Reviewerly/exHarmony huggingface dataset:
Appreciation of ICT Tools in Higher Education in Times of Pandemic

The use of technologies in education has been occupying important spaces. In the time of pandemic that was experienced worldwide, these became more important and even essential. In university education, some actors were prepared while others had a hard time adapting to this environment. Some students were attracted to its use, and it helped facilitate their education, while others were not benefited by such attraction. At the Private Technical University of Loja, Ecuador, education is taught both in distance and face-to-face mode. In the case of the latter, students are more attracted to traditional education. This research seeks to determine the relationship between satisfaction with the use of technological resources and satisfaction with the knowledge acquired using technological tools. Students of the face-to-face modality are taken as a sample, in which different ICT tools are incorporated in the teaching and evaluation process. To determine this relationship, non-parametric tests are applied and interpreted for the analysis of correlations and variances. The results obtained show a relationship between satisfaction with the use of technological resources and satisfaction with the knowledge acquired using technological tools.
Image Denoising for Satellite Imagery Using Amalgamated ROAD-TGM and PCA Algorithm

Satellites are widely used for surveillance and natural resource management. Satellites are also used for defense applications as they provide effective and economical solution for communication and surveillance-based applications. The role of satellite is very crucial for applications of disaster management where satellite imagery is of utmost use. The satellite imagery is often affected by the noise due to malfunction of semiconductors and influence of noise during the image communication. This reduces the performance of all the applications based on satellite imagery due to noisy input images. To overcome this issue, amalgamated ROAD-TGM and PCA algorithm is presented in this paper. This algorithm uses the advantages of spatial and transform domain together to improve the accuracy of noise detection. The standard dataset of earth dataset provided by NASA is used to analyze the performance of proposed algorithm. PSNR and SSIM parameters are used to evaluate the performance of proposed algorithm with existing stage of the art algorithms. The proposed method outperforms the existing methods satellite image denoising.
App and Classroom Instruction for Better Pronunciation: Some Results

Various studies show the positive impact of computer-assisted training on pronunciation, but it is important to evaluate how the use of mobile apps, easily available online, can influence pronunciation development. If helpful, the use of these gamified tools could boost the time-on-task and the effects of classroom instruction. Consequently, the goals of this study are (1) to assess the impact on pronunciation of a teaching strategy that combines the use of a mobile app and classroom instruction, and (2) to propose an improved model of the strategy “App + Class = better pronunciation”. The effects, on pronunciation, of using the app “Fun Easy Learn European Portuguese—6000 Words” as an informal learning activity outside the classroom—combined with face-to-face classroom instruction—over a 2-week period are observed. The participants are 12 Chinese native speakers learning Portuguese as a Foreign Language as 1st year undergraduate students majoring in Portuguese Language. A pretest and a posttest assessed the participants’ pronunciation of words included, and non-included, in the app. A questionnaire presented after the posttest explored the users’ views and experience on this learning activity and the properties of the app. The main findings show a significant improvement in the pronunciation of the words “App + Class” and “App-only” but not in the “Class-only” ones, and the students’ belief that using the app promotes pronunciation learning. Considering the results, several recommendations are proposed for an improved model of the strategy “App + Class = better pronunciation”.
Security Risk Management and Cybersecurity: From the Victim or from the Adversary?

Cybersecurity and information security risks are generally addressed following the guidelines of current standards and best practices, which traditionally point to ISO 31000 risk management. This key exercise to ensure the "known" risks is carried out in a reactive perspective, since not only the risks are known and treated, but also the organization is prepared when one of them materializes. Thus, it is necessary to advance in cyber risk management from the adversary's perspective. That is, to understand both the intentions and their capabilities to get out of the comfort zone of standards, and mobilize the organization to another framework that seeks to detect, deter, delay, confuse and anticipate the adversary. Therefore, this article develops two perspectives of security and cybersecurity risk management (from the victim and the adversary), in order to tune efforts around the treatment of known risks and the development of capabilities to maintain a vigilant posture towards latent and emerging risks, allowing the organization navegate throughout the uncertainties and instability of the current international context.
MAYA: Exploring multiform attributes of node to align YANG data models

Nowadays Network Configuration Protocol (NETCONF) has become an essential part of network management for its flexibility and scalability. Vendors intend to use NETCONF to replace Command-Line Interface (CLI) and Simple Network Management Protocol (SNMP) to manage devices. Yet Another Next Generation (YANG) models are tailored to model NETCONF protocol messages. However, there exists heterogeneity issue with YANG models as a consequence of vendors proposing proprietary YANG models which differ from each other in structure or content. Thus, managing network devices from different vendors requires expert knowledge and plenty of resources. In this paper, we present MAYA, a solution to automatically accomplish alignment of YANG models from different vendors or organizations by exploring multiform node attributes. In MAYA, different semantic similarity techniques are used to measure distance between different attributes, such as name, description and type, in nodes from various YANG models. A customized SMP based matching algorithm for YANG models alignment is proposed to generate mapping relations between models based on the semantic similarity. The real cases analysis and experiments show that MAYA is able to meet the demands in production on the problem of YANG model alignment.
Higher Education in Philippines

This chapter presents an overview of the current state of the higher education sector in the Philippines. It also highlights key challenges, including access, inclusion and equity, and quality of higher education. Inequitable and inadequate household income issues bring about the abovementioned challenges, the lack and poor implementation of laws and policies for inclusion and equity, the profit-seeking nature of nonsectarian private higher education institutions, and the varying quality of higher education institutions and programs. Recent developments in the sector include the Universal Tertiary Education Act of 2017 (granting free tuition and miscellaneous fees for first-degree students studying at public higher education institutions), ongoing international benchmarking (with the shift to outcomes-based competency standards), and multistakeholder participation in developing minimum curriculum standards. Such initiatives have partly addressed access, quality, and relevance issues in Philippine higher education. The Government’s role through the Commission on Higher Education and the alignment of the higher education sector with the Philippine Development Goals and international commitments, including the UN 2030 Sustainable Development Goals, are presented. The key trends in Philippine higher education include an increased focus on the internationalization of higher education, enhancing global and regional partnerships, and the shift to outcomes-based competency standards. The utilization of Information and Communication Technology (ICT), which the COVID-19 pandemic has accentuated, is also discussed.
The AmTriangle Meta-Dataset for Playing with Machine Learning

AmTriangle is a meta-dataset for machine learning (ML) that supports convenient and configurable dataset production in addition to a classifier that performs supervised learning. The idea is to have a never-ending source of supervised datasets, minimizing learning barriers to ML, for training models, and facilitating educational experiments and comparisons between different workflows. It is a “meta-dataset” because it is a solution to generate sets of samples (datasets), as many as desired, correctly classified. Each sample is a triangle, classified as “acute”, “obtuse” or “right”, according to trigonometry. Each generated dataset can be used to “teach-by-example” how to classify new samples, by different techniques, namely K-Nearest-Neighbors and neural networks. The triangles could be other analogous objects, other tuples.
Web Page to Promote the Values of School Coexistence in Basic Education Students

The incorporation of technologies in educational settings has led to changes that have affected the behavior of people, in education it is due to a current need to address this issue of significant relevance such as the lack of practice of ethical values in educational institutions, where it is perceived that students do not apply values. The main objective of this work was with 57 students whose age fluctuates between 10 and 14 years, for which an observation sheet was applied To know if the students apply or not values in the institution, the analysis of the results showed that there are problems of coexistence in the classroom, and in some cases acts of violence among the students are included, conflict situations that in many occasions last longer. Beyond class hours, almost all students show undesirable behaviors in some period of their development, this pattern varies according to the teacher, parent, situation or period. For this reason, the need arises to create web pages as an instructional and pedagogical resource to improve communication in the teaching–learning process of basic education students.
Contribution of Social Networks in Learning: A Study in Higher Education

Social networks are the most used technological platforms in the information and knowledge society, and especially among young people. Due to their importance, higher education educational units have considered them as strategic learning tools. This article deals with the use of social networks and their relationship with group work and academic performance. The methodological approach used was quantitative, the data were contrasted through a descriptive study, where a previously validated questionnaire was applied to 234 students from different degrees in the administrative area of the Universidad Técnica Particular de Loja with a Likert questionnaire (1–5). When analyzing the results through structural equation modeling, this study found that there is a positive relationship between the use of social networks and group work, in the same way between the use of social networks and academic performance. Students use social networks mainly because they are flexible, allow interaction with other people, share information, achievements, strengthen motivation and develop the ability to work collaboratively.
Usable Identity and Access Management Schemes for Smart Cities

Usable Identity and Access Management (IAM) schemes are highly required to control and track users’ identity and access privileges for a safe and secure smart city. Any safety or security breach in critical infrastructures, e.g., smart financial solutions, smart transportation, and smart buildings, can disrupt the normal life of its residents. Studies have reported that traditional knowledge- and token-based IAM schemes are unable to fully secure these emerging use cases due to inherent security and usability issues in them. This chapter presents multi-modal biometric-based IAM schemes for smart payment apps, smart transportation, and smart buildings that can partially address the safety and security concerns of residents. We also describe the framework for designing risk-based, implicit, or continuous verification IAM schemes for such use cases.
Graph Databases Assessment: JanusGraph, Neo4j, and TigerGraph

Over time, data growth across technology platforms has become a challenge for relational databases. Therefore, it becomes difficult to store and process massive volumes of data and data where the schema is represented by graphs, such as social networking sites. Graph databases emerged as the solution, wherein entities from the domain of interest are represented by nodes and relationships between them by edges. The main objective of this paper is to find out which of the top three open source graph databases are the most complete and efficient. According to DB-Engines Ranking, the top three graph databases are: JanusGraph, Neo4j, and TigerGraph. We apply the OSSpal methodology, which consists of an evaluation based on qualitative and quantitative measures to these databases. The evaluation categories of the OSSpal methodology are focused on software functionalities, attributes and features, available documentation, support and service, user community, and graph database development.
Learning Graphics in Supervised Teaching Practice in an Educational COVID-19 Context

The pandemic caused by COVID-19 has brought a sudden change in society and education. Thus, it was necessary to readapt the management of resources and knowledge already acquired to create new environments for the teaching and learning process. In this new paradigm, it was imperative not to give up and the watchword was to differentiate, as each child was an individual being with their own needs and motivations. On the other hand, it was vital to continue to promote educational experiences that articulated knowledge, with special emphasis on Mathematics, an unloved subject for many children. Based on these conceptions, in the Supervised Teaching Practice of the master’s in teaching of the 1st CEB and of Mathematics and Natural Sciences in the 2nd CEB, it was important, in this distance learning period, to study what is the influence on the learning of the content on Statistics based on the child’s real-life data. The methodology used was the case study which was implemented in a classroom and the technics implemented were the qualitative and interpretive. This teaching strategy was significant for the child, and a pedagogical differentiation promoted since different individual teaching activities applied for the use of new educational platforms. At the end, beneficial consequences for children were observed specifically on significant mathematical learning.
The Future Era of Quantum Computing

This paper will cover the impact of quantum computing and how it will affect the entire landscape, from security to ecommerce and even technologies such as the blockchain. First, it introduces the basics of encryption and compares it to classical computing and how it will differ from the quantum era of computing. The older style of encryption, when used in classical computing, will be defenseless in the age of quantum computing. We go over different types of protections and other theories to protect such attacks if and when they occur from a quantum computer. Our research paper also covers decryption comparing how classical computing decryption can be done manually or even automatically. Still, with the introduction of quantum computing, it would have to utilize quantum mechanical principles to perform cryptography. In addition this paper will also go over how big companies such as IBM and their technology strive to be ahead of their competitors in regards to advancement in the quantum space. One of the biggest companies, such as IBM, is investing in the quantum computing field with a power of 64 Qubits, and it is still in progress of advancement in Qubits. Quantum computing will also bring many benefits, such as fixing old security protocols and encryption issues. It will also solve problems such as key encryption or even make older encryptions stronger when running on a quantum system. In addition, cyber security with quantum computing will come with some negatives which will weaken all classical types of security, such as RSA, DSA, and ECDSA. It can keep trying to compute until it cracks through these classical security protocols. Ecommerce in the future will be handled through quantum computing and more advanced security protocols. Blockchain technology emerged with Bitcoin and is used for secure ways to do transactions and communication between two users. The technology relies more on classical cryptography protocols, and emerging quantum computing will create security issues including areas of blockchain data transparency. As the conclusion on quantum computing which requires future work to be done for implementation of effective strategies to push the classical computing technologies to the quantum era.
Personalized and Energy-Efficient Health Monitoring: A Reinforcement Learning Approach

We consider a network of controlled sensors that monitor the unknown health state of a patient. We assume that the health state process is a Markov chain with a transition matrix that is unknown to the controller. At each timestep, the controller chooses a subset of sensors to activate, which incurs an energy (i.e., battery) cost. Activating more sensors improves the estimation of the unknown state, which introduces an energy-accuracy tradeoff. Our goal is to minimize the combined energy and state misclassification costs over time. Activating sensors now also provides measurements that can be used to learn the model, improving future decisions. Therefore, the learning aspect is intertwined with the energy-accuracy tradeoff. While Reinforcement Learning (RL) is often used when the model is unknown, it cannot be directly applied in health monitoring since the controller does not know the (health) state. Therefore, the monitoring problem is a partially observable Markov decision process (POMDP) where the cost feedback is also only partially available since the misclassification cost is unknown. To overcome this difficulty, we propose a monitoring algorithm that combines RL for POMDPs and online estimation of the expected misclassification cost based on a Hidden Markov Model (HMM). We show empirically that our algorithm achieves comparable performance with a monitoring system that assumes a known transition matrix and quantizes the belief state. It also outperforms the model-based approach where the estimated transition matrix is used for value iteration. Thus, our algorithm can be useful in designing energy-efficient and personalized health monitoring systems.
Privacy preservation satisfying utility requirements based on multi-objective optimization

Data publishing requires privacy preservation maintaining utility. Therefore, it is necessary to balance the tradeoff between privacy (security) and utility. To achieve this, it is necessary to set parameters appropriately for data anonymization. This step usually requires trial and error by hand, but it is more convenient to automatically anonymize data for frequent data publication. We propose an automatic parameter-tuning method that maintains the required utility while preserving privacy by using multi-objective optimization with objective functions of the index to measure privacy level and utility with design parameters for anonymization. We use the non-dominated sorting genetic algorithm II to balance the above tradeoff. Experiments on diabetes data showed that our method maintains utility while preserving privacy and can obtain non-dominated solutions that balance the tradeoff. Our method includes various anonymization techniques such as top-bottom coding, k-anonymization, outlier removal, random replacement, and differential privacy.
E-Learning Migration Model for Cloud-based Education

Cloud computing is one of the technological advancements that have led to rising prospects in the field of e-learning and has become necessary. E-learning is mostly dependent on technology; it necessitates the use of learning management systems that can be hosted on cloud computing platforms to provide alternative solutions to higher education institutions' inadequate infrastructure. In this paper, we proposed a new migration education model that can be used by educational institutions to migrate from the traditional e-learning system to the cloud-based e-learning system. The proposed model takes into account the education and e-learning education requirements to complete and plan for migration, the model divides the migration process into three phases namely, Pre-migration, migration, and post-migration. Finally, we consider in the education migration model several points that will be taken in the proposed model. The proposed model focus on the importance of cloud computing as a solution for e-learning systems in higher education institutions.
Cloud Ubiquitous Learning Approach Based on Multi-agents System

Information technology (IT) plays an important role in the education field, with ubiquitous computing technology the ubiquitous learning (U-Iearning) is now a practical possibility. Cloud computing can offer many benefits to the learning field, as it provides all resources such as software (SaaS), platforms (PaaS), and infrastructure (IaaS) as a service. The multi-agent systems approach is an excellent way to implement flexible systems whose structure evolves dynamically. Based on the discussion of the benefits of cloud computing and the multi-agent systems approach to support adaptive and personalized learning content, the paper presents a hybrid approach based on cloud computing that uses the multi-tenant architecture where tenants share the system software. Multi-agent system is used in this approach to support a ubiquitous learning system. A multi-tenant SaaS application addresses the unique requirements of each tenant by enabling customization at the tenant level with the adoption of the concept ofvirtualization (virtual machines). This paper explores the challenges of achieving performance isolation in the context of multi-tenant SaaS applications. As an output of this study, U- learning on the cloud-based multi-agent system architecture has been proposed. To achieve lower cost of resources and server allocation and provide isolation among learners in the ubiquitous learning environment.
A Classifier to Detect Stream Applications Based on Network Traffic Statistical Metrics

One of the most crucial considerations, when considering security vulnerabilities, is network traffic. There is still potential for more research on the inter-arrival time side, even though some studies concentrate on network traffic from the perspective of the packet fields such as packet length and packet number. Inter-arrival timings are crucial to investigate because there are numerous attacks, such as Covert Timing Channels attacks, that heavily rely on them. In this article, we conduct a statistical analysis of the TCP inter-arrival times of two major key streaming programs (Zoom and Skype), which are frequently used, particularly during and following the COVID-19 pandemic. Using two internet-connected devices and the statistical measures of TCP, a dataset of 18,371 instances is created for this use. Five machine learning algorithms are evaluated on balanced and imbalanced forms of the dataset. The results revealed that the traffic of Zoom and Skype calls can be identified by machine learning algorithms with an accuracy of up to 99% by random forest.