventional computers.
Perceptions of Selected Stakeholders on the Use of Tablets for University Teaching

The purpose of this study was to examine the perceptions of lecturers and managers as selected stakeholders on the use of tablet computers in teaching at a South African university. The study adopted a mixed method approach. A sample of 14 lecturers from a population of 25 voluntarily participated in the survey. Additionally, five lecturers and nine managers were interviewed. Findings showed that both stakeholders had viewed tablets as an effective teaching tool. Furthermore, teaching skills of the lecturers were enhanced due to the use of tablets and it made completion of their work faster.
Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews

Sentiment analysis is an important task in the field ofNature Language Processing (NLP), in which users' feedbackdata on a specific issue are evaluated and analyzed. Manydeep learning models have been proposed to tackle this task, including the recently-introduced Bidirectional Encoder Rep-resentations from Transformers (BERT) model. In this paper,we experiment with two BERT fine-tuning methods for thesentiment analysis task on datasets of Vietnamese reviews: 1) a method that uses only the [CLS] token as the input for anattached feed-forward neural network, and 2) another methodin which all BERT output vectors are used as the input forclassification. Experimental results on two datasets show thatmodels using BERT slightly outperform other models usingGloVe and FastText. Also, regarding the datasets employed inthis study, our proposed BERT fine-tuning method produces amodel with better performance than the original BERT fine-tuning method.
How Can We Broaden and Diversify Humanities Knowledge Translation?

Humanities research is extremely relevant for the challenges and opportunities of the 21st century. But despite the growing corpus of humanities research, there are few explicit translation mechanisms from academic work to broader communities. Building off of such a premise, this paper looks at where knowledge translation is occurring in other fields and what lessons might be learned for the wider and more efficient circulation of humanities work.
Feedforward Neural Networks

In this chapter, we will cover the most generic version of neural networks, feedforward neural networks. Feedforward neural networks are a group of artificial neural networks in which the connections between neurons do not form a cycle. Connections between neurons are unidirectional and move in only forward direction from input layer through hidden layers and to output later. In other words, the reason these networks are called feedforward is that the flow of information takes place in the forward direction.