Reviewerly/exHarmony huggingface dataset:
Geological Mineral Energy and Classification Based on Machine Learning

In order to mine geological mineral energy and study on geological mineral energy classification, a method based on a wireless sensor was proposed. Of logistic regression, artificial neural networks, random forests, and main wireless sensor algorithms of support vector machine (SVM) with the model in the application of the energy mineral resource prediction practice effects are reviewed and discuss the practical application in the process of sample selection, the wrong points existing in the cost, the uncertainty evaluation, and performance evaluation of the model using wireless sensor algorithm, random forest of the probability distribution of mineralization in the study area is calculated, and five prospecting potential areas are delineated. The results show that the ratio of ore-bearing unit and non-ore-bearing unit is 1 : 1, and the best random forest training model is obtained. 70% of the training sample set was randomly selected as the training set, and the remaining 30% was used as the test set to construct the random forest model. The training accuracy of the model is 96.7%, and the testing accuracy is 96.5%. Both model training accuracy and model testing accuracy are very high, which proves the accuracy of RF model construction and achieves satisfactory results. In this study, a wireless sensor is successfully applied to 3D mineral energy prediction, which makes a positive exploration for mineral resource prediction and evaluation in the future. Finally, the prediction of mineral resource energy based on a wireless sensor is an important trend of future development.
Recognition of Eye-Written Characters Using Deep Neural Network

Eye writing is a human–computer interaction tool that translates eye movements into characters using automatic recognition by computers. Eye-written characters are similar in form to handwritten ones, but their shapes are often distorted because of the biosignal’s instability or user mistakes. Various conventional methods have been used to overcome these limitations and recognize eye-written characters accurately, but difficulties have been reported as regards decreasing the error rates. This paper proposes a method using a deep neural network with inception modules and an ensemble structure. Preprocessing procedures, which are often used in conventional methods, were minimized using the proposed method. The proposed method was validated in a writer-independent manner using an open dataset of characters eye-written by 18 writers. The method achieved a 97.78% accuracy, and the error rates were reduced by almost a half compared to those of conventional methods, which indicates that the proposed model successfully learned eye-written characters. Remarkably, the accuracy was achieved in a writer-independent manner, which suggests that a deep neural network model trained using the proposed method is would be stable even for new writers.
Intelligent Information Retrieval for Reducing Missed Cancer and Improving the Healthcare System

This study presents an intelligent information retrieval system that will effectively extract useful information from breast cancer datasets and utilized that information to build a classification model. The proposed model will reduce the missed cancer rate by providing a comprehensive decision support to the radiologist. The model is built on two datasets, Wisconsin Breast Cancer Dataset (WBCD) and 365 free text mammography reports from a hospital. Effective pre-processing techniques including filling missing values with regression, an effective Natural Language Processing (NLP) Parser is developed to handle free text mammography reports, balancing the dataset with Synthetic Minority Oversampling (SMOTE) was applied to prepare the dataset for learning. Most relevant features were selected with the help of filter method and tf-idf scores. K-NN and SGD classifiers are optimized with optimum value of k for K-NN and hyper tuning the SGD parameters with grid search technique.
The state of the art of agile kanban method: challenges and opportunities

In the recent years, the Agile Kanban has emerged as an appropriate method used for managing projects in numerous fields and various settings. Despite getting an increased popularity in the software organizations, the Agile Kanban method still has different challenges that need to be addressed. Therefore, this study aims to concisely explore the current state of the art and latest researches on the Agile Kanban method through conducting an extensive review of the literature. The results of this study carry strong implications and confirm the important need for conducting researches on the Agile Kanban method. It also provides the key challenges and opportunities that can be investigated in future studies. The cross analysis of the results leads to a better understanding of the Agile Kanban method and aids the research teams to address the Kanban limitations and increase its adoption in the software organizations.
A Real-time Posture Recognition System using YOLACT++ and ResNet18

This paper presents a deep learning based real-time two-stage gesture recognition system. In the first stage, the system deploys the YOLACT++ model to detect the human body mask. Then four ResNet18 models are used separately to predict the correctness of push-up, the number of push-ups, correctness of sitting posture, and the flexibility of the standing forward bend. The accuracy rates of these four ResNet18 models reached 97.9%, 97.5%, 95.1%, and 95.4%, respectively, using the test dataset collected from ten individuals.
Improved Certificate-Based Encryption Scheme in the Big Data: Combining AES and (ECDSA â€“ ECDH)

Big data usually running in large-scale and centralized key management systems. However, the centralized key management systems are increasing the problems such as single point of failure, exchanging a secret key over insecure channels, third-party query, and key escrow problem. To avoid these problems, we propose an improved certificate-based encryption scheme that ensures data confidentiality by combining symmetric and asymmetric cryptography schemes. The combination can be implemented by using the Advanced Encryption Standard (AES) and Elliptic Curve Diffie-Hellman (ECDH). The proposed scheme is an enhanced version of the Certificate-Based Encryption (CBE) scheme and preserves all its advantages. However, the key generation process in our scheme has been done without any intervention from the certificate issuer and avoiding the risk of compromised CA. The Elliptic Curve Digital Signature Algorithm (ECDSA) has been used with the ECDH to handle the authentication of the key exchange. The proposed scheme is demonstrated on a big dataset of social networks. The scheme is analyzed based on security criteria that have been compared with the previous schemes to evaluate its performance.
A Secure IoT Based Grid-Connected Inverter using RSA Algorithm

The model of the internet of things (IoT) based single-phase grid-connected inverter using RSA (Rivest-Shamir-Adleman) algorithm is presented here. The inverter is developed by H-bridge architecture and sinusoidal pulse width modulation technology (SPWM). The grid connectivity of this inverter is built by the phase control technique. The inverter has fewer harmonics and a simplistic structure than conventional available technologies. Because this IoT-enabled inverter is continually connected to the network, cybersecurity concerns and obstacles are developing. The paper explores how the RSA method could be used to assure the IoT-enabled inverter's connectivity and data security. The inverter simulation is performed in MATLAB / SIMULINK to validate the final output, and the RSA technique is performed in Python.
Institutional quality and economic growth: evidence from Sub-Saharan Africa countries

This paper employs two step systems GMM to analyse the effect of institutional quality on economic growth for Sub-Saharan African countries for the period from 2006 to 2018. The findings show that an improvement on institutional quality positively and significantly improve Sub-Saharan African countries output. The findings further provide evidence that the effect of institutional quality on output varies with regional location of SSA countries. In particular, institutional qualities are more effective in driving income growth in West African region than the other three regions of Eastern Africa and Central Africa. In addition, the findings indicate that the impact of institutional quality on output growth varies with income level of SSA countries. An improvement in intuitional quality is more likely to improve economic performance of low income SSA economies than the middle income SSA countries. SSA countries should strengthen independent institutional bodies that prosecute economic crimes. Also, African countries should support African agendas that are aligning with global development agenda. Sub-Saharan African countries should strengthen institutions that widen democratic space, civil liberty and the participation of citizen in the development agenda of a country.
Learning from Non-Assessed Resources: Deep Multi-Type Knowledge Tracing

The state of the art knowledge tracing approaches mostly model student knowledge using their performance in assessed learning resource types, such as quizzes, assignments, and exercises, and ignore the non-assessed learning resources. However, many student activities are non-assessed, such as watching video lectures, participating in a discussion forum, and reading a section of a textbook, all of which potentially contributing to the students' knowledge growth. In this paper, we propose the  first novel deep learning based knowledge tracing model (DMKT) that explicitly model student's knowledge transitions over both assessed and non-assessed learning activities. With DMKT we can discover the underlying latent concepts of each non-assessed and assessed learning material and better predict the student performance in future assessed learning resources. We compare our proposed method with various state of the art knowledge tracing methods on four real-world datasets and show its effectiveness in predicting student performance, representing student knowledge, and discovering the underlying domain model.
An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representation

Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data. However, NMF pays the same attention to all attributes of a data point, which inevitably leads to inaccurate representations. For example, in a human-face dataset, if an image contains a hat on a head, the hat should be removed or the importance of its corresponding attributes should be decreased during matrix factorization. This article proposes a new type of NMF called entropy weighted NMF (EWNMF), which uses an optimizable weight for each attribute of each data point to emphasize their importance. This process is achieved by adding an entropy regularizer to the cost function and then using the Lagrange multiplier method to solve the problem. Experimental results with several datasets demonstrate the feasibility and effectiveness of the proposed method. The code developed in this study is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Poisson-EM/Entropy-weighted-NMF</uri> .
Argumentation in classroom interaction. Teaching and learning Italian as a second language

Special issue.Gobber G., Cantarini S., Cigada S., Gatti M.C., Gilardoni S. (ed.), Proceedings of the IADA Workshop “Word meaning in argumentative dialogue”, Milan 2008, 15-17 May.Homage to Sorin Stati
Importance of Perception as a Parameter in University Rankings : A Critical Evaluation

Assigning ranks and grades (or stars) has been a common practice for ages to describe the characteristics of a product with the intention that it will significantly influence customers’ buying behavior. What started as a marketing tool to compare and show supremacy over competitors’ products, ranking and grading have now become institutionalized, involving many commercials at stake. Nowadays, ranking evaluation has altogether become separate diligence with stiff competition among different players. Such types of rankings and accreditations have also influenced the education sector. Though there are various international rankings (QS World University Rankings, Shanghai Ranking, Academic Ranking of World Universities, etc.) for the education sector, the Indian government also administrates its ranking system to bring in a sense of competition and performance reviews among academic institutions of India. With this thought, the National Institution Ranking Framework (NIRF), also popularly known as ‘India Rankings,’ was started in 2015. The NIRF rankings of higher education institutions (HEIs) offering engineering education were considered for the study. Perception as an evaluation indicator has always been debated for its validity in the finalization of such rankings. This paper attempted to scientifically study the influence that the parameter ‘perception’ contributes to the overall ranking of HEIs.
Computer-based assessment of reading

We have reached a watershed in the history of psychological and educational assessment. It is hard to see how conventional assessment methods could be further improved – in effect, they have reached the zenith of their development. In a relatively short time, however, they are likely to be usurped by computerised assessment as swiftly as the electronic calculator banished tables of logarithms from the classroom (and for very similar reasons). The rationale for this view derives from the particular advantages that computer-based testing has over conventional methods. The purpose of this chapter, therefore, is first to outline the nature and advantages of computer-based assessment techniques and to explain why it is at this particular moment in history that such an advancement should take place. I will then go on to consider some of the critical problems concerning how computer-based approaches to assessment can be shown to have equivalence with conventional methods of assessment. Current research on computer-based assessment of reading and dyslexia will be critically reviewed. Finally, I will address the integration of screening and computer-based assessment approaches and evaluate the prospects for more effective and practical screening procedures that can be computer-based.
Students’ multimodal knowledge sharing in school: Spatial repertoires and semiotic assemblages

In a world 'flooded' with data, students in school need adequate tools as Visual Analytics (VA), that easily process mass data, give support in drawing advanced conclusions and help to make informed predictions in relation to societal circumstances. Methods for how the students' insights may be reformulated and presented in 'appropriate' modes are required as well. Therefore, the aim in this study is to analyse elementary school students' practices of communicating visual discoveries, their insights, as the final stage in the knowledge-building process with an VA-application for interactive data visualization. A design-based intervention study is conducted in one social science classroom to explore modes for students presentation of insights, constructed from the interactive data visualizations. Video captures are used to document 30 students' multifaceted presentations. The analyses are based on concepts from Pennycook (2018) and Deleuze and Guattari (1987). To account for how different modes interact, when students present their findings, one significant empirical sequence is described in detail. The emerging communicative dimensions (visual-, bodily- and verbal-) are embedded within broad spatial repertoires distributing flexible semiotic assemblages. These assemblages provide an incentive for the possibilities of teachers' assessments of their students' knowledge outcomes.
Novel Computer Aided Diagnostic System Using Synergic Deep Learning Technique for Early Detection of Pancreatic Cancer

Pancreatic cancer (PC) in the more extensive sense alludes to in excess of 277 distinct kinds of cancer sickness. Researchers have recognized distinctive phase of pancreatic cancers, showing that few quality transformations are engaged with cancer pathogenesis. These quality transformations lead to unusual cell multiplication. Therefore, in this study we propose a Computer Aided Diagnosis (CAD) system using Synergic Inception ResNet-V2, Deep convoluted neural network architecture for the identification of PC cases from publically Usable CT images that could extract PC graphical functionality to include clinical diagnosis before the pathogenic examination, saving valuable time for disease prevention. Simulation results using MATLAB is shown to illustrate that quite promising results have been obtained in terms of accuracy in detecting patients infected with BC. Accuracy of 99.23 per cent is reached using the proposed deep learning method, which is better than all other state-of-the-art approaches available in the literature. The calculation time was found to be less than the other current 22 second process. The proximity of the suggested approach to the True Positive values in the ROC curve suggests a result that is greater than the other methods. The comparative study with Inception ResNet-V2 is based on separate test and training data at a rate of 90 percent-10 percent, 80 percent-20 percent and 70 percent-30% respectively, which shows the robustness of the proposed research work. Experimental findings show the proposed reliability of the device relative to other detection approaches. The proposed CAD device is fully automated and has thus proved to be a promising additional diagnostic tool for frontline clinical physicians.
6-DOF Robot Arm Visual Servoing with Canny Edge-Based Object Detection

Recently, the robot industry has developed and is doing human work instead. Among them, Visual Servoing research is actively underway to control robots using vision sensors. In this study, using OpenCV, a machine vision open-source library, to detect the contours of the object and the central point of the object. Machine Vision algorithms are performed using features such as Grayscale, Gaussian filter, Canny Edge, Contouring. The proposed algorithm recognizes only objects on the workstation without detecting other things. Also, transform the coordinate system of the vision sensor into the robot arm's coordinate system so that the robot arm can move correctly around the center of the object. For this purpose, camera calibration was performed, and a transformation matrix about vision to robot was obtained. Furthermore, we aim to control the 6-DOF robot arm through experiments to move the robot arm's end-effector to the center point of the object detected by the proposed algorithm.
Phishing Kits

Although there are various methods for launching a phishing website, attackers can use premade toolkits to create phishing websites. According to WhatIs TechTarget, phishing kits are a set of software tools that make it easier for someone with little or no technical knowledge to launch a phishing exploit. Phishing kits typically include website building software with basic tools, a graphical user interface (GUI), and more. This type of kit usually includes email templates, images, and example scripts for creating convincing impersonations of authentic websites and correspondences.
Teachers and students in modern professional education: rethinking roles

This study examined the problem of the relationship between teachers and students, which has become a key to success in the educational process. Shown is the importance of using the results of a survey of students about their satisfaction with the organization and implementation of the educational process, the level of professional and pedagogical skills and personal qualities of teachers. The transition to a classroom interactive learning system is recommended, which facilitates the use of innovative educational technologies such as problem-based learning, modular learning, blended learning. Recommendations are made for some systemic changes in vocational education to produce people who are well prepared for modern life.
Predicting help desk ticket reassignments with graph convolutional networks

Efficient triaging of incident tickets is a critical task in Information Technology Service Management. Introducing interventional measures on tickets that are difficult to resolve can help improve the triaging of complex tickets. This work reports a method to predict the resolution complexity of a reported incident. The number of times a ticket is reassigned is a measure of difficulty in resolving the incident. Ticket resolution is associated with a variable workflow. A graph representation of ticket resolution offers advantages from the standpoint of running ad hoc queries. Predicting ticket reassignments requires the application of machine learning to this graph. A Relational Graph Convolutional Network is used for this purpose. The developed model provides benefits beyond predicting ticket reassignments accurately. It provides embeddings that can be used to derive insights about the operation of the help desk organization and the users of the help desk.
Model Explainability for Rule-Based Expert Systems

Rule-based systems are relatively easier to explain to end users because rules can be expressed as if/else conditions. Indeed, at times there are multiple if/else conditions. Expert systems are deterministic in nature and very accurate, because they are driven by well-established rules and conditions. The need for explainability arises when multiple rules either conflict or are combined in such a way that it is difficult to interpret. In the field of artificial intelligence, an expert system is a program designed to simulate a real decision making process. Expert systems are designed to solve complex problems by simple reasoning and are represented as if/else/then conditions. Expert systems are designed to act like an inference engine. The role of an inference engine is to make decisions. Sometimes expert systems are simple, like a decision tree, and sometimes expert systems are very complex. Complex expert systems require a knowledge base. Sometimes these knowledge bases are an ontology, which is a form of a body of knowledge, through which a particular rule can be framed and inferencing can be done.
Designing an effective graphic abstract

The application of visual and design principles to create graphic abstracts clarifies their visual literacy and message.
Analyzing Unconstrained Reading Patterns of Digital Documents Using Eye Tracking

Researchers read scientific literature to keep current in the field and find state-of-the-art solutions to various scientific problems. Prior work suggests that reading patterns may vary with the researcher's domain expertise and on the content of the digital document. In this work, we present a pilot study of eye-tracking measures during a reading task with the options for zooming and panning of the reading material. The main goal is to analyze unconstrained reading patterns of digital documents using eye movement fixations and dwell time on various sections of a digital document. Our results indicate that participants mostly focused on methodology and results sections, which is consistent with the prior work with constrained reading patterns.