Reviewerly/exHarmony huggingface dataset:
Retracted: Role of Human-Computer Interaction Healthcare System in the Teaching of Physiology and Medicine

[This retracts the article DOI: 10.1155/2022/5849736.].
Literacy in Indian Akshara Languages and Other Transparent Orthographic Languages - Teacher Education Considerations

Alphabet-based languages are more often researched in literacy acquisition and education than akshara languages. Languages that use alphasyllabaries including symbols, called aksharas, represent a large portion of the world languages, including the languages in the second most populous country, India. This conceptual research paper addresses teacher education in literacy related to akshara languages. Using theory and research base of existing alphabet and akshara acquisition, with teacher education standards for literacy in alphabet languages, this paper presents a model for teacher education in literacy for akshara languages. This framework provides teacher education standards and other considerations such as evaluation of teacher education curriculum and performance to enable data-based decision making in literacy instruction. The premise of this paper is to approach the problem of dismal literacy rates by drawing into the robust research in alphabetic language literacy education by using a systematic approach to target the source – pre-service teacher education. While the paper addresses examples of languages in India, other transparent orthographies that use symbols or aksharas can draw from this to inform their teacher education in literacy
Cross-network Embeddings Transfer for Traffic Analysis

Artificial Intelligence (AI) approaches have emerged as powerful tools to improve traffic analysis for network monitoring and management. However, the lack of large labeled datasets and the ever-changing networking scenarios make a fundamental difference compared to other domains where AI is thriving. We believe the ability to transfer the specific knowledge acquired in one network (or dataset) to a different network (or dataset) would be fundamental to speed up the adoption of AI-based solutions for traffic analysis and other networking applications (e.g., cybersecurity). We here propose and evaluate different options to transfer the knowledge built from a provider network, owning data and labels, to a customer network that desires to label its traffic but lacks labels. We formulate this problem as a domain adaptation problem that we solve with embedding alignment techniques and canonical transfer learning approaches. We present a thorough experimental analysis to assess the performance considering both supervised (e.g., classification) and unsupervised (e.g., novelty detection) downstream tasks related to darknet and honeypot traffic. Our experiments show the proper transfer techniques to use the models obtained from a network in a different network. We believe our contribution opens new opportunities and business models where network providers can successfully share their knowledge and AI models with customers.
Managing Application Secrets

Throughout this book, I've used sensitive data, a.k.a. secrets, for example, connection strings that may have been used to connect to a database, an APM, etc. The easiest way to make these secrets available is to save them in application configuration files named by environment (appsettings.json, appsettings.dev.json, appsettings.prod.json, a dedicated configuration file for each environment) and then push all these files into the GIT repository. GIT is a source code manager on a remote server to store your code. However, two questions arise: How/where to store these secrets, and how to update them easily?
Going Further with Clean REST APIs

Congratulations, reader! You made it through Chapter 4, which was packed with content! I showed you all the basics you could implement in an API in your career. In this new chapter, we'll take you a step further with ASP.NET Core 8. Everything we've covered in Chapter 4 is still valid. Still, I suggest you enhance your API with features that will enable you to make your API more structured, more elegant, and easier to evolve, offering your customers a better user experience and making them safer. In this chapter, I'm going to teach you the following:
Research on the Application of Random Forest-based Feature Selection Algorithm in Data Mining Experiments

Handling high-dimensional big data presents substantial challenges for Machine Learning (ML) algorithms, mainly due to the curse of dimensionality that leads to computational inefficiencies and increased risk of overfitting. Various dimensionality reduction and Feature Selection (FS) techniques have been developed to alleviate these challenges. Random Forest (RF), a widely-used Ensemble Learning Method (ELM), is recognized for its high accuracy and robustness, including its lesser-known capability for effective FS. While specialized RF models are designed for FS, they often struggle with computational efficiency on large datasets. Addressing these challenges, this study proposes a novel Feature Selection Model (FSM) integrated with data reduction techniques, termed Dynamic Correlated Regularized Random Forest (DCRRF). The architecture operates in four phases: Preprocessing, Feature Reduction (FR) using Best-First Search with Rough Set Theory (BFS-RST), FS through DCRRF, and feature efficacy assessment using a Support Vector Machine (SVM) classifier. Benchmarked against four gene expression datasets, the proposed model outperforms existing RF-based methods in computational efficiency and classification accuracy. This study introduces a robust and efficient approach to feature selection in high-dimensional big-data scenarios.
Fortifying Against Cyber Fraud: Instrument Development with the Protection Motivation Theory

Cybersecurity has become a trending topic in this technological era. Crimes keep happening in this medium and bring challenges for researchers and IT professionals worldwide to find the best solution to overcome this issue. Crimes primarily related to fraud on e-services have become a red alert that needs to be a concern for netizens. Instead of simply believing in the human-created network and system, individuals or users should acquire and implement protective behaviours for themselves. Thus, a few factors such as source credibility, perceived value of data, wishful thinking, perceived threat severity, perceived threat vulnerability, maladaptive rewards, and response efficacy have been investigated in this study, and the Protection Motivation Theory is used to counter cybersecurity issues faced by users. A tool has been created to facilitate the collection of empirical data necessary for verifying the proposed model. Analysis such as Content validity index (CVI) and Scale-level CVI (S-CVI) have been used to validate the item. The findings indicate that one of the items does not meet the criteria, however, it has been suggested by experts to revise and make it comprehensible to use for the main study. This paper also includes a discussion part regarding the implications of the experts' evaluation. This study, in particular, can help boost the understanding of cyber fraud and the proper methods, a user can employ to avoid becoming a victim.
China will scale computing infrastructure for AI

Significance To that end, in early October, the Ministry of Industry and Information Technology (MIIT), together with five other ministry-level government bodies, published an Action Plan for the High-Quality Development of Computing Infrastructure. This document sets important quantitative targets for data processing, data storage and data transmission. Impacts Diversification will take place within China's computing industry, with different data and computing centres catering for different sectors. The plan will hinge on continued access to foreign technologies and components, which will become ever more challenging. Bringing down the cost of computing is essential to the development of the digital industry.
ORIENTING BASIS FOR CHOOSING CLASSROOM TACTICS FOR STUDENTS AT EDUCATIONAL PRACTICE

Educational practice is an important part of professional training of students in many specialties. The live pedagogical process is diffi cult for trainee students. Those who have no experience in teaching or speaking in front of the audience worry a lot. Supervisors of pedagogical practice for students, particularly psychology students, in both Russia and Azerbaĳ an are convinced of this. The authors of the article faced these problems in -. To solve them, we developed an algorithm for selecting the tactics of teacher-practitioner (student- practitioner) behaviour according to the audience factor in traditional face-to-face teaching. The algorithm is also applicable to online classroom format. Algorithm of work tactics choice by the factor “audience composition and structure”: . Diagnose the structure of the audience by the factors of homogeneity, size, level of preparation. . Identify the presence/absence of a leader and/or a marginalised person in the audience. . Identify (fi nd) the “backbone” part of the audience. . Choose and stick to the tactics according to results of diagnostics. Following the rules of self-control. . Form and maintain the favorable climate in the audience, productive atmosphere of work. . Make conclusions at the end of the class-teaching about the productivity of the chosen tactics of behaviour. The algorithm is based on the principle of an indicative action framework. The “A” - action - is the action of choosing the tactics of the teacher-practitioner’s behaviour according to the factors of the classroom characteristics. The factors include size, homogeneity, profi ciency level, and general mood of the participants. The presented algorithm was implemented in the content of students’ preparatory work before pedagogical practice in - . Students applied this algorithm to choose strategies for their behaviour in practice. The authors of the article received acknowledgements from student practitioners who emphasised the effectiveness of the algorithm. However, the authors did not take separate measurements, so this text is offered as a theoretical model for students’ choice of tactics in their pedagogical practice.
The Effects of the Application of Graphic Organizers on EFL Students’ Ability to Write Opinion Essays and Self-Efficacy

This study examines the effectiveness of ready-to-use researcher-developed graphic organizers to boost students' abilities in writing opinion essays. The study was carried out in the COVID-19 pandemic situation, with the implementation of the teaching and learning process as part of a learning management system. Thirty-one students living in several regions of Indonesia were involved in the study. A Pre-Experimental One Group Pretest Posttest Design was applied in this study, and then descriptive statistics and N-gain scores as well as one way Anova were used to analyze the data. In the pretest and posttest, respondents wrote opinion essays. Furthermore, they filled out a questionnaire on writing self-efficacy. The results indicate a significant improvement in the means of the students as shown in the comparison of the pretest and posttest scores. In addition, the result shows that there is no effect of the application of graphic organizers on the students’ self-efficacy.
Research on a Symbolic Cluster Analysis Algorithm Approaching to the Optimal Value

Nowadays, there is a large amount of symbolic data in various fields, and people can fully utilize these symbolic data for clustering, providing a better foundation and direction for data mining and analysis. Currently, clustering algorithms for symbol data have emerged one after another, but there are still shortcomings in computational cost and algorithm robustness. Therefore, it is urgent to study an algorithm with stable clustering results, less time consumption, and low I/O overhead. The following proposes a symbolic cluster analysis algorithm that approaches to the optimal value. It reduces the size of the original data by generating a symbolic association graph from a large number of symbolic data samples, effectively solves the problem of high computing costs caused by the huge amount of data, and proves the clustering effect of the algorithm through empirical analysis.
Sentence Similarity Using Modified Latent Semantic Analysis and Semantic Relations

In this paper, we propose a method for finding the similarity of sentence pairs. The method combines two modules – a modified Latent Semantic Analysis and a semantic similarity computation. The proposed method makes use of the syntactic structure and semantic information contained in the sentence pairs. The syntactic structure in the form of dependency triplets is extracted and a semantic similarity calculation is carried out. The semantic similarity between words is calculated using Wu &amp; Palmer similarity measure and Wordnet synonym relation is used in modified Latent Semantic Analysis. The proposed method is evaluated on the Microsoft Research Paraphrase Corpus dataset and the accuracy obtained on the dataset is 73.19% which is better than existing statistical and zero shot domain adaptation methods. The proposed method is also tested on Li et al. text similarity dataset and the Pearson correlation coefficient of 0.9021, Spearman correlation of 0.9103 and mean deviation of 0.105 with the human judgement show that the method outperforms state-of-the-art methods.
Development and Analysis of High-Yield (HY-QKD) Protocol to Enhance the Key Generation Rate over a Multi-node Network.

Abstract Quantum key distribution is considered to be the most secure and reliable way of generating keys for public cryptography. Quantum key distribution allows users to send and receive data with infallible security. In this article,bench-marked protocols, the BB84, B92 and MKP16 protocols are studied and analysed with focus to overcome their limitations. The authors then attempt to address these shortcomings with their own novel QKD protocol i.e High- Yield (HY-QKD) Protocol.All these protocols have been implemented and tested using QISKIT,taking into consideration error rate and key length as parameters. Based on the comparison, it has been analysed that B92 protocol has the lowest key rate, MKP16 protocol has twice the key generation rate as that of BB84 keeping this simulation environment constant. Moreover, an algorithm to enhance key generation rate and error rate has been proposed. It has been inspected that the key generation rate and error rate of the HY-QKD protocol directly depend on the number of available channels and the number of Eve(s) attacking. If there are no Eve(s) in the system the key generation rates for HY-QKD is multiple times higher in comparison to that of BB84 validating the proposed HY-QKD protocol.
Machine Learning Algorithms for Attack and Anomaly Detection in IoT

With the invention of IoT and its range of smart applications, people’s life has been reformed drastically. IoT infrastructure consists of actuators and sensors that generate a massive volume of data that requires extensive computing. Due to the constrained nature of IoT devices, they can be easily exploited. Moreover, some of the IoT nodes behaves abnormally which makes the IoT infrastructure vulnerable that could be exploited by the adversary to gain unauthorized access or to perform other malicious deed. Thus, anomaly detection is a primary concern in the IoT infrastructure, and the investigation of IoT network for detecting anomalies is a fast-emerging subject. There is a plethora of techniques which have been developed by the researchers for detecting anomalies. In this article, we have emphasized on the machine learning-based anomaly detection techniques due to their ability to bring out accurate results and predictions. Further, to provide a detailed overview, we have categorized the machine learning-based anomaly detection techniques into unsupervised and supervised learning. This study helps the researchers to get a better idea of machine learning techniques that have been employed for the detection of anomaly in the IoT infrastructure.
A manifesto for the metaverse: opportunities and challenges for learning development

Drawing upon a section of the co-created Learning Development (LD) Manifesto (ALDinHE, 2018), in this workshop we invite participants to come and be creative – and imagine beyond what Learning Developers do now into what they may do in the future, inspired by the metaverse. The metaverse is a science fiction hypothetical iteration from the book ‘Snow Crash’ (Stephenson, 1992) set in a near future where the global political structure has collapsed (!), a tiny number of super-corporations control most aspects of life, and the rich spend their time in the metaverse (Ball, 2022).&#x0D; Today the metaverse is the Facebook-owned platform Meta, which Mark Zuckerberg (2021) explains as “an embodied internet where you’re in the experience, not just looking at it”. Rather than our current 2D, screen-based internet, the metaverse will be a 3D virtual space, accessed by either a VR headset or AR (augmented reality) glasses, which superimpose a layer of digital information on top of the visible world. What impact might this have on LD practices, knowledge and beliefs? The metaverse is highly contentious, and we invite Learning Developers to take the challenge and look to possible futures and their potential value to the sector. The educational possibilities of the metaverse will build from the UNESCO (2022) ‘Reimagining education’ discussion paper.&#x0D; There is also a need to focus the conversation on the ethics of the metaverse (Fielding, 2021), to consider how we can embed safety, privacy and inclusion at the core. It is fair to argue that these values closely align with LD, yet in the metaverse there is the potential for violence, harassment, isolation and bullying. How can we promote and enhance equality, diversity and inclusion in this space?&#x0D; We will invite participants (who will work in teams) to co-create a #Take5 blogpost with us from our mapping and debates.&#x0D; No technology is needed for the session, and no previous knowledge of the metaverse &#x0D; Our manifesto&#x0D; What does Learning Development do? &#x0D; &#x0D; It contextualises, embeds and maps knowledge, and contributes to learning gain &#x0D; It teaches how to learn and scaffolds learning &#x0D; It widens opportunity, not participation; it can trouble what we mean by participation &#x0D; It infiltrates throughout the university and operates in a 3rd space, connecting and &#x0D; collaborating with the wider community &#x0D; It works with the hidden curriculum &#x0D; It legitimises the different forms of knowledge our students have &#x0D; It levels the playing field and widens the academy &#x0D;
Improving Students’ Reading Comprehension Improving the Students’ Writing Recount Text Ability by Using Think Talk Write and Self-Regulated Strategy Development

Based on research conducted at SMA Negeri 2 Alasa, researchers found that there were several problems in learning to write English faced by students at SMA Negeri 2 Alasa. Students often have difficulty composing a paragraph by writing down their own ideas. Therefore, researchers conducted research entitled "Improving the students' writing recount text ability by using Think Talk Write and Self-Regulated Strategy Development at the eleventh grade of SMA Negeri 2 Alasa North Nias". This research aims to observe the improvement of students' writing skills using Think Talk Write and Self-Regulated Strategy Development, whether there was an improvement in learning outcomes and what strategies were more effectively used between the two strategies. In this research, researcher used classroom action research which is applies treatments to improve learning outcomes that are better than before. From this research, the average pre-test score for class XI-IPS was 52.14% and class XI-IPA was 42.86%. After being given Think Talk Write strategy treatment in class XI-IPS, the average post-test score was 72.35% and in class XI-IPA using Self-Regulated Strategy Development with an average score of 73.57%. From these score, it can be seen that there has been a significant improvement in students' writing abilities, namely 20.21% in class XI-IPS and class XI-IPA 30.71%. Thus, it is concluded that using Think Talk Write and Self-Regulated Strategy Development can improve students' ability in writing recount texts and in this research Self-Regulated Strategy Development is more effective than Think Talk Write strategy.
Developing Application-Based Learning Media for Beginner Students

Abstract&#x0D; The aim of this research was to develop an application-based learning media for Elementary School students to help them in learning English. Technology and information nowadays develop rapidly where the role of English is very crucial as a global language as well as the language of technology. This makes English one of elements to success in today's globalized world. English in Indonesia is still considered as a foreign language. In order to make English to be mastered as a communication tool, it is advisable to introduce English from an early age, especially at the Elementary School level. One way that can increase elementary school students' interest in getting to master English well is by developing various medium, especially application-based learning media that can be used on smartphones. Therefore, researchers developed Application-based Media to improve the English skills of IT Darussalam Muara Beliti Elementary School students. The results of this research were expected that the application-based learning media on smartphones to be used by teachers and elementary school students in the learning and teaching process.&#x0D; Keywords : developing, application-based learning, media
Design of Augmented Reality (AR) Learning Media in Ecosystem Meteri in Elementary School Inclusion Classroom

One of the multimedia technologies that is increasingly popular today is Augmented Reality (AR) technology. The current phenomenon is that inclusive schools still do not have learning media that accommodate children with special needs to be able to develop in accordance with the planned learning objectives. This research tries to develop a media that involves verbal elements represented by reading texts and nonverbal media in the form of three-dimensional projections simultaneously by utilizing AR technology in inclusive classes. The stages of the research conducted refer to the Bord and Gall development model. Development research is divided into several levels of research, while the level of this research is at level 1. Where at level 1 the research stages of the research are carried out to carry out development without testing the product. This research produces Augmented reality-based learning media design in ecosystem material on the food chain sub-chain in grade V elementary school. Learning media design is carried out based on the stages of exploring potential problems of making deian based on the results of the literature review and the validation process by experts. The conclusion is that augmented relaity learning media can visualize abstract concepts for understanding and the structure of an object model allows Augmented relaity as a more effective media in accordance with the objectives of the learning media