Reviewerly/exHarmony huggingface dataset:
Application of Information and Communication Technologies for Development (ICT4D) to Rural Communities in Kenya

There is growing evidence of the positive role ICTs can play in development, particularly in rural areas of developing countries using public services in the form of telecentres. Emphasizing proactive measures ensures ICTs serve as effective tools for social inclusion, social change, and widespread access, especially for the poor and disadvantaged communities. This research study explores the application of Information and Communication Technologies for Development (ICT4D) in a rural community in Kenya, by evaluating the Nguruman Community Knowledge Center (CKC), established in 2003 by a development organization. This study uses a participatory ethnographic research method that combines participatory techniques and ethnographic research, with the potential of feeding into action research. The research makes use of the communicative ecology approach in evaluating communication and ICTs for development. In this study, ICTs, particularly traditional ICTs (radio and television) significantly contribute to improving people’s living conditions by making information available that helps in solving real problems encountered. The expectations of community members who use these ICTs reflect their level of understanding of the relationship that exists between these tools and the improvement of their living conditions as well as enhancing development efforts. The study shows that the context and institutional framework for ICTs in Kenya are changing which reflects the government’s commitment to being part of the information society, especially in rural communities. Despite this, a gap exists between the aspirations of policymakers and the reality in rural areas owing to the poor state of ICT and general infrastructure.
Web app for quick evaluation of subjective answers using natural language processing

In current digital climate, education sector is evolving as the computer technology advances.Education is being digitized: online classes, online examination methods are conducted, etc.During examination, students are assessed by their answers having given for the question set by a teacher.Today many tools are available to assess the performance of a student using multi choice questions tools which provide instant evaluation, but there are available very limited and operational tools where subjective type answer of students are evaluated.This paper presents a web-based application to address this challenge.It automates the process of subjective answers checking and generates results through using natural language processing methods, like keyword matching semantic, lexical analysis and cosine similarity.Experiments show that appreciated by the teacher result and the system estimation does not have much difference which signifies that the system evaluates answers with a 97 % accuracy.The presented system not only reduces manpower but also eliminates the traditional method of conducting exclusively subjective exams using paper documents.It also eliminates the delays in the paper checking, result generation process.The cases of information leak are being reduced and the objectivity of the assessment is being increased.
‘EDUTUBERS’. ESTUDIO COMPARATIVO DE LA COMUNIDAD ANDINA Y ESPAÑA

Nowadays, the impact of Information and Communication Technologies on society is a fact, and education is no stranger to this reality. One of the main platforms that currently has a great impact within the educational field is YouTube, where the figure of the educational YouTuber or edutuber stands out. The objective of this paper is to find out which are the most relevant edutubers in both, the Andean Community, and Spain. To this end, a mixed methodology has been used, combining both qualitative and quantitative strategies to analyze a total of forty channels, twenty of them from the Andean Community, and twenty from Spain. Among the results obtained, it is worth mentioning that the ages of the edutubers analyzed are from 23 to 50 years old. Educational channels are under construction and have not yet reached the level of subscribers of other types of channels. In conclusion, it is important to highlight how, through edutubers and their channels, education and the way in which students approach it are changing.
Modelling of basic Indonesian Sign Language translator based on Raspberry Pi technology

Deaf people have hearing loss from mild to very severe.Such people have difficulty processing language information both with and without hearing aids.Deaf people who do not use hearing aids use sign language in their everyday conversations.At the same time, it is difficult for general people to communicate with the deaf, so in order to communicate with the deaf they must know sign language.There are two sign languages in Indonesia, namely SIBI (Indonesian Sign Language System) and BISINDO (Indonesian Sign Language).To help with communication between deaf and normal people, we developed a model using the one-handed SIBI method as an example, and then further developed it using the one-handed and two-handed BISINDO.The main function of the method is the recognition of basic letters, words, sentences and numbers using a Raspberry Pi single-board computer and a camera which are designed to detect the movements of language gestures.With the help of a special program, images are translated into text on the monitor screen.The method used is image processing and machine learning using the Python programming language and Convolutional Neural Network techniques.The device prototype issues a warning to repeat the sign language if the translation fails, and delete the translation if it doesn't match the database.The prototype of the device requires further development providing its flexibility: to provide reading of dynamic movements, facial expressions, to provide translation of words not included in the existing database.You need to add a database other than SIBI, such as BISINDO, or sign languages from other regions or countries.
Multivocal Literature Review on the Security of DevSecOp

DevOps revolutionize the software development lifecycle by providing agile and fast-paced solutions. DevOps ignores security prospects since it focuses only on increasing development and speed. DevSecOp is a notion of implanting Security into DevOps operation without distressing its agile nature by discovering contemporary security practices. This research aims to reveal a comprehensive overview of DevSecOp. Here we presented a brief overview of the research methodology. Afterward, it presented the method of gathering the required information. This research paper is distributed in the following section. Section II presented the research methodology, while section III provided results from this study. In the end, we conclude our research&#x0D; In this study, we discover essential DevSecOp concepts, leverages of DevSecOp, and potential research challenges in implementing it. We used a Multivocal literature review to explore the aforementioned subjects. For this Multivocal literature review, we searched grey data and, after processed that data, found answers to our research questions. This review concluded that DevSecOp, although challenging to implement, can be a constructive addition to the DevOps paradigm.&#x0D; DevSecOp is a relatively new concept that is not even fully concise in its name and definition. The key idea of DevSecOp is to implant security into DevOps procedures to make them more secure. We presented MLR on DevSecOp, keeping in mind pre-designed research questions. Since DevSecOp is not as popular and does not contain enough academic literature, we had to include grey data for our literature review. This MLR concluded that DevSecOp is mainly defined as integrating Security into DevOps
Quantum-probabilistic SVD: complex-valued factorization of matrix data

The paper reports a method for compressed representation of matrix data on the principles of quantum theory.The method is formalized as complex-valued matrix factorization based on standard singular value decomposition.The developed approach establishes a bridge between standard methods of semantic data analysis and quantum models of cognition and decision.According to the quantum theory, real-valued observable quantities are generated by wavefunctions being complex-valued vectors in multidimensional Hilbert-space.Wavefunctions are defined as superpositions of basis vectors encoding composition of semantic factors.Basis vectors are found by singular value decomposition of the initial data matrix transformed to a real-valued amplitude form.Phase-dependent superposition amplitudes are found to optimize approximation of the source data.The resulting model represents the observed real-valued data as generated from a small number of basis wavefunctions superposed with complex-valued coefficients.The method is tested for random matrices of sizes from 3 × 3 to 12 × 12 and dimensionality of latent Hilbert-space from 2 to 4. The best approximation is achieved by encoding latent factors in normalized complex-valued amplitude vectors interpreted as wavefunctions generating the data.In terms of approximation fitness, the developed method surpasses standard truncated SVD of the same dimensionality.The mean advantage over the considered range of parameters is 22 %.The method permits cognitive interpretation in accord with the existing quantum models of cognition and decision.The method can be integrated in the algorithms of semantic data analysis including natural language processing.In these tasks, the obtained improvement of approximation translates to the increased precision of similarity measures, principal component analysis, advantage in classification, and document ranking methods.Integration with quantum models of cognition and decision is expected to boost methods of artificial intelligence and machine learning improving imitation of natural thinking.
Experiencia del uso de las TIC con nativos digitales

Nowadays, the society is involved in Media. The students receive such amount of stimulus that it is difficult to catch their attention. The arrival of digital natives requires the integration of digital education platforms in the academic training. This proposal has been developed though a control group uses the traditional teaching method, while another experimental group incorporates the use of digital platforms. Both groups were analysed. Moreover, it has been worked with students of Primary Education through the development of a blog parallel to the textbook. Social Sciences taught as subject. In addition, the evaluation method has carried out, through the application of M-learning, thanks to a questionnaire creation platform, Kahoot. It makes easy the access to more information. As a result of the use of ICTs in class, the global vision qualifies positively the use of gamification in education. Finally, unaware of final evaluations, the students learnt without being conscious.
Evaluation of Comprehensive Services of an Online Learning Platform Based on Artificial Intelligence

With the college students having increasing needs for learning diversified knowledge and skills, online learning platforms for essential qualities enhancement have emerged one by one. However, due to lack of feedbacks from students, the comprehensive service quality of these platforms varies greatly. Therefore, it has certain practical significance to study how to evaluate the comprehensive services of online learning platforms. The current evaluation models are not suitable for online learning platforms, nor have they fully considered the dynamic and subjective feedbacks of students about their experience. To this end, this paper takes an online music education platform as an example and studies the evaluation on the comprehensive services of the online learning platform. First, the overall architecture design of an online learning platform for essential qualities enhancement was displayed, and the teacher-student interaction mode for the comprehensive services of the online learning platform was identified and analyzed. Then, the derivation process of the evaluation model was presented, the evaluation indicator system for the comprehensive service quality of the online learning platform constructed, and the comprehensive service evaluation model for the online learning platform consisting of the hierarchical model and the rough set-neural network evaluation model established. The experimental results verified the effectiveness of the constructed indicator system and evaluation model.
IMPLEMENTASI METODE TOPSIS PADA DECISION SUPPORT SYSTEM UNTUK PENILAIAN MAHASISWA BERBASIS PRESTASI AKADEMIK DAN NON AKADEMIK

Today the development of the industrial world and technology is growing rapidly. The development of these two fields has a significant impact on all fields of human activity, one of which is the field of education. The influence of the times has pushed the output of higher education to be required to be able to compete and advance the country in the face of competition in the industrial era 4.0. Students in this case as outputs need to hone their academic and non-academic abilities so that they are ready to face the future in the current era of competition, so it is necessary to make an assessment of students to realize these goals. In addition, it is necessary to conduct student assessments in order to provide achievement motivation among students and create a better academic culture. In addition, it is hoped that this selection process can be adopted as a system for fostering achievement in higher education. Assessment of outstanding students in higher education can be done by using a qualified computing infrastructure. One of the mathematical methods that can be developed to assess outstanding students is the TOPSIS method. This method will later be implemented with a Decision Support System that is able to act as a data processor because it is facilitated by a database. From the results of the preference scores, several students with the highest scores were produced. Assessment based on the Decision Support System proves the effectiveness of mathematical calculations using the TOPSIS method rather than using manual methods that only use human power.
Evaluation of Refractory Metal Concentrations in Nano-Particulate Pressed-Powder Pellets Using LA-ICP-MS

Whole-rock geochemical analysis is a standard method to measure the chemical composition of ores. Analysis of refractory ore metals such as Ta and W typically requires fused bead and acid digestion followed by inductively coupled plasma atomic emission spectrometry (ICP-AES) and inductively coupled mass spectrometry (ICP-MS). Since these techniques are time-consuming and expensive, there is a demand for methods that can quantitatively measure low elemental concentration of refractory ore metals using a less expensive and simple approach. This paper evaluates preparation and analytical procedures developed to obtain whole-rock element concentrations of ore samples and mineral concentrates. It shows that the production of nano-particulate pressed-powder pellets followed by LA-ICP-MS analysis of W and Ta ores can be used to determine, within the error margin, the concentrations of the refractory metals W, Ta, Nb, and Sn compared to a reference values obtained by solution analysis. The results have implications for developing a commercially viable method for analysis of refractory elements to benefit mineral processing given the simplicity and resource-efficiency of the combined pressed pellet production and laser ablation analytical methodology.
SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans

In this paper, we propose a novel squeeze M-SegNet (SM-SegNet) architecture featuring a fire module to perform accurate as well as fast segmentation of the brain on magnetic resonance imaging (MRI) scans. The proposed model utilizes uniform input patches, combined-connections, long skip connections, and squeeze-expand convolutional layers from the fire module to segment brain MRI data. The proposed SM-SegNet architecture involves a multi-scale deep network on the encoder side and deep supervision on the decoder side, which uses combined-connections (skip connections and pooling indices) from the encoder to the decoder layer. The multi-scale side input layers support the deep network layers' extraction of discriminative feature information, and the decoder side provides deep supervision to reduce the gradient problem. By using combined-connections, extracted features can be transferred from the encoder to the decoder resulting in recovering spatial information, which makes the model converge faster. Long skip connections were used to stabilize the gradient updates in the network. Owing to the adoption of the fire module, the proposed model was significantly faster to train and offered a more efficient memory usage with 83% fewer parameters than previously developed methods, owing to the adoption of the fire module. The proposed method was evaluated using the open-access series of imaging studies (OASIS) and the internet brain segmentation registry (IBSR) datasets. The experimental results demonstrate that the proposed SM-SegNet architecture achieves segmentation accuracies of 95% for cerebrospinal fluid, 95% for gray matter, and 96% for white matter, which outperforms the existing methods in both subjective and objective metrics in brain MRI segmentation.
The Design of Android-Based Interactive Lean Manufacturing Application to Increase Students' Work Skill in Vocational High School: The Development and Validity

Vocational education graduates should have expertise competencies according to their fields so that it is easy to fill the job market, but this condition is not in line with expectations. BPS data as of February 2020 still shows the open unemployment rate, which is dominated by SMK graduates, at 8.49%, meaning that it is the highest-ranking contributor to open unemployment. One indication of this high percentage is the lack of work skills for vocational school graduates in the world of work. This lack of work competence is indicated because the learning process is not integrated with the real world of work. Therefore, improvement steps are sought through a learning process that integrates school learning with the real world of work by developing media that support learning activities with the real world of work. Therefore, the purpose of this research is to design an Android-based interactive lean manufacturing application to improve the work skills of vocational students. This study uses the 4D development method (four-Ds) for the development of android-based interactive lean manufacturing applications and uses a survey method for validity and this AR application will be validated by 3 experts. The results of this study indicate that the validation test of android-based interactive lean manufacturing applications is included in the valid category. So, it can be concluded that the Android-based interactive lean manufacturing application technology has been valid to be used in the learning process.
A Generic Taxonomy for Steganography Methods

&lt;p&gt;A unified understanding of terms and their applicability is essential for every scientific discipline: steganography is no exception. Being divided into several domains (for instance, text steganography, digital media steganography, and network steganography), it is crucial to provide a unified terminology as well as a taxonomy that is not limited to some specific applications or areas. A prime attempt towards a unified understanding of terms was conducted in 2015 with the introduction of a pattern-based taxonomy for network steganography. Six years later, in 2021, the first work towards a pattern-based taxonomy for steganography was proposed. However, this initial attempt still faced several shortcomings, e.g., the lack of patterns for several steganography domains (the work mainly focused on network steganography and covert channels), various terminology issues, and the need of providing a tutorial on how the taxonomy can be used during engineering and scientific tasks, including the paper-writing process.&lt;/p&gt; &lt;p&gt;As the consortium who published this initial 2021-study on steganography patterns, in this paper we present the first comprehensive pattern-based taxonomy tailored to fit all known domains of steganography, including smaller and emerging areas, such as filesystem steganography and cyber-physical systems steganography. Besides, to make our contribution more effective and promote the use of the taxonomy to advance research on steganography, we also provide a thorough tutorial on its utilization. &lt;/p&gt; &lt;p&gt;Our pattern collection is available at &lt;a href="https://patterns.ztt.hs-worms.de/" target="_blank"&gt;https://patterns.ztt.hs-worms.de&lt;/a&gt;.&lt;/p&gt;
Effects of online home-based exercise on metabolic syndrome and health-related physical fitness factors in 20"s women

Since the spread of COVID19, participation of home-based exercise has expected to increase as more people work from home and spend time with their families. In this study, 35 healthy women in their 20s were conducted to identify the effects of online home-based exercise programs on metabolic syndrome and health-related physical fitness factors for 12 weeks. The metabolic syndrome-related factors waist circumference, triglyceride, HDL-C, blood pressure, and fasting blood sugar were investigated. For health-related factors physical fitness, leg strength, muscular strength, cardiorespiratory endurance, muscular endurance, and flexibility were investigated. regarding metabolic syndrome-related factors, the exercise group and the control group showed a difference in waist circumference before and after exercise. regarding health-related fitness factors, the exercise group and the control group showed a difference in cardiopulmonary endurance (20M shuttle run), Muscular endurance (sit-up), flexibility (sit-and-reach) before and after exercise. Combining these results, the Home-based online exercise program helped prevent metabolic syndrome and relieve symptoms, and helped positively change health-related variables.
Tracing security requirements in industrial control systems using graph databases

Abstract We must explicitly capture relationships and hierarchies between the multitude of system and security standards requirements. Current security requirements specification methods do not capture such structure effectively, making requirements management and traceability harder, consequently increasing costs and time to market for developing certified ICS. We propose a novel requirements repository model for ICS that uses labelled property graphs to structure and store system-specific and standards-based requirements using well-defined relationship types. Furthermore, we integrate the proposed requirements repository with design-time ICS tools to establish requirements traceability. A wind turbine case study illustrates the overall workflow in our framework. We demonstrate that a robust requirements traceability matrix is a natural consequence of using labelled property graphs. We also introduce a compatible requirements change management procedure that aids in adapting to changes in development and certification schemes.