Reviewerly/exHarmony huggingface dataset:
Sykehusinnleggelser med covid-19 ‒ en sammenligning av ulike datakilder

Three different data sources exist for monitoring COVID-19-associated hospitalisations in Norway: The Directorate of Health, the Norwegian Intensive Care and Pandemic Registry (NIPaR), and the linking of the Norwegian Patient Registry (NPR) and the Norwegian Surveillance System for Communicable Diseases (MSIS). A comparison of results from different data sources is important to increase understanding of the data and to further optimise current and future surveillance. We compared results from the three data sources from March to June 2020.We analysed the number of new admissions, as well as the total number of hospitalised patients and those on ventilatory support, reported per day and by regional health authority. The analysis was descriptive.The cumulative number of new admissions according to NPR-MSIS (n=1260) was higher than NIPaR (n=1153). The discrepancy was high early in the epidemic (93 as of 29 March). The trend in the number of hospitalised patients was similar for all three sources throughout the study period. NPR-MSIS overestimated the number of hospitalised patients on ventilatory support.The discrepancy in new admissions between NIPaR and NPR-MSIS is primarily due to missing registrations for some patients admitted before NIPaR became operational. Basic information retrieved daily by the Directorate of Health give comparable results to more comprehensive daily information retrieval undertaken in NIPaR and NPR-MSIS, adjusted retrospectively. Further analysis is necessary regarding whether NIPaR and NPR-MSIS provide timely data and function as required in an emergency preparedness situation.
PARTITIONING KNOWN ENVIRONMENTS FOR MULTI-ROBOT TASK ALLOCATION USING GENETIC ALGORITHMS

A common challenge facing emergency services, particularly in response to fires and/or earthquakes, is the location and subsequent extraction of people from hazardous buildings in a timely manner. This usually requires emergency service workers to enter the building and put their own lives at risk, even when there may be no people to extract. However, given recent advances in autonomous robotics, drones are expected to help humans in tasks such as search and rescue, and similar tasks, where coverage and time are key parameters. The aim is to complete a comprehensive search of the environment as quickly as feasible. Using multiple drones rather than a single drone can reduce search time, although performance can be poor if the searching is non-coordinated. Therefore, partitioning a terrain is important in order to effectively distribute the drone search work so that good coverage can be achieved in a reasonable amount of time and redundant searching is eliminated. In this paper a novel Square Based Terrain Partitioning (SBTP) algorithm is presented using a genetic algorithm to partition a known environment into multiple domains in a multi-robot exploration system. In addition, a second genetic algorithm is presented to allocate the domain search workload such that, given a certain number of drones, the overall search time is minimized.
THE INFLUENCE OF YOUTUBE ON ENGLISH VOCABULARY FOR CHILDREN 7-8 YEARS OLD

In the new norm era, children learn online from home. YouTube is used as a learning media to increase interest in learning English vocabulary. This qualitative descriptive study describes; how does YouTube affect the acquisition of English vocabulary and what is the impact of YouTube on the acquisition of English vocabulary. The study population was 7-8-year-old children. The research data is watching short English film and singing very simple English song from YouTube. The results of this study indicate that it is easier for children to pronounce and remember English words
Abnormal Activity Detection and Notification Platform for Real-Time Ad Hoc Network

As aging society era is getting near, number of elders who live alone is increasing. These people often need special care. Due to this reason, we propose Abnormal Activity Detection and Notification Platform (AADN) for Real-Time Ad Hoc Network which can help taking care of these people. The proposed platform relies on human tracking using cameras that are installed in different rooms inside the house. AADN will take as input images from the cameras to process and output activity in the form of human pose and objects with their relative distant to the detected human. Relationship Degree of Human Object Interaction (RD-HOI) will be analyzed every minute and be used to distinguish abnormal behavior by means of decision tree. In addition, activities will be used to generate routine behavior log and AADN will notify the person in charge of taking care of the subject if the detected activity differs from the routine. The proposed platform can achieve human pose accuracy of up to 99.66% by using COCO with VGG-NB model and can correctly identify object 68% of the time. Our experiments showed that AADN could notify abnormal activity by using RD-HOI when human and harmful objects were clearly visible in the picture and could correctly notify abnormal activity when time spent in a certain activity differed from the routine by a certain threshold given sufficient amount of data.
Professional and personal readiness of a teacher as a component of an inclusive culture

Currently, inclusive education is becoming widespread as equal access to education for all children, regardless of starting opportunities. In this regard, an important task of an educational organization is the formation of an inclusive culture that allows building subject-subject relations between all participants in educational relations based on humanism and the acceptance of the value of each child. The professional and personal readiness of the teacher is a component of the inclusive culture of the educational organization and the inclusive culture of the personality of the teacher.
Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension

In multi-turn dialog, utterances do not always take the full form of sentences (Carbonell 1983), which naturally makes understanding the dialog context more difficult. However, it is essential to fully grasp the dialog context to generate a reasonable response. Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog. Enlightened by the multi-task learning scheme, we propose a joint framework that unifies these two tasks, sharing the same encoder to extract the common and task-invariant features with different decoders to learn task-specific features. To better fusing information from the question and the dialog history in the encoding part, we propose to augment the Transformer architecture with a memory updater, which is designed to selectively store and update the history dialog information so as to support downstream tasks. For the experiment, we employ human annotators to write and examine a large-scale dialog reading comprehension dataset. Extensive experiments are conducted on this dataset, and the results show that the proposed model brings substantial improvements over several strong baselines on both tasks. In this way, we demonstrate that reasoning can indeed help better response generation and vice versa. We release our large-scale dataset for further research.
3D Large-Pose Face Alignment Method Based on the Truncated Alexnet Cascade Network

Aiming at the low accuracy of large-pose face alignment, a cascade network based on truncated Alexnet is designed and implemented in the paper. The parallel convolution pooling layers are added for concatenating parallel results in the original deep convolution neural network, which improves the accuracy of the output. Sending the intermediate parameter which is the result of each iteration into CNN and iterating repeatedly to optimize the pose parameter in order to get more accurate results of face alignment. To verify the effectiveness of this method, this paper tests on the AFLW and AFLW2000-3D datasets. Experiments on datasets show that the normalized average error of this method is 5.00% and 5.27%. Compared with 3DDFA, which is a current popular algorithm, the accuracy is improved by 0.60% and 0.15%, respectively.
Speech Synthesis System for the Holy Quran Recitation

This paper aims to develop a Text-To-Speech (TTS) synthesis system for the holly quran recitation, to properly helps reciters and facilitates its use. In this work, the unit selection method is adopted and improved to reach a good speech quality. The proposed approach consists mainly of two steps. In the first one, an Expert System (ES) module is integrated by employing Arabic, Quran language, phonetic and phonological features. This part was considered as a preselection to optimize the synthesis algorithm's speed. The second step is the final selection of units by minimizing a concatenation cost function and a forward-backward dynamic programming search. The system is evaluated by native and non-native Arabic speakers. The results show that the goal of a correct Quran recitation by respecting its reading rules was reached, with 97 % of speech intelligibility and 72.13% of naturalness
A Two-stage Strategy to Optimize Energy Consumption for Latency-critical Workload under QoS Constraint

Data centers afford huge energy costs. Saving energy while providing efficient quality of service (Qos) is the goal pursued by data centers. But this is a challenging issue. To ensure the Qos of latency-critical applications, data centers always schedule processors to run at higher frequencies. The continuous high frequency operation will cause great energy waste. Modern processors are equipped with dynamic voltage and frequency scaling (DVFS) technology, which allows the processor to run at every frequency levels it supports, so we focus on how to use DVFS to trade-off between energy and Qos. In this paper, we propose a two-stage strategy based on DVFS to dynamically scaling the CPU frequency during latency-critical workload execution, aimed at minimizing the energy consumption for latency-critical workload which is under the Qos constraint. The two-stage strategy includes a static stage and dynamic stage, which are worked together to determine the optimal frequency for running workload. The static stage uses a well designed heuristic algorithm to determine the frequency-load matches under Qos constraint, while the dynamic stage leverages a threshold method to determine whether to adjust the pre-set frequency. We evaluate the two-stage strategy in terms of Qos and energy saving on the cloudsuite benchmark, and compares the two metrics with the-state-of art Ondemand. Results show that our strategy is superior to Ondemand for energy saving, improving more than 13%.
Partial programs in access to the social values: technology that changes preschool education

Abstract The article describes the current state and current issues of education in Ukraine. The relationship between the tasks of reforming general secondary education and the goal of preschool education in the context of continuity between the links of preschool and primary education has been established. The elaboration of the problem of education and the value orientations by both foreign and domestic researchers is analyzed. The importance of education of social values is revealed. The peculiarities of partial programs, their difference from complex ones are clarified. Partial programs for the education of social values in preschool institutions are described: «Aflatot», «The boundless world of playing with LEGO», «Learning to live together» and programs for the formation of computer literacy in preschool children («Computer literacy for children», «Media World for Preschoolers», «Fairy Tales and Paints»). The requirements for the education of social values of preschool children have been updated and the ways to implement the above programs have been presented. Each program identifies the purpose, key elements, principles and results of implementation. The connection between the principles of personality-oriented learning and the tasks of partial programs «Aflatot», «Boundless world of playing with LEGO», «Learning to live together», «Computer literacy for kids», «Media education for preschoolers», «Fairy tales and paints» is revealed. The article presents the practical aspect of using the programs: forms of work, topics of classes, description of materials and clarity for classes, etc.
Analisis Sensitivitas pada Model Matematika Transmisi Pengguna Narkoba dengan Faktor Edukasi

Drugs have a bad effect on the users which is mental disorders and health problems even causing death. Cases of drug users spread can be formulated with a mathematical approach through the concept of mathematical modeling. This article discusses the mathematical model of SURS type drug users spread. Model development by adding educational factors to each population class. Furthermore, the basic reproduction number ( ) is constructed to model the threshold value of the spread of drug addicts. The basic reproduction number is determined by using a Next Generation Matrix approach. The next step is to do a sensitivity analysis to determine the parameters that most influence the spread of drug addicts. Based on the analysis results, the parameters β and indicate the most dominant sensitivity index to the basic reproduction numbers. At the end of this article, a simulation is carried out to show the effect of changing parameters on the population of drug addicts.
Tightness of the Suffix Keyed Sponge Bound

Generic attacks are a vital ingredient in the evaluation of the tightness of security proofs. In this paper, we evaluate the tightness of the suffix keyed sponge (SuKS) bound. As its name suggests, SuKS is a sponge-based construction that absorbs the key after absorbing the data, but before producing an output. This absorption of the key can be done via an easy to invert operation, like an XOR, or a hard to invert operation, like a PRF. Using SuKS with a hard to invert absorption provides benefits with respect to its resistance against side-channel attacks, and such a construction is used as part of the authenticated encryption scheme Isap. We derive two key recovery attacks against SuKS with easy to invert key absorption, and a forgery in case of hard to invert key absorption. The attacks closely match the terms in the PRF security bound of SuKS by Dobraunig and Mennink, ToSC 2019(4), and therewith show that these terms are justified, even if the function used to absorb the key is a PRF, and regardless of whether SuKS is used as a PRF or a MAC.
Online Action Recognition

Recognition in planning seeks to find agent intentions, goals or activities given a set of observations and a knowledge library (e.g. goal states, plans or domain theories). In this work we introduce the problem of Online Action Recognition. It consists in recognizing, in an open world, the planning action that best explains a partially observable state transition from a knowledge library of first-order STRIPS actions, which is initially empty. We frame this as an optimization problem, and propose two algorithms to address it: Action Unification (AU) and Online Action Recognition through Unification (OARU). The former builds on logic unification and generalizes two input actions using weighted partial MaxSAT. The latter looks for an action within the library that explains an observed transition. If there is such action, it generalizes it making use of AU, building in this way an AU hierarchy. Otherwise, OARU inserts a Trivial Grounded Action (TGA) in the library that explains just that transition. We report results on benchmarks from the International Planning Competition and PDDLGym, where OARU recognizes actions accurately with respect to expert knowledge, and shows real-time performance.
Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum

Today, such a branch of science as «artificial intelligence» is booming in the world. Systems built on the basis of artificial intelligence methods have the ability to perform functions that are traditionally considered the prerogative of man. Artificial intelligence has a wide range of research areas. One such area is machine learning. This article discusses the algorithms of one of the approaches of machine learning – reinforcement learning (RL), according to which a lot of research and development has been carried out over the past seven years. Development and research on this approach is mainly carried out to solve problems in Atari 2600 games or in other similar ones. In this article, reinforcement training will be applied to one of the dynamic objects – an inverted pendulum. As a model of this object, we consider a model of an inverted pendulum on a cart taken from the Gym library, which contains many models that are used to test and analyze reinforcement learning algorithms. The article describes the implementation and study of two algorithms from this approach, Deep Q-learning and Double Deep Q-learning. As a result, training, testing and training time graphs for each algorithm are presented, on the basis of which it is concluded that it is desirable to use the Double Deep Q-learning algorithm, because the training time is approximately 2 minutes and provides the best control for the model of an inverted pendulum on a cart.
Analysis of Steganographic on Digital Evidence using General Computer Forensic Investigation Model Framework

Steganography is one of the anti-forensic techniques used by criminals to hide information in other messages which can cause problems in the investigation process and difficulties in obtaining original information evidence on the digital crime.Digital forensic analysts are required ability to find and extract the messages that have been inserted by using proper tools.The purpose of this research is to analyze the hidden digital evidence using steganography techniques.This research uses the static forensics method by applying five stages in the Generic Forensics Investigation Model framework, namely pre-process, acquisition & preservation, analysis, presentation, and postprocess as well as extracting files that have been infiltrated based on case scenarios involving digital crime.The tools used are FTK Imager, Autopsy, WinHex, Hiderman, and StegSpy.The results on the steganographic file insertion experiment of 20 files indicate that StegSpy and Hiderman are effective on the steganographic analysis of digital evidence.StegSpy can detect the presence of secret messages with 85% success rate.The extraction process using Hiderman for 18 files with containing steganographic messages had 100% successful.
Preferred Explanations for Ontology-Mediated Queries under Existential Rules

Recently, explanations for query answers under existential rules have been investigated, where an explanation is an inclusion-minimal subset of a given database that, together with the ontology, entails the query. In this paper, we take a step further and study explanations under different minimality criteria. In particular, we first study cardinality-minimal explanations and hence focus on deriving explanations of minimum size. We then study a more general preference order induced by a weight distribution. We assume that every database fact is annotated with a (penalization) weight, and we are interested in explanations with minimum overall weight. For both preference orders, we study a variety of explanation problems, such as recognizing a preferred explanation, all preferred explanations, a relevant or necessary fact, and the existence of a preferred explanation not containing forbidden sets of facts. We provide a detailed complexity analysis for all the aforementioned problems, thereby providing a more complete picture for explaining query answers under existential rules.
Methodological efficiency of using virtual and augmented reality in the educational process

This article presents the results of an experiment on creating an electronic textbook for universities and programs of additional professional education using virtual and augmented reality technologies and evaluating the results of testing the textbook on the basis of the State University of Management. The topic of the electronic textbook concerns the organization of the security service of accommodation facilities. The authors provide a description of the electronic textbook, its structure, main interactive elements, the progress of work, talk about the difficulties that they encountered. The working group on the creation of the textbook included methodologists, psychologists, specialists from the security services of Moscow hotels and specialists in information technology. Teams of key developers - the State University of Management, the Lotte group of companies, the LLC ITRON limited liability company, and the Rusays Publishing House - worked in partnership. Conclusions are made about the possibilities of using virtual and augmented reality technologies in educational activities. Augmented reality technology is used to create a mobile application that displays educational materials with reference to real-life objects according to special markers. This technology allows you to make the textbook unique for a specific object (office, building, structure, landscape). The results of the training using an electronic participant are presented. The peculiarities of the psychological perception of educational material and the effectiveness of the new format of presentation of the educational environment are noted.
Fast and Accurate Multiple Sequence Alignment with MSAProbs-MPI

Multiple sequence alignment (MSA) is a central step in many bioinformatics and computational biology analyses. Although there exist many methods to perform MSA, most of them fail when dealing with large datasets due to their high computational cost. MSAProbs-MPI is a publicly available tool (http://msaprobs.sourceforge.net) that provides highly accurate results in relatively short runtime thanks to exploiting the hardware resources of multicore clusters. In this chapter, I explain the statistical and biological concepts employed in MSAProbs-MPI to complete the alignments, as well as the high-performance computing techniques used to accelerate it. Moreover, I provide some hints about the configuration parameters that should be used to guarantee high-performance executions.