Reviewerly/exHarmony huggingface dataset:
Models for Teaching Information Literacy: A Comparative Review of the Top Six Models

Information literacy is a necessary prerequisite for achieving educational, occupational, economic and personal goals in society. It has also become an essential skill to achieve individual goals. Many models have been designed for developing these crucial skills but few published scholarly studies have explored their effectiveness and none have compared them. The present paper reviewed the literature to select the most commonly used models for teaching information literacy, and analysed the reported strengths and weaknesses of the top six models. The top six models are the Information Search Process, the Big6 information skills, the Seven Pillars of Information Literacy, the Pathways to Knowledge, the PLUS model, and the Seven Faces of Information Literacy. The Information Search Process knowledge model stood out as the most useful prescriptive model especially through its ability across disciplines, beginning at the elementary educational level and extensively university level. The paper also highlights the values of integrating information literacy into curriculum development to enhance students’ learning experience and to develop skills and abilities necessary for the rapidly changing information environment of the twenty-first century.&#x0D;
Autocalibration method for scanning electron microscope using affine camera model

This paper deals with the task of autocalibration of scanning electron microscope (SEM), which is a technique allowing to compute camera motion and intrinsic parameters. In contrast to classical calibration, which implies the use of a calibration object and is known to be a tedious and rigid operation, auto- or selfcalibration is performed directly on the images acquired for the visual task. As autocalibration represents an optimization problem, all the steps contributing to the success of the algorithm are presented: formulation of the cost function incorporating metric constraints, definition of bounds, regularization, and optimization algorithm. The presented method allows full estimation of camera matrices for all views in the sequence. It was validated on virtual images as well as on real SEM images (pollen grains, cutting tools, etc.). The results show a good convergence range and low execution time, notably compared to classical methods, and even more in the context of the calibration of SEM.
Bayesian structured antedependence model proposals for longitudinal data

An important problem in Statistics is the study of longitudinal data taking into account the effect of other explanatory variables, such as treatments and time and, simultaneously, the incorporation into the model of the time dependence between observations on the same individual. The latter is specially relevant in the case of nonstationary correlations, and nonconstant variances for the different time point at which measurements are taken. Antedependence models constitute a well known commonly used set of models that can accommodate this behaviour. These covariance models can include too many parameters and estimation can be a complicated optimization problem requiring the use of complex algorithms and programming. In this paper, a new Bayesian approach to analyse longitudinal data within the context of antedependence models is proposed. This innovative approach takes into account the possibility of having nonstationary correlations and variances, and proposes a robust and computationally efficient estimation method for this type of data. We consider the joint modelling of the mean and covariance structures for the general antedependence model, estimating their parameters in a longitudinal data context. Our Bayesian approach is based on a generalization of the Gibbs sampling and Metropolis-Hastings by blocks algorithm, properly adapted to the antedependence models longitudinal data settings. Finally, we illustrate the proposed methodology by analysing several examples where antedependence models have been shown to be useful: the small mice, the speech recognition and the race data sets.
An Efficient Model to Encrypt Text and Gray Image Based on Amino Acid Chains

The main purpose of the data encryption process is the difficulty of understanding the content of the message with the difficulty of detecting the encryption mechanism. Therefore, in this paper we proposed a new model of encryption based on the idea of encrypting text messages and gray images using amino acid chains. Amino acids are distinguished by being a series of alphabets represented by 20 characters and do not have a specific length. The main idea of this paper is to get an amino acid chain that contained encrypted message. To develop this model, we construct artificial lookup table to encrypt any character of message to amino acid chain, after that applying encryption process. The proposed model applied on different kinds and sizes of messages, which proved the success of the main idea of the research, which is obtaining amino acid chains with encrypted messages. The proposed model appears efficiency in real time of encryption and extraction process compared with known methods. In the end we compared the amino acids that we extract on the process of encrypting with internationally known amino acids and we found a similarity ratio, which proves that the proposed model is effective in terms of increasing ambiguity on the encrypted message.
Objective and Subjective Evaluation of a Multi-Stereo 3D Reconstruction System

In recent years, 3D reconstruction systems comprising multiple depth sensors have received increasing interest for dynamic scene reconstruction and related applications. Publicly available ground truth data are of limited usefulness when dealing with quality assessment of self-recorded data delivered by customized stereo configurations. In this paper, we propose a framework that incorporates versatile strategies for quantitative and qualitative evaluation of a multi-stereo reconstruction system and its intermediate products. Besides the design of suitable calibration objects for quantitative measurements, the framework exploits multiview data redundancy and generated novel views for objective quality assessment and to obtain subjective ratings from users. We demonstrate the applicability of our evaluation system in experiments with several stereo matching algorithms and view fusion approaches along with a pair-comparison based user study. We believe that our proposed evaluation framework is beneficial for the assessment of 3D products derived from self-recorded dynamic data of comparable set-ups, for example, in the context of subsequent augmented reality applications.
Impact of Savings on Economic Growth in Africa

This paper investigates the impact of savings on economic growth in Africa. Annual data covering thirty African countries based on data availability for the period of thirty-five years starting from 1980 were used. The study was found to be imperative because extant studies in this line were of mixed results. Panel Estimated Generalised Least Squares (EGLS) with pooled, fixed and random effects estimations were carried out, but Pooled Panel EGLS with cross-section Seemingly Unrelated Regression (SUR) weight estimation was explained. The study revealed that savings contribute 3.96 per cent to economic outputs when increased by a percentage. Meanwhile, a per cent increment in each of the foreign direct investment (FDI) and current account balance will positively impact economic growth by 18.7 and 4.6 per cent respectively. Also, there is no causality between domestic saving and economic growth. But bidirectional causality exists between foreign direct investment and domestic saving. The study concluded that saving is relevant to economic growth in Africa, though, its contribution is very low when compared to FDI’s impact but very important. The current account balance is very relevant to foreign direct investment and domestic saving. It is recommended that policies favouring savings should be encouraged such as universal coverage pension and grass-roots oriented saving schemes. Also, a surplus current account balance should be maintained in the continent to attract more foreign direct investment and improve domestic savings.
Visual and Gyroscope Sensor for Head Movement Controller System on Meal-Assistance Application

Head movement utilizes gestures to aid people with disabilities so that they can have hands-free human-computer interaction. Currently, motion-based sensor is the most widely used approach to recognize head gestures. Identification of head movement is important to control a robotic manipulator in an assisting device. However, the most effective methodologies to assess head angular movements are yet to be discovered. This paper combines two algorithms, the visual sensor and the gyro sensor, to identify head orientation movement with high precision. Head orientations were measured using data distribution and this was done with a meal-assistance robot manipulator used in a sitting position. Evaluation of the accuracy of the system shows a visual sensor and gyro sensor. Experimental results show that a correct head movement with the average accuracy is 82%. Therefore, we propose the application of position control of meal assistive robot based on user's head movement in a sitting position.
Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions

Electronic Health Record (EHR) data is a rich source for powerful biomedical discovery but it consists of a wide variety of data types that are traditionally difficult to model. Furthermore, many machine learning frameworks that utilize these data for predictive tasks do not fully leverage the inter-connectivity structure and therefore may not be fully optimized. In this work, we propose a relational, deep heterogeneous network learning method that operates on EHR data and addresses these limitations. In this model, we used three different node types: patient, lab, and diagnosis. We show that relational graph learning naturally encodes structured relationships in the EHR and outperforms traditional multilayer perceptron models in the prediction of thousands of diseases. We evaluated our model on EHR data derived from MIMIC-III, a public critical care data set, and show that our model has improved prediction of numerous disease diagnoses.
Exploration on Theoretical and Practical Projects of Software Architecture Course

It has been widely recognized that software architecture is one of the hard courses to teach in software engineering. First, it is usually difficult to update the knowledge written in textbooks with the rapidly developed practical progress. Accordingly, we need to teach students how to follow the state-of-the-art research works within both academic and industrial context. Secondly, due to the fact that students are lacking practical experience on the design and development of large-scale systems, it is difficult for them to deeply understand the theory and rationality of software architecture. Accordingly, we need to teach students how to cope with practical problems based on the learnt theoretical work. This paper presents our exploration on designing theoretical and practical projects of software architecture course for postgraduate students during the last ten years. We aim to improve students' ability to investigate cutting-edge achievements and concerns in academia and industry, to understand, analyze, and design industrial software architecture based on open-source systems. Furthermore, we expect and encourage students' potential to become software architects. This paper details our project setup and discusses the feedbacks, and shows the benefits of our projects.
Overview of the holographic-guided cardiovascular interventions and training – a perspective

Abstract Immersive technologies, like Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) have undergone technical evolutions over the last few decades. Their rapid development and dynamic changes enable their effective applications in medicine, in fields like imaging, preprocedural planning, treatment, operations planning, medical students training, and active support during therapeutic and rehabilitation procedures. Within this paper, a comprehensive analysis of VR/AR/MR application in the medical industry and education is presented. We overview and discuss our previous experience with AR/MR and 3D visual environment and MR-based imaging systems in cardiology and interventional cardiology. Our research shows that using immersive technologies users can not only visualize the heart and its structure but also obtain quantitative feedback on their location. The MR-based imaging system proposed offers better visualization to interventionists and potentially helps users understand complex operational cases. The results obtained suggest that technology using VR/AR/MR can be successfully used in the teaching process of future doctors, both in aspects related to anatomy and clinical classes. Moreover, the system proposed provides a unique opportunity to break the boundaries, interact in the learning process, and exchange experiences inside the medical community.
Quantum Data Aggregation Using Secret Sharing and Genetic Algorithm

In the process of data aggregation, intruder nodes and compromised nodes may decrease the system performance. Hence, security plays an important role. Secret sharing is a method to find the compromised nodes. The previous researches in secure data aggregation concentrated on key authentication process and little research is done in N-party secret sharing methods. In this article, we proposed quantum data aggregation using secret sharing and genetic algorithm, where a node forwards data to its most trusted neighbor only. In the proposed method, a node selects n 1-hop neighbors from a list of its m 1-hop neighbors, which well behaved over a period and forwards data to the most trusted amongst them. We simulated quantum teleportation using SimulaQron tool for establishing secure communication. However secured is the communication channel the compromised nodes may decrease the system performance by exposing network to malicious activities. Each node creates n shares of a secret and forwards a share on a secured quantum channel to the n 1-hop neighbors in such a way that from any t neighbors share(t<; n) the secret is reconstructed. The data aggregation security increases since a group of t users can reconstruct the key but no single user has the complete authority on the secret. The proposed method ensures that each node will be able to aggregate the captured message securely without any interventions from the compromised/intruder nodes. The proposed system applies genetic method to improve the efficiency of the process of detection of the compromised nodes based on the behavioral history of the node. While detecting the malicious nodes the proposed method accuracy rate is 98.8 and false rate is 0.15, which is almost equal to secure and energy-efficient data aggregation method based on an access control method. However, in terms of the size of the network considered, the percentage of malicious nodes assumed and from the basic principles of quantum teleportation, the proposed method proves to be a better protocol for implementing secure data aggregation.
Use of 360-Degree Video in Organizational Communication: Case Study of Humanitarian Aid NGOs

Virtual reality and 360-degree video are being used in several areas. Journalism is among them. In fact, the use of these technologies and formats has given way to a novel trend known as Immersive Journalism. But organizational communication and, more specifically, humanitarian aid NGOs have found in 360 video storytelling an opportunity to bring the realities where they work closer to the society. The aim of this immersive narrative is to allow users to become witnesses through a first-person experience of the stories’ events. And connected to this, to promote the creation of ties with ‘the others’ and their realities. In this study, it is analyzed from a critical and ethical point of view the 360-degree video content produced by five well-known NGOs: International Federation of Red Cross and Red Crescent Societies, Doctors Without Borders, UNHCR, Save the Children and World Vision International.
A Long/Short-Term Memory Based Automated Testing Model to Quantitatively Evaluate Game Design

The mobile casual game application lifespan is getting shorter. A company has to shorten the game testing procedure to avoid being squeezed out of the game market share. There is no sufficient testing indicator to objectively evaluate the operability of different game designs. Many automated testing methodologies are proposed, but they adopt rule-based approaches and cannot provide quantitative analysis to statistically evaluate gameplay experience. This study suggests applying “Learning Time” as a testing indicator and using the learning curve to identify the operability of different game designs. This study also proposes a Long/Short-Term Memory based automated testing model (called LSTM-Testing) to statistically testing game experience through end-to-end functionality (Input: game image; Output: game action) without any manual intervention. The experiment results demonstrate LSTM-Testing can provide quantitative testing data by using learning time as the control variable, game design as the independent variable, and time to complete game as the dependent variable. This study also demonstrates how LSTM-Testing evaluates the effectiveness of different gameplay learning strategies, e.g., reviewing the newest decisions, reviewing the correct decision, or reviewing the wrong decisions. The contributions of LSTM-Testing are (1) providing an objective and quantitative analytical game-testing framework, (2) reducing the labor cost of inefficient and subjective manual game testing, and (3) allowing game company boosts software development by focusing on game intellectual property and leaves game testing to artificial intelligence (AI).
Female Adolescents in attitudes and side effects of consuming iron supplements

As many as 27% of female adolescents in developing countries suffer from anemia. Anemia can have a big impact on the health of young women, especially pregnant women. The problem of anemia in adolescents needs to be overcome because teenagers will become pregnant women who of course need a healthy body more later. One effort is to take iron (Fe) supplements. This study aims to analyze the risk factors for attitudes and side effects of Fe tablets on the consumption of Fe tablets in female adolescents in high school. This study used a case-control study design. The population of this study was all young women who received Fe tablets at SMA Negeri 10 Kendari with a total of 85 students who were held from February 2020 to completion. The sample consisted of 40 people, namely 20 cases and 20 controls, the sampling technique was simple random sampling. The results showed that attitude is a risk factor for consuming Fe tablets, obtained OR = 6,000 (95% CI 1.458-24.686) and side effects of Fe tablets are a risk factor for consuming Fe tablets, the value of OR = 5.571 (95% CI 1.420-21.860) was obtained. It is necessary to increase communication, education, and information (IEC) in schoolchildren to increase positive attitudes and reduce the number of complaints of side effects of Fe tablets in school children.
Human Detecting Robot based on Computer Vision - Machine Learning

In the recent decades, several advanced and creative applications have been cultivated and implemented by scientist, visionary engineers and researchers on robots, for illustration such as search and rescue, surveillance, detection, traffic monitoring, weather monitoring and so on what was recently reviewed as science fiction or inconceivable futuristic into reality, making our lives much easier, delightful and more fascinating. The Robots have transitioned era of machines that can make everyday things and they are exceptional because they can generate wide type of actions based on the identical machine. The current ongoing progress in robotic intelligence and machinery provoked substantial changes in enabling robots to perform a broad spectrum of detection missions with increasing level of intricacy. Operations such as search, rescue and detection require a large camera hedging and thus making robots an appropriate tool to execute advanced tasks. At the same instance, the expanding inclination of machine learning applications in computer vision provides an exceptional insight into the drive of this project. This project demonstrates an approach which makes it possible to identify the existence of human in the atmosphere with human object detection algorithm using computer vision capabilities. The intention of detecting human presence in intended area is to mitigate unlawful entry into forbidden area, unlawful logging activities as well as saving the human lives in unintentional conditions. Also, the consequences of this project are anticipated to magnify the exercise of robots for supervision and vigilance purpose to save time and cost.
Hardware Implementation of Authenticated Ciphers for Embedded Systems

The demand for embedded systems in applications that handle critical or private information has strongly focused designers' attention on the security aspects of this kind of system. Using the C programs and HDL descriptions available in the repositories of the CAESAR Competition and the ATHENa Project, this work presents a design flow that eases the development and evaluation of different solutions for the hardware implementation of authenticated ciphers and their incorporation as accelerating peripherals in embedded systems for different application cases. Three ciphers, finalists in the different categories established in the contest, have been analyzed, although the described approaches can be applied to any of the proposals submitted to the CAESAR Competition. A Zybo-Z7 development board that incorporates a Zynq-7000 device from Xilinx, which combines programmable logic from the FPGAs of the 7-Series with a dual-core Cortex-A9 ARM processing system, has been used as hardware platform in all the designs. The Vivado environment has been employed to perform the different stages of synthesis and verification necessary to carry out the implementation of the cipher, its conversion into an IP module, and its integration in an embedded system using different interconnection schemes that allow establishing cost/performance tradeoffs for different applications.