Reviewerly/exHarmony huggingface dataset:
Mechanical Simulation on the Vertical Migration of Ore-forming Elements in Soil Cover

For deep-penetrating geochemistry, there is a frontier issue as to how to explore ore-bearing information deep in covered areas, while it is, too, important to investigate the geochemical migration of ore-forming elements for the development of exploration geochemistry. In this study, a 380-day migration column experiment was conducted with surface soil and pure Cu powder collected from the Yujiashan study area in Wuhan City, Hubei Province taken as the object of research to simulate the migration process of ore-forming elements under natural conditions. The results showed obvious vertical migration of the ore-forming elements and conversion of mobile metal forms in the process; further, according to 6 groups of simulation experiments that took 45 days to conduct on migration column samples and the surface oil and Cu powder collected from the Pulang mining area in Yunnan Province, the vertical migration of the ore-forming element was still visible, and the exogenous substance (pure Cu powder) could enhance the anomaly without changing the basic migration mechanism. This indicates that Cu might migrate in a stable manner in soil cover in the Pulang mining area, and its migration was mostly driven by the transformation of Fe-Mn oxide bound state and organic bound state; finally, a multifractal analysis was conducted, revealing that the ore-forming elements presented a nonlinear and complex structure during their upward migration, which caused anomalies in the covered area, and multiple parameters exerted a certain indicative effect on different source intensities and properties of soil. In this study, laboratory simulations were performed to summarize the active-state evolution pattern and migration mechanism of ore-forming elements with a view to providing technical support and theoretical guidance for geochemical prospecting in the covered region.
Development and Prediction of Kuala Terengganu Driving Cycle via Long Short-Term Memory Recurrent Neural Network

Driving cycle is as representation of traffic behaviour in an area or city.It plays a fundamental role in the design of vehicles and to test the performance of the vehicles.This paper studies a driving cycle development method based on k-means clustering and driving cycle prediction based on Long Short-Term Memory (LSTM) by Recurrent Neural Network (RNN).The objectives of this paper are to develop a Kuala Terengganu Driving Cycle (KTDC) by using k-means clustering, to develop a prediction of future KTDC, and lastly to analyse the energy consumption and emissions of KTDC.Firstly, the driving data is collected in five different routes in Kuala Terengganu city at go-to-work times.Then the data is divided into micro-trips and the driving features are extracted.The features are used to develop a driving cycle using k-means clustering approach.The prediction is developed after the training of neural networks by using LSTM network approach.Finally, the energy consumption and emissions of KTDC is analysed by using AUTONOMIE software.
Using Gradient Descent to An Optimization Algorithm that uses the Optimal Value of Parameters (Coefficients) for a Differentiable Function

Deep neural networks (DNN) are commonly employed. Deep networks' many parameters require extensive training. Complex optimizers with multiple hyper parameters speed up network training and increase generalisation. Complex optimizer hyper parameter tuning is generally trial-and-error. In this study, we visually assess the distinct contributions of training samples to a parameter update. Adaptive stochastic gradient descent is a variation of batch stochastic gradient descent for neural networks using ReLU in hidden layers (aSGD). It involves the mean effective gradient as the genuine slope for boundary changes, in contrast to earlier procedures. Experiments on MNIST show that aSGD speeds up DNN optimization and improves accuracy without added hyper parameters. Experiments on synthetic datasets demonstrate it can locate redundant nodes, which helps model compression.
role of Information System to measure the Cost and Performance Aware Scheduling Technique for Cloud Computing Environment

The lot of interest has been put forth by researchers to improve workload scheduling in the cloud platform. However, the execution of scientific workflow on a cloud platform is time-consuming and expensive. Much research has been emphasised, as users are charged based on the usage hour, minimising processing time to reduce cost. However, the processing cost can be reduced by minimising energy consumption, especially when resources are heterogeneous; Minimal work has been done considering optimising cost with energy and processing time parameters to meet task Quality of Service (QoS) requirements. This paper presents cost and performance-aware workload scheduling (CPA-WS) techniques under a heterogeneous cloud platform. This paper presents a cost optimisation model through the minimisation of processing time and energy dissipation for the execution of the task. Experiments are conducted using two widely used workflows such as Inspiral and CyberShake. The outcome shows the CPA-WS significantly reduces energy, time, and cost compared to the standard workload scheduling model.
Relationship Between Artificial Intelligence and Machine Learning in Network Monitoring

Artificial Intelligence and Machine Learning can have a close relationship. AI is a discipline that focuses on developing systems that can perform tasks that require human intelligence, where Machine Learning is one of the main branches of AI that deals with the development of algorithms and statistical models to analyze network data in real-time, identify patterns and behaviors and take appropriate actions, thereby strengthening the detection of security threats in the network through network traffic data analysis, ML algorithms can learn from normal traffic patterns and identify suspicious behavior in analyzing traffic data, ML algorithms can learn normal traffic patterns from users, devices, or applications. The anomaly detection method uses a different approach by training the model to recognize the usual patterns in the data and identifying data that differs from those patterns as anomalies. The purpose of this research is to improve security threat detection, analyze network performance efficiently, identify unusual behavior patterns and improve the effectiveness and efficiency of network monitoring with the results obtained are increased detection of security threats, more accurate identification of anomalies, recognition of new attack patterns, real-time network performance monitoring and reduction in the number of false positives.
Optimization of Deep Convolutional Neural Network with the Integrated Batch Normalization and Global pooling

Deep convolutional neural networks (DCNN) have made significant progress in a wide range of applications in recent years, which include image identification, audio recognition, and translation of machine information. These tasks assist machine intelligence in a variety of ways. However, because of the large number of parameters, float manipulations and conversion of machine terminal remains difficult. To handle this issue, optimization of convolution in the DCNN is initiated that adjusts the characteristics of the neural network, and the loss of information is minimized with enriched performance. Minimization of convolution function addresses the optimization issues. Initially, batch normalization is completed, and instead of lowering neighborhood values, a full feature map is minimized to a single value using the global pooling approach. Traditional convolution is split into depth and pointwise to decrease the model size and calculations. The optimized convolution-based DCNN's performance is evaluated with the assistance of accuracy and occurrence of error. The optimized DCNN is compared with the existing state-of-the-art techniques, and the optimized DCNN outperforms the existing technique.
Multi-Magnification Attention Convolutional Neural Networks [AI-eXplained]

To apply convolutional neural networks (CNNs) on high-resolution images, a common approach is to split the input image into smaller patches. However, the field-of-view is restricted by the input size. To overcome the problem, a multi-magnification attention convolutional neural network (MMA-CNN) is proposed to analyze images based on both local and global features. Our approach focuses on identifying the importance of individual features at each magnification level and is applied to pathology whole slide images (WSIs) segmentation to show its effectiveness. Several interactive figures are also developed to enhance the reader’s understanding of our research.
Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython

This document provides a comprehensive guide to hyperparameter tuning using spotPython for scikit-learn, PyTorch, and river. The first part introduces spotPython's surrogate model-based optimization process, while the second part focuses on hyperparameter tuning. Several case studies are presented, including hyperparameter tuning for sklearn models such as Support Vector Classification, Random Forests, Gradient Boosting (XGB), and K-nearest neighbors (KNN), as well as a Hoeffding Adaptive Tree Regressor from river. The integration of spotPython into the PyTorch and PyTorch Lightning training workflow is also discussed. With a hands-on approach and step-by-step explanations, this cookbook serves as a practical starting point for anyone interested in hyperparameter tuning with Python. Highlights include the interplay between Tensorboard, PyTorch Lightning, spotPython, and river. This publication is under development, with updates available on the corresponding webpage.
Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes

Purpose: The purpose of this study is to analyze text documents from Twitter about public policies in handling COVID-19 that are currently or have been determined. The text documents are classified into positive and negative sentiments by using Multinomial Naive Bayes.Methods: In this research, CRISP-DM is used as a method for conducting sentiment analysis, starting from the business understanding process, data understanding, data preparation, modeling, and evaluation. Multinomial Naive Bayes has been applied in building classification based on text documents. The results of this study made a model that can be used in classifying texts with maximum accuracy.Result: The results of this research are focused on the model or pattern generated by the Multinomial Naive Bayes Algorithm. The classification results of social media users' tweets against the new normal policy obtained good results with an accuracy value of 90.25%. After classifying the tweets of social media users regarding the new normal policy, the results show that more than 70% agreed and supported the new normal policy.Novelty: This study resulted in how classification can be done with Multinomial Naive Bayes and this algorithm can work well in recognizing text sentiments that generate positive or negative opinions regarding public policies handling COVID-19. So, the research provided conclusions about the views of people around the world on new normal public policy.
Improved Action Unit Detection Based on a Hybrid Model

Facial action detection and facial expression recognition are two closely intertwined problems in behavior analysis.This paper presents evidence that model architectures designed for facial expression recognition can be seamlessly adapted for the action units detection task, taking advantage of the structural similarity between the two problems.As a sample case, we have adapted the Pyramid crOss-fuSion TransformER (POSTER) model for action unit detection by adjusting the architecture to handle a multilabel problem with one output per action unit.Then, we tuned the training parameters and retrained the model to achieve stateof-the-art performance on two widely used datasets: DISFA and BP4D.The results obtained with a standard 3-fold cross-validation setup show an average F1 score of 67.8% for DISFA and 65.5% for BP4D.These results outperform state-of-the-art models for AU detection, support the effectiveness of the approach, and suggest placing higher efforts on adapting existing architectures to leverage the synergies between facial expression recognition and action unit detection.
Integration of research with teaching. Experience of an university department

This article discusses general aspects of the functions of the university and the work of the university professor. Likewise, the results of some articles that have investigated the relationship between research and teaching are analyzed. Three positions are identified in this relationship. The first affirms that teaching and research are not related; therefore, a teacher can be very productive in one aspect without necessarily being productive in the other. A second position maintains that in fact there is a relationship of incompatibility between them. The third maintains that both are mutually reinforcing. In any case, there is currently a broad consensus on the convenience of bringing research and teaching closer together in the work of professors. Finally, some examples of how research has been integrated into the teaching activity of a university department are described. It is concluded that the relationship between research and teaching can provide benefits in the learning and training of students. Keywords: university; teaching; research
Improving Financial Literacy Knowledge from An Early Age by Socialization and Training to The Elementary School in Medan City

Financial literacy is the ability and the knowledge of individual or communities regarding to all their financial resources from decision making until how to manage it. Therefore, education about financial literacy is very necessary from an early age, especially starting from elementary school student. The problem at the 060888 Public Elementary School in Medan at this time is no financial literacy education, so that the correct introduction and implementation of financial literacy has not been implemented yet. By this community service program, students get a good understanding and knowledge about financial literacy. This program is carried out by several methods, there are financial literacy education, implementation of financial literacy, and provide reading corners by providing financial books for elementary school student as well as monitoring and evaluating.
Boosting ridge for the extreme learning machine globally optimised for classification and regression problems

Abstract This paper explores the boosting ridge (BR) framework in the extreme learning machine (ELM) community and presents a novel model that trains the base learners as a global ensemble. In the context of Extreme Learning Machine single-hidden-layer networks, the nodes in the hidden layer are preconfigured before training, and the optimisation is performed on the weights in the output layer. The previous implementation of the BR ensemble with ELM (BRELM) as base learners fix the nodes in the hidden layer for all the ELMs. The ensemble learning method generates different output layer coefficients by reducing the residual error of the ensemble sequentially as more base learners are added to the ensemble. As in other ensemble methodologies, base learners are selected until fulfilling ensemble criteria such as size or performance. This paper proposes a global learning method in the BR framework, where base learners are not added step by step, but all are calculated in a single step looking for ensemble performance. This method considers (i) the configurations of the hidden layer are different for each base learner, (ii) the base learners are optimised all at once, not sequentially, thus avoiding saturation, and (iii) the ensemble methodology does not have the disadvantage of working with strong classifiers. Various regression and classification benchmark datasets have been selected to compare this method with the original BRELM implementation and other state-of-the-art algorithms. Particularly, 71 datasets for classification and 52 for regression, have been considered using different metrics and analysing different characteristics of the datasets, such as the size, the number of classes or the imbalanced nature of them. Statistical tests indicate the superiority of the proposed method in both regression and classification problems in all experimental scenarios.
A Cross-Case Analysis Study on Scrum Culture Adoption in Three Digital Startups

Indonesian startups actively embrace Agile development, but implementation can be difficult due to complexity, rapid change, and ambiguity. Scrum, the most popular Agile methodology, assists organizations in dealing with issues that arise during product development. Cultivating a Scrum culture can also take time, but truly implementing it can increase productivity, product quality, and customer satisfaction. Many businesses embrace Agile without first assessing their values and principles, resulting in poor performance. This study investigates how the value of Scrum is quantified and applied to digital startups, with the goal of understanding metric results for evaluation and recommending a better implementation of Scrum culture. A cross-case analysis study was conducted on three digital startups in Indonesia, with data collected through interviews. To evaluate and enhance Scrum implementation, a Values, Goals, Questions, and Metrics (VGQM) model developed from Scrum values was presented. Businesses demonstrated the same strengths in commitment and focus for Scrum values but needed continuous improvement in different values of respect, openness, and courage for better prioritizing requests and delivering feedback. Based on the findings, recommendations were made, including extensive training, cross-functional collaboration, assessing tools and processes, and establishing clear boundaries for customer satisfaction and productive work. Improving product backlog management and employing effective Scrum tools were also mentioned. There are no metrics used by the companies in order to evaluate scrum culture implementation in their organizations. The study suggests constantly retaining those values, while continuously improving the values of openness and courage.
The Relationship Between Learning Concentration and Understanding Level Through the Online Learning Process

Learning using online methods is indeed beneficial in several ways, but in nursing study programs, online learning faces several problems. Problems such as lack of focus and concentration become obstacles in how students understand course material, especially for topics related to skills. This study contributes to knowing the relationship between learning concentration and understanding level through an online learning process. This research is a type of quantitative research with a cross-sectional research design consisting of 130 respondents and using a total sampling technique, the instrument used is a questionnaire. The analysis used was the chi-square test. Data is collected online using the Google form. Univariate analysis shows that in the majority of students, when participating in online learning, the level of concentration of their learning is in the middle category (70%). For most students, when participating in online learning, their understanding level is in the low category (60.8%). The results of the bivariate analysis with the chi-square test showed that there was a relationship between learning concentration and the level of understanding through the online learning process with a p-value = 0.000. There is a relationship between learning concentration and the level of understanding when participating in online learning.
Efficient Fuzzy-logic Control of Indoor Mobile Robots Evolving in Static and Dynamic Workspaces

Abstract This paper presents an efficient motion control approach for indoor mobile robots in both static and dynamic workspaces. The proposed system consists of two layers. In the Optimization Layer ( Global planner ), a Deterministic Constructive Algorithm ( DCA ) generates the best path for the mobile robot, represented as a set of nodes, to move from the initial position ( Source ) to the final position ( Target ) while avoiding the static obstacles. The Control layer ( Local planner ), which is the main focus of this article, consists of an Efficient Fuzzy-Logic Controller ( EFLC ), mimicking the human reasoning, to avoid the dynamic obstacles and drive safely the mobile robot along the generated path. The strength of the proposed EFLC approach is shown using V-REP software in dynamic workspaces.
Identification of parameters of interval nonlinear models of static systems using multidimensional optimization

The article proposes an approach to parametric identification of interval nonlinear models of static systems based on the standard problem of minimizing the root mean square deviation between the values ​​of the modeled characteristics of the static object and the values ​​belonging to the experimental intervals. As a result of expanding the parameter space of nonlinear models by introducing additional coefficients to match the predicted and experimental values into the objective function, a multidimensional optimization problem with a nonlinear multiextremal objective function is obtained. The paper examines the characteristics of the objective function and the convergence of its optimization.
Digital Pedagogy. Definition and Conceptual Area

Is there a digital pedagogy, as a distinct domain, different from general pedagogy and from any other pedagogy? If so, what are the aims, object of study, specific methods and principles of digital pedagogy? What is digital pedagogy and how does it articulate with education sciences? These are the questions of the beginning stage of an emerging area within the education sciences, where practice challenges us to expand the existing body of knowledge corpus and pedagogical methods. The article contributes to the contextualization of the set of information, experiences and scientific knowledge, conditions, relationships, meanings orbiting the phrase “digital pedagogy”. The term itself has been used for over 10 years – the first dedicated articles being from 2011, with elements of digital proto-pedagogy in 2004: Latham, apud Fyfe – but absorbing a series of much older concepts and practices, sometimes in a new way.
Secure and Efficient Two-Party Quantum Scalar Product Protocol With Application to Privacy-Preserving Matrix Multiplication

Secure two-party scalar product (S2SP) is a promising research area within secure multiparty computation (SMC), which can solve a range of SMC problems, such as intrusion detection, data analysis, and geometric computations. However, existing quantum S2SP protocols are not efficient enough, and the complexity is usually close to exponential level. In this paper, a novel secure two-party quantum scalar product (S2QSP) protocol based on Fourier entangled states is proposed to achieve higher efficiency. Firstly, the definition of unconditional security under malicious models is given. And then, an honesty verification method called Entanglement Bondage is proposed, which is used in conjunction with the modular summation gate to resist malicious attacks. The property of Fourier entangled states is used to calculate the scalar product with polynomial complexity. The unconditional security of our protocol is proved, which guarantees the privacy of all parties. In addition, we design a privacy-preserving quantum matrix multiplication protocol based on S2QSP protocol. By transforming matrix multiplication into a series of scalar product processes, the product of two private matrices is calculated without revealing any privacy. Finally, we show our protocol's feasibility in IBM Qiskit simulator.