Reviewerly/exHarmony huggingface dataset:
Trello: a management strategy in project-based learning

The research will be carried out at the Simón Rodríguez Educational Unit; the purpose is to propose a proposal through the application of project-based learning (PBL). The target population of the project is high school students. The objective of applying the PBL is to achieve the active participation of teachers and students, through group work and interaction, to promote inclusion and self-learning. The conclusion reached is that the PBL or ABPy is a methodology that favors self-learning and inclusion, while requiring the effort to investigate a particular topic, promoting the link between peers, where the technical manager plays the role of coach and guide, both for teachers and students; Hence, this proposal applied in the realization of projects, promotes the development of self-esteem, motivation, while awakening interest in research, has an impact on the substantial improvement of learning, which has an impact on improving the results of their learning, since students manage to realize significant learning experiences.
Machine learning based cryptanalysis techniques: perspectives, challenges and future directions

The field of cryptanalysis has lately witnessed considerable advancement due to the need for artificial intelligence technologies to simplify the complicated task of vulnerability assessments for cryptographic algorithms. The use of well-known tools such as machine learning and deep learning has piqued the interest of researchers and experts in the field because it has supported research work in discovering great knowledge on the strong and weak points of cryptographic techniques while ushering in the era of automated and AI-driven cryptanalysis.Despite the positive solutions obtained through using DL in the realm of cryptanalysis, it is not without drawbacks. This paper emphasizes the issues encountered when using ML and DL in cryptanalysis as well as new paths of DL with the advent of the quantum neural network approach, which can provide better answers and hence the relevant state of the art.
A survey of parallel clustering algorithms based on vertical scaling platforms for big data

Clustering, or cluster analysis, is an important unsupervised task in machine learning that determines how the observed data naturally clusters. Many efficient traditional clustering methods based on different behaviours, such as partitioning’ hierarchical, grid, model, and density based, have been proposed in recent decades. However, clustering itself is considered an NP-hard problem, and it becomes more challenging when the clustered data is large. The classical clustering techniques cannot handle big data problems due to their large volume, fast generation, significant heterogeneity and complexity. Therefore, more effective, flexible, and efficient clustering approaches are required. Recently, the parallel and distributed computing concepts gives birth to the parallel clustering algorithms. Nowadays, the researches focus on scalable clustering methods based on different acceleration platforms to deal with big data problems. The acceleration platforms can be classified into horizontal and vertical-scaling platforms. In this paper we present a recent overview of the latest parallel and distributed clustering algorithms based on vertical scaling platforms. Otherwise, the paper gives a discussion that will be useful for researchers to propose more effective and efficient algorithms for Big Data clustering.
Fuzzy Cognitive Maps for Intelligent Agent's Artificial Situational Awareness in Collaborative Driving Context

Automation such as the autopilot car technology is considered to be a promising solution to reduce the number of accidents on the road. However, it is believed that to increase safety for such technologies, it is necessary for an intelligent agent (IA) and the human driver to share their situational awareness so that the same conclusions regarding driving situations can be reached. Hence, developing a reasoning engine to generate goal-driven explanations regarding IA's situational awareness is highly required. To address this challenge, this paper proposes a fuzzy-cognitive-map-based reasoning engine to disclose inferred situations behind IA's executed action. Carla simulator was used to conduct an experimental test in a collaborative driving context. As the results, goal-driven explanations can be generated with a better performance than the baseline method. This work is important to reduce road incidents involving autonomous cars.
A novel adaptive δ‐generalised labelled multi‐Bernoulli filter for multi‐target tracking with heavy‐tailed noise

Abstract Conventional δ‐generalised labelled multi‐Bernoulli filter (δ‐GLMB) cannot deal with the problem of the heavy‐tailed process noise and measurement noise. In order to solve this problem, an adaptive δ‐GLMB approach based on minimising Kullback‐Leibler Divergence (KLD) is proposed in this study. The inverse wishart and Student‐ t mixture is used to approximate the joint posterior distribution of process noise covariance and measurement noise covariance together with multi‐target state, and the multi‐target state and noise parameters are jointly estimated by minimising the KLD. The simulation results show that the proposed approach is robust to multi‐target tracking for condition with heavy‐tailed process noise covariance and measurement noise covariance. Accurate target number and target state estimation are obtained by effectively estimating the process noise covariance and measurement noise covariance.
A literature review of land title with the aim of maximising the benefits of Blockchain technology in the management of Land Title in Nigeria

Blockchain is a trending technology that has paved way to solving various challenges in different sectors. It has received much interest from researchers over the years, as it is known to be a transparent, secured, no third party and tamper-proof public records repository system for documents, contracts, properties and assets. Land title has been a major challenge in Nigeria due to the fact that existing system, digital archives, used to manage the sector is not effective, thus brought about issues such as, double spending, possibility of tampering, third party interference and non-transparency of operations. Hence the need for blockchain technology. Blockchain has characteristics, such as decentralization, security, immutability, smart contract, it is therefore expected to improve land title management. This paper applies a content-analysis based literature review blockchain technology and existing method of land title in Nigeria. This paper propose five benefits. Blockchain can help to improve proper land record keeping, information transparency, accountability, anonymity and avoidance of double spending.
METHODOLOGY FOR THE DEVELOPMENT OF DIGITAL TWINS FOR SCIENTIFIC AND PRODUCTION PROCESSES IN ZOOTECHNICS AND ZOOHYGIENE

It has been established that digital twins are based on identified dependencies and mathematically formalized regularities, and use boundary conditions, curvilinear and nonlinear functions of parameter relationships in the calculation algorithm design. The&#x0D; main goal of developing digital twins is to fully reproduce a particular process through numerical values that can be easily verified (confirmed or disproved) by independent researchers based on their professional knowledge. In this case, researchers act as software testers in the IT field, that is, professionals involved in testing a component or system.
Curriculum and development trends

Curriculum is an important factor when researching an educational model, a training institution or any content related to education. Understanding the curriculum comprehensively and deeply as well as grasping curriculum development trends will help educators have clearer orientations. The article focuses on clarifying the concept of curriculum, approaches to curriculum, classification of curriculum, phases of curriculum development, general elements of the curriculum framework and current trends in curriculum development.
Introduction to Cascading Style Sheets

When HTML was first developed, it was entirely used for communicating information. HTML was originally built as an easy way to browse and view documents. Therefore, HTML documents were never pretty but were very functional. In addition, there was a direct match between the tag used and the purpose that the tagged text served in the document. For instance, <h1> tags meant that the given text was a main heading. If you were looking at the HTML code itself, you would know precisely what it meant if you found text with that tag. In fact, if you knew the tags, you could almost just as easily read the document source with the tags as you could read it rendered in the browser.
Data as a Resource for Designing Digitally Enhanced Consumer Packaged Goods

The incorporation of digital functionalities into consumer packaged goods (CPG) has the potential to improve our lives by supporting us in our daily practises. However, despite the increasing availability of data about their use, research is needed to explore how these data can be harnessed to create such digital enhancements. This paper explores how consumers can utilise data about interactions with CPGs to conceptualise their enhanced versions. We devised a data-inspired ideation approach, using data visualisations and design cards to facilitate the conceptualisation of enhanced CPGs. Analysing the role of data as expressed through participants’ comments and designs, we found that data served as a basis for the creation of unique concepts imbued with greater consideration for the experiences of others and attention to their own interests. Our study shows the value of empowering consumers through data to broaden and inform their contributions towards the creation of smart products.
REVIEW ON CHATBOT-MAYA

Our project, developing a Chatbot focuses on developing a model which will be able to generate responses automatically to the questions asked using a number of different machine learning techniques.We are perusing our objective in three phases: Design, Analysis and Implementation.For Analysis, we have taken a large segment of data from social networking sites to generate the outcomes for commonly discussed problems.The parameters are prioritized on the basis of interpretation of this data.
Semi-Supervised Sentiment Classification and Emotion Distribution Learning Across Domains

In this study, sentiment classification and emotion distribution learning across domains are both formulated as a semi-supervised domain adaptation problem, which utilizes a small amount of labeled documents in the target domain for model training. By introducing a shared matrix that captures the stable association between document clusters and word clusters, non-negative matrix tri-factorization (NMTF) is robust to the labeled target domain data and has shown remarkable performance in cross-domain text classification. However, the existing NMTF-based models ignore the incompatible relationship of sentiment polarities and the relatedness among emotions. Besides, their applications on large-scale datasets are limited by the high computation complexity. To address these issues, we propose a semi-supervised NMTF framework for sentiment classification and emotion distribution learning across domains. Based on a many-to-many mapping between document clusters and sentiment polarities (or emotions), we first incorporate the prior information of label dependency to improve the model performance. Then, we develop a parallel algorithm based on message passing interface (MPI) to further enhance the model scalability. Extensive experiments on real-world datasets validate the effectiveness of our method.
Text encryption for lower text size: Design and implementation

For enterprises of all sizes and types in the IT sector, data security is essential. Data security is the process of giving databases and websites secure data privacy protections and preventing unauthorized data access. By encrypting digital data, software/hardware, and hard drives, unauthorized individuals or hackers can only access unreadable material, which is why encryption is a crucial data protection strategy. Data is produced by computing operations, which are then used to encrypt and send the data into the network. For low bandwidth channels, an encryption strategy has been put out in this work. The proposed method uses the properties of homomorphic transform to make it more unique and suitable for text encryption. Conversion of plain text to cipher text is done by using simple union and symmetric difference operators. Which makes it more reliable in terms of execution time.
Dust image restoration based on channel difference prior and depth residual attention network

Images captured in dust weather have severe yellow and local red, and traditional color correction methods have low robustness. Thus, a dust image restoration method based on channel difference prior (CDP) and a depth residual attention network is proposed in this paper. First, CDP based on statistical observations is defined and used to acquire a dust image dataset. Then, a deep residual network that combines channel-spatial attention is introduced, and channel attention and spatial attention are used to describe important information for every residual block feature. Finally, channel fusion and nonlinear outputs are used to generate clear images. Rich experiments have confirmed the effectiveness and rationality of the proposed method.
The relationship between knowledge exchange and teachers' use of mobile technologies in the mathematics classroom

This study investigates the factors impacting the way secondary mathematics teachers used tablets in their teaching, and the role knowledge exchange played in their developing use. The study introduced a structured method of knowledge exchange, which used guiding questions to facilitate teachers' discussions in order to develop teachers' use of tablets in teaching mathematics. We found seven categories of factors that impact teachers' use of digital tools and that a structured knowledge exchange among teachers can help teachers address underlying issues related to these factors. Implications of teacher professional development and future research are discussed in light of these findings.
Evaluation Test Generation Model Using Two Intervals of Difficulty and Keywords

The Covid-19 pandemic has brought to light some of the shortcomings in the educational systems. One of these is related to the evaluation methods that are outdated and lack efficiency. The solution, proposed in this paper, uses two intervals of difficulty and a list of keywords as part of the input for a genetic algorithm. Each question in the returned list respects certain rules regarding its difficulty and it belongs to a certain selected topic, described by one or more keywords. Also, as a genetic algorithm requires its setting parameters, this paper analyses the impact of each of them on the final result.
Artificial intelligence in literacy

Artificial intelligence (AI) and machine learning (ML) are beginning to impact reading and reading instruction, and will have a greater impact in the near future. Given this important, development, we review the tradition, present practice, and future of reading and reading instruction. Components of reading skill are discussed within the context of how they can be studied and measured within the context of AI and ML. We analyze prototypical examples of AI instruction, assessment, and practice. We give examples of AI Text Analysis in the context of mining databases to optimize reading complexity. We describe the potential value of AI and mentoring by social robots. Given the important similarities and differences between spoken and written language, we close with speculations about the possibility of more literacy but with less reading.
Assessing learning processes

Assessments of learning processes have relied on surveys and think aloud protocols. Because learners forget, recall with bias and lack expertise for describing learning events, these assessments have limited validity. Multimodal measures of physiological states are proxies for learning processes with similar shortcomings. Precise and reliable traces of learning processes can be engineered in software guided by the COPES typology of learning tasks (conditions, operations, products, evaluations, standards) and the SMART typology of cognitive operations (searching, monitoring, assembling, rehearsing, translating). Process mining and graph theory analyses of trace data generate reliable and valid ipsative and criterion-referenced assessments of learning processes.
Procedural Generation of Underground Environments for Gazebo

One of the challenges in the field of autonomous robotics is the thorough testing of the navigation capabilities of the different methods in a wide variety of situations. Towards this end, simulation plays an ever increasingly important role as the improvements in the technology keep closing the gap between reality and simulation. This work presents a scalable, efficient and easy-to-use framework for generating random underground environments. The system is based on the procedural placement of discrete modules in such a way that the end result are sprawling and complex networks of tunnels that can be used for testing robotic applications for underground environments.
Researches Advanced in the Development and Application of Transformers

The basic task of feature learning is to use algorithms to allow machines to automatically learn useful data and its features during the model building process. The quality of the learned features will greatly affect the results of downstream tasks. Early feature learning methods relied on handcrafted features. Thanks to the development of deep learning, feature learning methods based on convolutional neural networks have greatly improved the quality of features. However, with the increasing scale of training data and the increasing complexity of modeling tasks, deep neural network Transformer based on self-attention mechanism and parallel data processing has gradually become a new research hotspot. Transformer can adaptively and selectively select contextual information and key semantic information in a scene by covering attention networks and fully connected layers and has become an important research area for computer vision and natural language processing tasks. This paper reviews the basic principles and development of Transformer, focuses on its application in CV and NLP, and analyzes effective Transformer-based models. Finally, the challenges faced, and future trends of transformer models are summarized.
Common item formats and evaluation practices

This article focuses on one facet of overall test development: It presents a general overview of traditional item types (i.e., test questions) that are typically used in educational measurement. It describes the advantages and disadvantages each item type contributes to the test design and, therefore, to test use, as well as to test validity, reliability, and fairness. The article considers selected-response versus constructed-response item types and then gives brief discussions of writing and reviewing both item types, as well as describing best practices in scoring the latter. The article concludes with brief descriptions of new item types, including technology-enhanced items and Likert-scale items for noncognitive assessments.
Mixed methods sampling and data collection

Due to the myriad of sampling schemes and data collection techniques available, choosing among these can present challenges for researchers when conducting a mixed methods research study. Thus, the aim of this chapter is to delineate methods of sampling and data collection for mixed methods research studies. Topics covered in this chapter for mixed methods research studies include data collection for mixed methods studies and sampling schemes. The four major types of sampling are discussed, including convenience, probability, purposive, and mixed methods sampling. In addition, multiple data collection techniques are explored and are outlined by how many samples are included and the number of time sampling occurs: data collection with one sample at one time point, data collection with one sample at different time points, and data collection with different samples at different time points. Using appropriate sampling techniques in mixed methods studies has multiple implications for researchers in education and beyond.
Youth as educators

In this entry, the authors offer a brief introduction to competing and similar criteria scholars have presented as characteristics of informal learning. Then, the authors trace some of the scholarly concerns expressed over the past century related to advances in technologies and communications (such as the radio and motion pictures) and the potential for negative or harmful informal learning among youth. Next, using online archives and relevant literature, the authors illustrate that despite the concerns that adults present, young people are often innovators and educators who utilize available technologies and communication mediums to advance social justice causes and engage in democratic processes.
Meta-analysis

Meta-analysis is the quantitative synthesis of the results of studies on a common topic or question. Its key goals are to assess the magnitudes of effects from the studies, and to quantify and explore variation in those effects. This chapter describes the set of practical and statistical procedures required to conduct a high-quality meta-analysis. The process begins with the formulation of a problem. This is followed by the data-collection step, during which the meta-analyst conducts a reproducible, exhaustive, and unbiased search for relevant studies that meet specific inclusion criteria. In the data-evaluation stage the meta-analyst extracts effect indices to represent study results, along with features of the participants, treatments (if any), methods, and measures used, and indicators of study quality. During data analysis, study features serve as potential explanations of between-studies differences. Finally, the review process and meta-analytic results are fully described and interpreted in the meta-analysis report. We illustrate these steps using two recent meta-analyses of studies of game-based instruction for second-language learning. Analyses based on data from Thompson and von Gillern (2020) illustrate fixed- and random-effects models for estimating overall effect magnitudes and their variance across studies, and for exploring predictors of between-studies differences. We also demonstrate the use of Bayesian meta-analysis to obtain information on the potential distributions of population parameters.
Automated scoring of constructed-response items in educational assessment

This entry focuses on automated scoring of questions calling for answers that cannot be graded using exact-matching techniques and that are used operationally in assessment programs and learning products. The entry describes the benefits of automated scoring, the contexts in which it has been used, and approaches to validation. Operational uses are briefly discussed for the automated scoring of essay writing, spoken responses, short text responses, STEM responses, and response processes. A discussion of emerging trends closes the entry.