Reviewerly/exHarmony huggingface dataset:
Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics

Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions.
Safe Testing

We develop the theory of hypothesis testing based on the e-value, a notion of evidence that, unlike the p-value, allows for effortlessly combining results from several studies in the common scenario where the decision to perform a new study may depend on previous outcomes. Tests based on e-values are safe, i.e. they preserve Type-I error guarantees, under such optional continuation. We define growth-rate optimality (GRO) as an analogue of power in an optional continuation context, and we show how to construct GRO e-variables for general testing problems with composite null and alternative, emphasizing models with nuisance parameters. GRO e-values take the form of Bayes factors with special priors. We illustrate the theory using several classic examples including a one-sample safe t-test and the 2 x 2 contingency table. Sharing Fisherian, Neymanian and Jeffreys-Bayesian interpretations, e-values may provide a methodology acceptable to adherents of all three schools.
metadata enriched system for the documentation of multi-modal digital imaging surveys

In the field of Digital Heritage Studies, data provenance has always been an open and challenging issue. As Cultural Heritage objects are unique by definition, the methods, the practices and the strategies to build digital documentation are not homogeneous, universal or standardized. Metadata is a minimalistic yet powerful form to source and describe a digital document. They are often required or mandatory at an advanced stage of a Digital Heritage project. Our approach is to integrate since data capture steps meaningful information to document a Digital Heritage asset as it is moreover being composed nowadays from multiple sources or multimodal imaging surveys. This article exposes the methodological and technical aspects related to the ongoing development of MEMoS; standing for Metadata Enriched Multimodal documentation System. MEMoS aims to contribute to data provenance issues in current multimodal imaging surveys. It explores a way to document CH oriented capture data sets with a versatile descriptive metadata scheme inspired from the W7 ontological model. In addition, an experiment illustrated by several case studies, explores the possibility to integrate those metadata encoded into 2D barcodes directly to the captured image set. The article lays the foundation of a three parted methodology namely describe - encode and display toward metadata enriched documentation of CH objects.
Usability and Acceptance of Exergames Using Different Types of Training among Older Hypertensive Patients in a Simulated Mixed Reality

Virtual and augmented reality (VR/AR) exergames are promising tools for increasing training motivation. However, the use of exergames with mixed reality (MR) headsets remains under-researched. Older adults with hypertension could also benefit from the increased training adherence associated with MR. Endurance and strength endurance exercises are recommended for this group to lower blood pressure. The aim of the preliminary study (n = 22) was to compare the usability and acceptance of two exergames, which represent two different training types—strength endurance training (SET) and endurance training (ET). The developed exergame prototypes were applied in “simulated MR” using a VR head-mounted display. We examined the following outcomes: usability (TUI), intention to use (TUI), subjective task load (NASA-TLX), frustration (NASA-TLX), and presence (PQ). The results showed that frustration was significantly greater in the ET than in the SET (p = 0.038). Presence was significantly higher in the SET (p = 0.002). No significant differences in usability and acceptance were found in the exergames. The results indicate that usability and acceptance are not related to the type of training when utilizing MR exergames. Whether the results are transferable with a real MR headset must be determined in further research.
The insider Change Laboratory in practice

This paper describes a Change Laboratory that took place in a laptop-mediated English language course at a large tertiary institution in the United Arab Emirates.Earlier research had suggested that despite much heralded technology interventions students were in fact failing in increasing numbers.The Change Laboratory aimed to make a collective improvement in face-to-face laptop-mediated classrooms.This paper focusses on the solutions proposed during the intervention, and further contradictions identified as the participants attempt to apply these solutions.A future model of activity is also proposed.The paper contributes to insider research, Activity Theory and technology enhanced learning.Suggestions for future interventions are also made.Studies in Technology Enhanced Learning, 3(1) https://doi.org/10.21428/8c225f6e.9dc1d9efprinciple of contradictions identify issues that may be causing unintended outcomes.However, to utilize CHAT purely for describing the complexity of an activity is to fall short, and to paraphrase Marx (1852/1979) fall into the trap of merely describing the world as it exists.Instead, we should seek to solve problems as they occur, and not simply describe the world but actively seek to change it.Through the Change Laboratory (Engeström et al., 1996;Virkkunen & Newnham, 2013), a methodology firmly rooted in CHAT at all stages, it is possible to seek concrete change from abstract ideas.A Change Laboratory is a formative intervention for the development of work activity that is carried out by practitioners in collaboration with a researcher-interventionist (Virkkunen & Newnham, 2013).Through a cycle of expansive learning, a Change Laboratory, can address contradictions and allow participants to apply solutions and create new, concrete work practices.The methodology is addressed in more detail in a later section.This paper will describe one such Change Laboratory that took place in a preparatory English course at a federal university in the United Arab Emirates (UAE) in 2019. The contextRecent decades have seen governments in the MENA region attempting to move from oil reliance to knowledge economies, and education has been placed at the forefront of this drive.The UAE has coupled the drive to improve education with a strong push for classroom technology.Degree programs in the UAE are largely taught in English.Students that fail to meet the English proficiency requirement can join a one-year English preparatory course.
Lossless embedding: A visually meaningful image encryption algorithm based on hyperchaos and compressive sensing

A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing (CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform (DWT) on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics (PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.
FLAGS framework for comparative analysis of Federated Learning algorithms

Federated Learning (FL) has become a key choice for distributed machine learning. Initially focused on centralized aggregation, recent works in FL have emphasized greater decentralization to adapt to the highly heterogeneous network edge. Among these, Hierarchical, Device-to-Device and Gossip Federated Learning (HFL, D2DFL \& GFL respectively) can be considered as foundational FL algorithms employing fundamental aggregation strategies. A number of FL algorithms were subsequently proposed employing multiple fundamental aggregation schemes jointly. Existing research, however, subjects the FL algorithms to varied conditions and gauges the performance of these algorithms mainly against Federated Averaging (FedAvg) only. This work consolidates the FL landscape and offers an objective analysis of the major FL algorithms through a comprehensive cross-evaluation for a wide range of operating conditions. In addition to the three foundational FL algorithms, this work also analyzes six derived algorithms. To enable a uniform assessment, a multi-FL framework named FLAGS: Federated Learning AlGorithms Simulation has been developed for rapid configuration of multiple FL algorithms. Our experiments indicate that fully decentralized FL algorithms achieve comparable accuracy under multiple operating conditions, including asynchronous aggregation and the presence of stragglers. Furthermore, decentralized FL can also operate in noisy environments and with a comparably higher local update rate. However, the impact of extremely skewed data distributions on decentralized FL is much more adverse than on centralized variants. The results indicate that it may not be necessary to restrict the devices to a single FL algorithm; rather, multi-FL nodes may operate with greater efficiency.
Efficient Path Planning for a Microrobot Passing through Environments with Narrow Passages

This paper presents an efficient path-planning algorithm for microrobots attempting to pass through environments with narrow passages. Because of the extremely small size of a microrobot, it is suitable for work in this kind of environment. The rapidly exploring random tree (RRT) algorithm, which uses random sampling points, can quickly explore an entire environment and generate a sub-optimal path for a robot to pass through it; however, the RRT algorithm, when used to plan a path for a microrobot passing through an environment with narrow passages, has the problem of being easily limited to local solutions when it confronts with a narrow passage and is unable to find the final path through it. In light of this, the objectives of the considered path planning problem involve detecting the narrow passages, leading the path toward an approaching narrow passage, passing through a narrow passage, and extending the path search more efficiently. A methodology was proposed based on the bidirectional RRT in which image processing is used to mark narrow passages and their entrances and exits so that the bidirectional RRT can be quickly guided to them and combined with the deterministic algorithm to find paths through them. We designed the methodology such that RRT generates the sampling points for path growth. The multiple importance sampling technique is incorporated with bidirectional RRT, named MIS-BiRRT, to make the path grow faster toward the target point and narrow passages while avoiding obstacles. The proposed algorithm also considers multiple candidate paths simultaneously to expand the search range and then retain the best one as a part of the planning path. After validation from simulation, the proposed algorithm was found to generate efficient path planning results for microrobots to pass through narrow passages.
Blockchain and IoT Technology Transformation in Indonesian Education

Every area of life has been touched by technology, and the education industry is no different. Recently, there have been seismic shifts. Teachers and students used a blackboard technique in the traditional classroom teaching-learning process. But this process' characteristics have changed during the last few years. Technology has snuck in and significantly improved it. On the one hand, whereas in conventional classrooms the players were the teachers, the students, and the books, in the modern classroom the players are the teachers, the students, robots, the e-books, the laptops, and the books. With the help of e-lectures and tutorials, the teaching-learning process can now transcend geographical borders in classrooms. For the process, a vast amount of information is easily available. Additionally, the current situation shows a change from "Teacher-centered" to "Student Centric". However, the focus of this article is on utilizing the applications of Blockchain and IoT in the sector and identifying the areas where they prove to be advantageous. Several technologies have been major actors in modernizing the education industry. To comprehend and determine how these technologies offer a solution to significant educational difficulties, a thorough literature review must be conducted. The teaching and learning process needs to be understood in light of a variety of factors, including changes to the participants, the process itself, the results that are produced, and others. It is also necessary to identify the related difficulties. A thorough examination of these enables us to draw the conclusion that they can revolutionize education for the better in the future. Blockchain, in particular, is still relatively new, but because of the advantages it offers, further study and adoption will undoubtedly alter the teaching-learning process.
DHGEEP: A Dynamic Heterogeneous Graph-Embedding Method for Evolutionary Prediction

Current graph-embedding methods mainly focus on static homogeneous graphs, where the entity type is the same and the topology is fixed. However, in real networks, such as academic networks and shopping networks, there are typically various types of nodes and temporal interactions. The dynamical and heterogeneous components of graphs in general contain abundant information. Currently, most studies on dynamic graphs do not sufficiently consider the heterogeneity of the network in question, and hence the semantic information of the interactions between heterogeneous nodes is missing in the graph embeddings. On the other hand, the overall size of the network tends to accumulate over time, and its growth rate can reflect the ability of the entire network to generate interactions of heterogeneous nodes; therefore, we developed a graph dynamics model to model the evolution of graph dynamics. Moreover, the temporal properties of nodes regularly affect the generation of temporal interaction events with which they are connected. Thus, we developed a node dynamics model to model the evolution of node connectivity. In this paper, we propose DHGEEP, a dynamic heterogeneous graph-embedding method based on the Hawkes process, to predict the evolution of dynamic heterogeneous networks. The model considers the generation of temporal events as an effect of historical events, introduces the Hawkes process to simulate this evolution, and then captures semantic and structural information based on the meta-paths of temporal heterogeneous nodes. Finally, the graph-level dynamics of the network and the node-level dynamics of each node are integrated into the DHGEEP framework. The embeddings of the nodes are automatically obtained by minimizing the value of the loss function. Experiments were conducted on three downstream tasks, static link prediction, temporal event prediction for homogeneous nodes, and temporal event prediction for heterogeneous nodes, on three datasets. Experimental results show that DHGEEP achieves excellent performance in these tasks. In the most significant task, temporal event prediction of heterogeneous nodes, the values of precision@2 and recall@2 can reach 30.23% and 10.48% on the AMiner dataset, and reach 4.56% and 1.61% on the DBLP dataset, so that our method is more accurate at predicting future temporal events than the baseline.
Students’ Literacy Skills and Quality of Textbooks in Indonesian Elementary Schools

Purpose of the study: to find out: 1) basic literacy skills (ability to read, write and count) elementary school early grades students; (2) quality of textbooks in aspects of content, presentation, language, and graphic design; and (3) The significance of the relationship between the quality of textbooks and students' basic literacy skills.Methodology: This research uses a quantitative approach with a survey method. It was conducted in eight provinces in Indonesia. The sampling technique used stratified random sampling, 805 teachers. Data collection used a questionnaire for elementary school teachers who teach in low grades. The data analysis used descriptive statistics and correlation analysis.Main Findings: It was found that there are still relatively many early grades elementary school students who are not fluent in reading, writing, and arithmetic. These students' basic literacy skills have a significant and positive relationship with the quality of textbooks in the aspects of substance, presentation, language, and graphic design.Applications of this study: To improve basic literacy skills (reading, writing, and arithmetic) in early grades elementary school students, it is also necessary to improve the quality of textbooks in the aspects of substance/Content, presentation, language, and graphic design.Novelty/Originality of this study: It was found that there is a significant and positive relationship between basic literacy skills (reading, writing, and arithmetic) of early grades elementary school students and the quality of the textbooks they used both in terms of substance/Content, presentation, language, and graphic design.
Scaling Ethereum 2.0s Cross-Shard Transactions with Refined Data Structures

(1) Background: To solve the blockchain scaling issue, sharding has been proposed; however, this approach has its own scaling issue: the cross-shard communication method. To resolve the cross-shard communication scaling issue, rollups have been proposed and are being investigated. However, they also have their own scaling limitations, in particular, the degree of compression they can apply to transactions (TXs) affecting how many TXs can be included in one block. (2) Methods: In this paper, we propose a series of novel data structures for the compiling of cross-shard TXs sent using rollups for both public and private Ethereum. Our proposal removes redundant fields, consolidates repeated fields, and compresses any remaining fields in the rollup, modifying its data structure to compress the address, gas, and value fields. (3) Results: We have shown that our proposals can accommodate more cross-shard TXs in a block by reducing the TX size by up to 65% and 97.6% compared to the state-of-the-art in public and private Ethereum, respectively. This compression in TX size results in an over 2× increase in transactions per block (TPB) for our proposals targeting both types of Ethereum. (4) Conclusions: Our proposals will mitigate the scaling issue in a sharded blockchain that utilizes rollups for cross-shard communication. In particular, it will enable such sharded Ethereum networks to be deployed for large-scale decentralized systems.
A novel genetic-artificial neural network based age estimation system

Abstract Age estimation is the ability to predict the age of an individual based on facial clues. This could be put to practical use in underage voting detection, underage driving detection, and overage sportsmen detection. To date, no popular automatic age estimation system has been developed to target black faces. This study developed a novel age estimation system from the combination of a genetic algorithm and a back propagation (BP)-trained artificial neural network (ANN) and using the local binary pattern feature extraction technique (LBGANN) targeted at black faces. The system was trained with a predominantly black face database, and the result was compared against that of a standard ANN system (LBANN). The results showed that the developed system LBGANN outperformed the LBANN in terms of the correct classification rate.
An Experimental Study on Performance Evaluation of Shading Matrix to Select Optimal Parking Space for Solar-Powered Electric Vehicles

This study evaluated the applicability of a shading matrix generated using hemispherical fisheye images to select the optimal parking space to maximize the photovoltaic (PV) power generation of solar electric vehicles. The study aims to identify the maximum power point (MPP) of a PV module in a parking space using current–voltage measuring equipment to determine whether the shading matrix reflects any shadow phenomenon. The experiment was conducted on a parking lot at Daeyeon Campus. The fisheye images were taken in seven parking spaces. The shading matrices were generated by tracking the position of the sun over 12 months and 24 h periods. A 50 W PV module was installed on the roof of the vehicle and the MPP was measured in five experiments (9 a.m., 11 a.m., 13 p.m., 15 p.m., and 17 p.m.) in seven parking spaces. In parking spaces with a shading factor of 0, the PV modules were not shaded at all during the experiment, and the average MPPs were 34, 43, 37, and 21 W at 9 a.m., 11 a.m., 13 p.m., and 15 p.m., respectively. The shading matrix calculated using the fisheye images accurately reflected the shadow phenomenon that decreases PV power generation.