Reviewerly/exHarmony huggingface dataset:
The Role of Mobile Learning in Supporting Community Health Workers’ Continuous Learning: A Case of a Village Health Teams Training APP

The purpose of this study is to assess the role of mobile learning in supporting Community Health Workers referred to as Village Health Teams in Uganda. Continuous learning with a smartphone application (App). The study aimed to objectify the concept of using Mobile learning to support CHWs’ training, which is presently considered to be irregular, and underfunded. A Mobile App prototype is used as a study tool to digitize training content on the subject of diseases, reproductive health, sanitation, and family wellbeing. The study involved 41 respondent participants categorized as CHWs, paramedical students, and Community Health Leaders. The variables for the study included the prevalence of smartphone devices, ease of Mobile App use, challenges of the existing approach to training and information delivery, App accessibility issues, and anticipated technical challenges. Despite the conspicuous challenges of mobile technology, the study findings suggest that mobile learning is a favorable alternative to support the training of CHWs. The identified advantages relate to geographic convenience, numerous mobile services, ease of access, update, storage, and sharing of content. However, imminent challenges include the availability of devices among CHWs, lack of electricity, unreliable network, and lack of internet data. Interventions by stakeholders to mitigate challenges are highlighted and mobile learning recommendations are expanded.
Educational Development in Africa: Bridging disability Gap with Distance Learning for the Visually Impaired

It is a known fact that the era of the Covid19 pandemic has further exposed the concept and technology of distance learning as more and more people are now interested in higher education through the distance learning mode. Distance learning courses are possibly an appropriate tool for mainstream students with impairments in education since they rely significantly on digital material and are technology-mediated. However, in the drive to move forward, most structures/systems are typically created without taking into cognizance the unique interface issues those students with disabilities are confronted with while using technology, especially the blind and the visually impaired. Hence, this paper is aimed at creating an enabling examination platform where this vulnerable group would be supported by providing a voice supported application, ODL Visually Impaired Assessment Bot for the blind and the visually impaired to write their examinations. This will further ensure equality and inclusive education in acquiring life-long learning through distance learning. Artificial intelligence technologies were used to develop a Robot that was used for the implementation of the Application. The Robot reads out the examination questions to the blind and visually impaired student and also listens to the student speak and thereafter captures and types out the answers of the student. It is against this backdrop that this paper is written to proffer a solution to the blind and the visually impaired students in Africa, especially, the National Open University of Nigeria. The significance of this ODL Visually Impaired Assessment Bot is aimed at building a sustainable educational system through the use of technology, thus eradicating limiting ideologies in Africa, like ‘those with disabilities cannot be educated and the rich only deserve quality education’. This application will create a level ground for all to access life-long quality education, through the open and distance mode. The visually impaired if given proper education cannot constitute any form of liability but an asset to themselves, their families and the society at large.
Multidisciplinary Perspectives on Automatic Analysis of Children’s Language Samples: Where Do We Go from Here?

Language sample analysis (LSA) is invaluable to describe and understand child language use and development for clinical purposes and research. Digital tools supporting LSA are available, but many of the LSA steps have not been automated. Nevertheless, programs that include automatic speech recognition (ASR), the first step of LSA, have already reached mainstream applicability.To better understand the complexity, challenges, and future needs of automatic LSA from a technological perspective, including the tasks of transcribing, annotating, and analysing natural child language samples, this article takes on a multidisciplinary view. Requirements of a fully automated LSA process are characterized, features of existing LSA software tools compared, and prior work from the disciplines of information science and computational linguistics reviewed.Existing tools vary in their extent of automation provided across the process of LSA. Advances in machine learning for speech recognition and processing have potential to facilitate LSA, but the specifics of child speech and language as well as the lack of child data complicate software design. A transdisciplinary approach is recommended as feasible to support future software development for LSA.
An open metaverse

Why is sci-fi author Neal Stephenson going head-to-head with Meta and Microsoft in the race to bring virtual worlds to the masses, asks Matthew Sparkes
How should you respond when a client takes offence to one of your employee's social media posts?

In PracticeVolume 44, Issue 8 p. 493-494 Opinion How should you respond when a client takes offence to one of your employee's social media posts? Georgina Mann, Georgina MannSearch for more papers by this author Georgina Mann, Georgina MannSearch for more papers by this author First published: 07 October 2022 https://doi.org/10.1002/inpr.253Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume44, Issue8October 2022Pages 493-494 RelatedInformation
Theater as Data: Computational Journeys into Theater Research Miguel Escobar Varela. Ann Arbor: University of Michigan Press, 2021; pp. viii + 222, 21 illustrations, 13 datasets, 4 code samples, 4 videos. $75 cloth, $29.95 paper, Open Access e-book.

Theater as Data: Computational Journeys into Theater Research Miguel Escobar Varela. Ann Arbor: University of Michigan Press, 2021; pp. viii + 222, 21 illustrations, 13 datasets, 4 code samples, 4 videos. 29.95 paper, Open Access e-book.
Automated and Robust User Story Coverage

Current practices in software testing such as Test Driven Development or Behavior Driven Development aim at linking code to expected behavior. In this context, code coverage is widely used to improve code quality, reduce bugs and ssure requirements satisfaction. Even if change tracking software allows finely analyzing code evolution, associating a particular code chunk to the requirements at the origin of the code modification is difficult for a large code base. In this preliminary work, we propose a new “user story coverage” metric that reports lacking requirement coverage quality, to help developers focus their efforts on enhancing unit and integration tests. We propose a methodology to compute this metric in a robust and automated fashion and evaluate its feasibility on open-source projects.
Deep Learning Technology Applied to Medical Image Tissue Classification

Medical image classification is a novel technology that presents a new challenge. It is essential that pathological images are automatically and correctly classified to enable doctors to provide precise treatment. Convolutional neural networks have demonstrated their effectiveness in classifying images in deep learning, which may have dozens or hundreds of layers, to illustrate the relationship between them in terms of their different neural network features. Convolutional layers consisting of small kernels take weights as input and guide them through an activation function as output. The main advantage of using convolutional neural networks (CNNs) instead of traditional neural networks is that they reduce the model parameters for greater accuracy. However, many studies have simply been focused on finding the best CNN model and classification results from a single medical image classification. Therefore, we applied a common deep learning network model in an attempt to identify the best model framework by training and validating different model parameters to classify medical images. After conducting experiments on six publicly available databases of pathological images, including colorectal cancer tissue, chest X-rays, common skin lesions, diabetic retinopathy, pediatric chest X-ray, and breast ultrasound image datasets, we were able to confirm that the recognition accuracy of the Inception V3 method was significantly better than that of other existing deep learning models.
Playful Meaning-Making as Prosocial Fun

Smart city infrastructures enable the routine interleaving and integration of diverse activities, including new ways to play, to be playful, and to participate. We discuss three examples: (1) citizen-based water quality monitoring, which combines outdoor exercise and social interaction with safeguarding public water supplies, (2) a digital scavenger hunt, which combines the experiences of a community arts festival with shared reflections about significant community places and events, and (3) public thanking, which encourages people to acknowledge neighbors and local groups that serve and strengthen the community. Each of these interaction possibilities in itself alters lived experience modestly. We argue that lightweight and playful meaning making activities can be prosocial fun, that is to say, they can simultaneously be playful and fun, but also substantive contributions to the coherence and richness of a community.
Finding Optimal Path and Privacy Preserving for Wireless Network

Abstract: Privacy-preserving routing protocols in wireless networks frequently utilize additional artificial traffic to hide the source-destination identities of the communicating pair. Usually, the addition of artificial traffic is done heuristically with no guarantees that the transmission cost, latency, etc., are optimized in every network topology. We explicitly examine the privacyutility trade-off problem for wireless networks and develop a novel privacy-preserving routing algorithm called Optimal Privacy Enhancing Routing Algorithm (OPERA). OPERA uses a statistical decision-making framework to optimize the privacy of the routing protocol given a utility (or cost) constraint. We consider global adversaries with both Lossless and lossy observations that use the Bayesian maximum-a-posteriori (MAP) estimation strategy. We formulate the privacy-utility trade-off problem as a linear program which can be efficiently solved.
An Open-Source Simulation Model for Solving Scheduling Problems

In this study, an open-source simulation model is presented for solving scheduling problems.The model is capable of solving different benchmarks.The methods involved in the simulation are mainly based on generating dispatching rules or using them to solve problems, but there are other heuristics as well.Dispatching rules in an evolutionary process are generated using Gene Expression Programming.For this aim, a coding method, which has not been described in the literature before, is explained.Along with the explanation of the properties of the source code, information about deterministic, dynamic models, buffer states, machine breakdown states, and the methods used to deal with them is presented.Concepts are explained with visual examples.In addition, a subject that has not been investigated in the literature before is analyzed by using the simulation model.This topic is to examine the results of solving machine assignment and operation sequencing sub-problems in flexible job shop scheduling problems with different rules.Moreover, objective functions that the source code can handle are discussed.Unlike many studies in the literature, the codes are presented to the readers as open source.Also, it is open to development and can be easily modified by users to solve other types of problems.Finally, in the study, experimental results are presented on the basis of some benchmarks available in the literature, and the limits of the study and source code are explained.
THE IMPACT OF ELECTRONIC ADVERTISEMENTS ON FINANCIAL SERVICES DEVELOPMENT IN JORDANIAN COMMERCIAL BANK

The study purpose is to examine the impact of electronic advertisements with their dimensions (advertising text, clarity of advertisement content, directing and technical design of the advertisement, and advertisement stimuli) on the development of banking services with their dimensions (technology used for services, quality of services, and diversity of services). The study methodology depend on the descriptive analysis approach used, and the study population covered all marketing department employees in the Jordanian commercial banks with a number of (409) employees, and the (SPSS) statistical program was used for hypothesis testing. The study concluded that there is a statistically significant effect at the level of significance (α≤0.05) for electronic advertisements with their combined dimensions (advertisement text, clarity of advertisement content, output and technical design of advertisement, and advertisement stimuli) in the development of banking services with its combined dimensions (technology used for services, quality of services, and diversity of services) for Jordanian commercial banks. The study findings suggest that the electronic advertisements are required including recorded videos that explain the nature of financial services within a clear content that highlights the benefits of those services and is designed and produced in technical ways within attractive stimuli for customers of Jordanian commercial banks.
Prior Knowledge-Based Causal Inference Algorithms and Their Applications for China COVID-19 Analysis

Causal inference has become an important research direction in the field of computing. Traditional methods have mainly used Bayesian networks to discover the causal effects between variables. These methods have limitations, namely, on the one hand, the computing cost is expensive if one wants to achieve accurate results, i.e., exponential growth along with the number of variables. On the other hand, the accuracy is not good enough if one tries to reduce the computing cost. In this study, we use prior knowledge iteration or time series trend fitting between causal variables to resolve the limitations and discover bidirectional causal edges between the variables. Subsequently, we obtain real causal graphs, thus establishing a more accurate causal model for the evaluation and calculation of causal effects. We present two new algorithms, namely, the PC+ algorithm and the DCM algorithm. The PC+ algorithm is used to address the problem of the traditional PC algorithm, which needs to enumerate all Markov equivalence classes at a high computational cost or with immediate output of non-directional causal edges. In the PC+ algorithm, the causal tendency among some variables was analyzed via partial exhaustive analysis. By fixing the relatively certain causality as prior knowledge, a causal graph of higher accuracy is the final output at a low running cost. The DCM algorithm uses the d-separation strategy to improve the traditional CCM algorithm, which can only handle the pairwise fitting of variables, and thus identify the indirect causality as the direct one. By using the d-separation strategy, our DCM algorithm achieves higher accuracy while following the basic criteria of Bayesian networks. In this study, we evaluate the proposed algorithms based on the COVID-19 pandemic with experimental and theoretical analysis. The experimental results show that our improved algorithms are effective and efficient. Compared to the exponential cost of the PC algorithm, the time complexity of the PC+ algorithm is reduced to a linear level. Moreover, the accuracies of the PC+ algorithm and DCM algorithm are improved to different degrees; specifically, the accuracy of the PC+ algorithm reaches 91%, much higher than the 33% of the PC algorithm.
Generative adversarial networks-based image steganography with multiscale features integration

As an effective method of information hiding, steganography embeds secret information into images in a way that is not perceived by humans. Recent interest in the combination of image steganography and generative adversarial networks (GANs) has yielded rapid progress. However, existing steganography frameworks still suffer from the low quality of the steganographic images and weak resistance to detection by steganalysis algorithms. To overcome these limitations, we propose an effective GAN-based image steganography framework with multiscale features integration. Specifically, we construct the secret image feature extraction network (SfeNet), which is driven by the spatial attention mechanism to extract multiscale features of secret images. And the encoder combined with the efficient channel attention mechanism is presented to embed multiscale features of secret images into the cover image. Subsequently, a steganalyzer is incorporated as the discriminator with the encoder in GAN to strengthen the ability of the model to resist steganalysis. Besides, a mixed loss function is proposed by combining perceptual loss, MS-SSIM, and L1 loss to preserve the structural similarity of images. Experimental results on ImageNet, Pascal VOC2012, and LFW show that the proposed method achieves better quality steganographic images and better resistance to steganalysis compared to some of the state-of-the-art steganography algorithms.
Studies of mineral composition of ores of the Kumtor deposit (Central Tien Shan) by QXRD and XRF nethods

Samples of various mineral ore types of the Kumtor gold deposit (Central Tien Shan) were studied using XRF method on a TORNADO M4 analyzer by digital processing of images of the elemental scanning maps to reveal the possibilities of this method to measure the mineral composition of ores without using the AMICS electronic mineral database. During the experimental work, the optimal parameters of elemental analysis and scanning of samples were selected, which allow maximum information with minimal time and expenses. The experimental analyses enabled us to estimate the semi-quantitative content of major ore-forming non-metallic and ore minerals and to establish the possible presence of minerals previously unknown in the composition of ores (barytocalcite, viterite, ferrinatrite, etc.). The macro- and microscopical identifcation of these minerals is problematic, although their relative content locally reaches several weight percents.
Studies of mineral composition of ores of the Kumtor deposit (Central Tien Shan) by QXRD and XRF methods

Samples of various mineral ore types of the Kumtor gold deposit (Central Tien Shan) were studied using XRF method on a TORNADO M4 analyzer by digital processing of images of the elemental scanning maps to reveal the possibilities of this method to measure the mineral composition of ores without using the AMICS electronic mineral database. During the experimental work, the optimal parameters of elemental analysis and scanning of samples were selected, which allow maximum information with minimal time and expenses. The experimental analyses enabled us to estimate the semi-quantitative content of major ore-forming non-metallic and ore minerals and to establish the possible presence of minerals previously unknown in the composition of ores (barytocalcite, viterite, ferrinatrite, etc.). The macro- and microscopical identifcation of these minerals is problematic, although their relative content locally reaches several weight percents.
Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios

Hyperspectral imaging opens up new opportunities for masked face recognition via discrimination of the spectral information obtained by hyperspectral sensors. In this work, we present a novel algorithm to extract facial spectral-features from different regions of interests by performing computer vision techniques over the hyperspectral images, particularly Histogram of Oriented Gradients. We have applied this algorithm over the UWA-HSFD dataset to extract the facial spectral-features and then a set of parallel Support Vector Machines with custom kernels, based on the cosine similarity and Euclidean distance, have been trained on fly to classify unknown subjects/faces according to the distance of the visible facial spectral-features, i.e., the regions that are not concealed by a face mask or scarf. The results draw up an optimal trade-off between recognition accuracy and compression ratio in accordance with the facial regions that are not occluded.
Potential of Visual Mind Mapping in Language Learning: A Systematic Review

In the era of globalization, language skills become important regardless of whether it is in one's community or internationally.Language learning is one of the learnings we must learn to communicate and gain knowledge in the 21st century.Therefore, the conventional teaching and learning approach in the subject of language needs to be improved to enable students to master the elements of language learning better.Nevertheless, language learning is not easy.It includes various elements which need to cover by students in the process of language learning.Teaching and learning languages are full of challenges.This study is conducted to make a systematic literature review to analyze in detail the potential of visual mind mapping as a language learning tool.This systematic literature review used Scopus as the database search engine to select articles that are related to the title.The selected articles for this study are from 2018 to 2021.There are 10 articles selected as samples among 158 articles found based on the keywords and limitations.PRISMA is used in the process of selecting articles.The results show that most of the studies focused on non-native language learning and found that visual mind mapping has shown high potential as a tool to help students in the process of language learning.Visual mind mapping has a positive influence on vocabulary learning, grammar learning, speaking ability, reading ability, and writing skills.This systematic review helps readers identify the potential of visual mind mapping in language learning.
Risk evaluation of energy investment projects along the Belt and Road based on cloud model and evidence theory

China's major energy projects along the Belt and Road are characterized by large-scale capital investment, long construction cycles, and complex investment environments, making it more difficult to assess project investment risks. Based on the PESTEL theory, a risk evaluation index system is constructed for energy enterprises' investment projects along the Belt and Road. In view of the uncertainty of energy investment projects, a risk evaluation method combining a cloud model and an evidence theory is proposed. To examine the developed index system and risk evaluation method, an overseas hydropower investment project is chosen and the risk factors are analyzed. The results suggest that the risk level of the enterprise's project is highest, the political and environmental risks are both high, the economic and project risks are both at average level, and the energy risk is relatively low, which verifies the effectiveness of this information fusion method. The method can be used to solve the problem of linguistic ambiguity and uncertainty in multi-attribute decision problems and is applicable to similar investment projects, thus providing a new technical path to assess the investment risks of energy enterprises along the Belt and Road.