Reviewerly/exHarmony huggingface dataset:
Optimizing SVM Hyperparameters using Predatory Swarms Algorithms for Use Case Points Estimation

The productivity factor is one of the key cost drivers in use case points estimation. However, this factor is uncertain because it is obtained from experts' guesses and does not depend on reliable historical data. This study aims to optimize the hyperparameters of the support vector machine technique through the use of three predatory-inspired algorithms, namely, Komodo mlipir algorithm, Grey wolf optimization, and Reptile search algorithm in predicting productivity factor rate. The optimizers searched for the best hyperparameter value to get an optimum hyperplane. In this work, we developed a hybrid model that consists of three primary stages: cluster generation using bisecting k-Medoids, prediction using an optimized support vector machine, and estimation using multiple linear regression. The hybrid model was evaluated using industrial and student projects datasets. Detailed investigations demonstrated that the komodo mlipir algorithm yielded the best mean value of 365.3 and 739.88, respectively, which has a significantly better hyperparameter of support vector machine model in predicting productivity factors among the reptile search algorithm for datasets 4 and 5 as of 0.03 (p < 0.05), but there is no significant difference among the grey wolf optimizer (p > 0.05). Meanwhile, the grey wolf optimizer obtained the best mean value of 366.47, with a significantly better hyperparameter compared with the reptile search algorithm for dataset four as of 0.047 (p < 0.05).
Intelligent Sentiment Analysis On Cellphone Checking By Police Officer

Twitter is one of social media used by the public to send and read tweets that have been shared, making it easier to express their opinions. The opinions found on Twitter are perceptions, both positive and negative. The abundance of public opinion can be used as research material to find information. The use of this information requires appropriate analytical techniques so that the gained information may help many parties in making decisions. Sentiment analysis technique is one of them. In this study, sentiment analysis was carried out to find out the public's opinion on the case of civilians' cellphone inspection/checking by a police officer. Naïve Bayes classifier and Support Vector Machine (SVM) classifier as comparison are employed and combined with RapidMiner and Python tools. Experimental results show SVM classifier provides the highest accuracy value of 94% for a 90:10 split ratio dataset with automatic labeling.
Improving the Accuracy of Text Classification Using the Over Sampling Technique in the Case of Sinovac Vaccine

The WHO has declared COVID-19 (Coronavirus Disease 2019) a global health emergency. Up to 19 November 2021, the total positive cases in Indonesia reached 4,252,705, of which 4,100,837 recovered, and 143,714 died. Therefore, vaccines have been developed to minimize COVID-19 transmission. There are some kinds of vaccines developed by several companies such as Sinovac, AstraZeneca, Pfizer, and Moderna. The general public has a different opinion on Sinovac vaccine on Twitter, where some people promote it while others reject it. Data used in this study were 1000 tweets about the Sinovac vaccine. During the dataset collection unequal distribution often occurs, where the number of labels is more on one side. Such a situation is called imbalance class. Imbalance class in a dataset can reduce classification performance. To overcome the imbalance class, this study used the Synthetic Minority Over-sampling Technique (SMOTE). The classification methods used were K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest, and TF-IDF was used to determine the weight of the words. The average rise of the accuracy value of the three algorithms after SMOTE optimization was 14%. The results of sentiment analysis for the Sinovac vaccine revealed a positive sentiment of 81%. Thus, it can be concluded that the Sinovac vaccine received a positive response from the public.
Binary Archimedes Optimization Algorithm based Feature Selection for Regression Problem

The use of datasets became paramount in many searches in one hand, on the other hand the rapidly growth of data size involves computational complexity and reduces model performances, this encourage us to find new methods to deal with this problem. Features Selection is the one of the main task used to resolve this issue. In this paper we propose a novel features selection method for regression task based on AOA (Archimedes Optimization Algorithm), experimental results shows that the proposed method can efficiently reduce dataset size and improve model performance.
Anomaly detection on MNIST stroke simulation dataset

MNIST handwritten digit dataset is one of the most popular datasets in the filed of machine learning and pattern recognition, whereas several other datasets have also been synthesized using the MNIST dataset. Anomaly detection is an admired area of research in the field of computer vision. In this context, the present research offers an anomaly detection technique using the method of next frame prediction in MNIST hand stroke simulation dataset. The MNIST hand stroke simulation dataset is a synthesized dataset that tries to mimic the hand stroke sequences that humans mostly use while writing digits. After the generation of synthesized dataset, anomaly is introduced in this dataset as a Gaussian noise in randomly selected frames for every simulation. Later, the anomaly detection task is accomplished done using a three layer neural network architecture where the first layer comprises convolutional neural network (CNN or ConvNet) for appearance encoding and the second layer comprises ConvGRU for memorizing all past frames which correspond to the motion information. Finally, the third layer is a deconvolutional network, which is a decoder and tries to generate the next frame. The derived dataset used in this paper is created using the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -Means clustering algorithm, along with a handcrafted features to help in identification of the sequence in which the clusters are chosen during the simulation. Finally, the performance of the proposed model is compared with the state-of-the-art recurrent neural network architectures.
A Biased Random-key Genetic Algorithm for Extractive Single-document Summarisation

Extractive text summarization has been dealt with by several metaheuristics that proved their efficiency. In those works the feasibility of solutions has been mostly guaranteed through some operators, whose role is to check and/or correct infeasible solutions. To reduce the complexity of the task, this works proposes a Biased Random-Key Genetic Algorithm, with a newly-proposed decoder, it is adapted to extractive single-document summarization. We have tested the performances of our approach on two standard datasets, DUC-2001 and DUC-2002, through using the ROUGE-1 and ROUGE-2 metrics. The results are very promising and show that our approach outperforms other reference methods, it came first out of 14 algorithms.
Texture Synthesis Using Improved Transfer Learning

Texture synthesis has been studied for more than two decades. Recent progress in deep learning is a great opportunity to revisit the texture synthesis based on convolutional neural networks. However, texture synthesizing is still a problem of trade-off generality for efficiency. In this paper different deep pre-trained Convolutional Neural Networks, usually used in classification problems have been considered. To our knowledge, a part of the VGG-19 model, none of the studied networks have been used in texture synthesis. Alexnet, GoogLenet, VGG16, VGG19 and ResNet 50 are used. Carried tests have shown that textures for various scales and structures have been well generated, surpassing results obtained by state-of-the-art methods.
Comparison Of Naive Bayes And Support Vector Machine For Detecting Hoax In Indonesian Tweet Case Study Of Tweet Covid-19

The rise of the infodemic containing false news or hoaxes and rumors about Covid-19 in society can worsen the pandemic situation itself. In addition, the infodemic can also be fatal to cause loss of life. This phenomenon that often appears in the community, such as drinking a cup of hot tea that has been dripped with eucalyptus oil can cure Covid-19 positive patients, making people believe that they ignore the health protocol recommendations. Therefore, to find out the truth or discrepancy of information in tweets, we can create a hoax detection system to minimize the emergence of hoax news. This study compares the Naive Bayes algorithm and the Support Vector Machine with a case study of Covid-19 tweets in Indonesian. The dataset collected is 500 tweets by manual labeling. For testing using the confusion matrix, the Naive Bayes method gets 78% accuracy and the support vector machine method gets 80% accuracy. The results of these tests prove that it can detect the truth of information or tweet using the support vector machine method with the highest accuracy.
A Novel Method of Requirements Analysis for Big Data Projects

Requirements engineering has a proven track record in reducing failures in typical IT projects. Big data projects are even more complex in terms of development and they too have experienced a lot of failure. The majority of research has focused on data analysis, the decision-making process and a little less on storage. However, few works have focused on the upstream phase on which all the other development phases depend on it. Our contributions to reduce the failure rate of IT projects lie in the proposal of a requirements engineering process dedicated to the specific characteristics of big data. Particularly in this work, we propose a new method for the activity of requirements analysis for the development of big data projects.
A Teacher Education Model for the Digital Age: Linking educational innovation and research

The digital age presents challenges for teacher preparation. The purpose of this study is twofold: (1) reliability and validity of the CD-REA scale, (2) Confirmation of the EBEI Model. A mixed-method, sequential-explanatory research was carried out in a couple of stages (QUAN and qual), relating questionnaires, interviews, plus observations. The sample in first stage is made up of 271 university professors and in the second stage we had 15 only. Two discoveries were found: (1) The digital competency scale and using of open educational resources (CD-REA) has proven to be a reliable and accurate tool for assessing digital abilities. (2) A high degree of interdependence between steps, which grows as the process progresses, demonstrated the validity of the Evidence-based Educational Innovation methodology (EBEI). A quality assessment tool for digital information competencies and a model for educational innovation that is supported by cutting-edge teaching techniques are both contributions made by this study.
Handwritten Digit Recognition Using Encryption Methods

Over the past years, deep learning techniques has had a strong impact on many areas of technical intelligence, including handwriting recognition. Handwriting recognition it is defined as the domain which allow a computer to interpret intelligible handwritten inputs from different sources. It is plays an important role in many areas, such as authenticating bank checking, exchanging remote computer files, and document duration. In this paper, we present a novel handwritten digit recognition method based on deep learning. In order to improve the performance of recognizing handwritten digits, we have proposed a new data augmentation method by using different encrypted digit images and image fusion techniques. The experimental results based on the CVL database shown that the proposed method achieved higher accuracy rates that were compared to the state of the art.
Digital Competencies in in-service teachers, a post-pandemic analysis

The objective of this work was to analyze the perception of Digital Teaching Competencies (DTC) in in-service teachers according to gender and age. The research approach was quantitative, descriptive, and non-experimental, the sample consisted of 100 graduate students, where Javier Tourón’s questionnaire was applied as an instrument to measure the Digital competencies of Teachers, and the assessment of the competence was stratified according to Stanone, finding that teachers who are 31 to 40 years old have good digital competences, These are framed within the five established competencies (Information and information literacy, communication and collaboration, Creation of digital content, Security, Problem-solving), it is also evident that women have a regular perception of their digital competencies compared to men who show a low perception of these competencies.
Multi-Resolution Medical Image Registration with Dynamic Convolution

Recent deep learning medical image registration (DLMIR) methods based on static convolutional neural network (CNN) have achieved advanced registration performance with feature representation and learning ability of CNN. To further improve the registration accuracy, the common practice is to increase the depth or width of the network, which increases the computational overhead. To address this problem, in this paper, we utilize dynamic convolution instead of static convolution to perform the registration task, where Dynamic convolution kernels are formed by the nonlinear aggregation of several parallel and input-dependent convolution kernels. We evaluate the proposed method on a public Magnetic Resonance (MR) brain scan dataset. The results demonstrate that the proposed method outperforms existing methods in terms of registration accuracy without increasing the depth and width.
Assessment of University Teachers on the Quality of Virtual Teaching

This study was carried out to evaluate the perception that the teaching staff of a private university in southern Peru had about the quality of virtual teaching conditioned by the COVID-19 pandemic. The study was conducted in 2022 with 341 participants, for a confidence level of 95% and a margin of error of 5%, measuring dimensions such as: academic aspect, materials and connectivity, participation and evaluation, and research and university social responsibility. The results showed that figures greater than the minimum proposed were reached, the best valued dimension was that of materials and connectivity and the least valued was research and university social responsibility. This change in teaching modality has represented an opportunity to improve the quality of university teaching, leaving interaction with other people - typical of attendance - in areas such as health sciences as a pending task.
The Future Teacher Facing the Challenge of Teaching

The reality of the classrooms of educational centers is far from university instruction, and with it, the problem arises in many factors that affect teacher training. Therefore, given the need for the future teacher to perform their work optimally, this study is proposed that aims to know the perceptions, sensations and shortcomings presented by the students of the Degree in Teacher of Early Childhood Education before the challenge of teaching, and prepare them as future teachers to face the reality of teaching, of the classroom and educational centers. The sample was formed by 55 students between 18 and 45 years old, of second year of the Degree of Teacher of early childhood education of the faculty of education of the University of Alicante. Used Cuestionario Autoevaluativo del Malestar Docente CAMDAlumnado. The results have shown that more than half of the sample believe they understand the teaching work, but do not perform the tutorial function, among other tasks pending development.
Live Demonstration: Pupil Tracking System Development for Electronystagmography

Electronystagmography (ENG) is a study used to clinically evaluate patients with dizziness, vertigo, or balance dysfunction. ENG provides an objective assessment of the oculomotor and vestibular systems. ENG abnormalities can be useful in the diagnosis and localization of the site of a lesion. However, ENG testing cannot be used to determine the specific site of lesion, the information acquired can be integrated with history, symptoms, and other test results to aid in diagnosis. [1]
People Tracking and Re-Identifying in Distributed Contexts: Extension Study of PoseTReID

In our previous paper, we introduced PoseTReID which is a generic framework for real-time 2D multi-person tracking in distributed interaction spaces where long-term people's identities are important for other studies such as behavior analysis, etc. In this paper, we introduce a further study of PoseTReID framework in order to give a more complete comprehension of the framework. We use a well-known bounding box detector YOLO (v4) for the detection to compare to OpenPose which was used in our last paper, and we use SORT and Deep-SORT to compare to centroid which was also used previously, and most importantly for the re-identification, we use a bunch of deep leaning methods such as MLFN, OSNet, and OSNet-AIN with our custom classification layer to compare to FaceNet which was also used earlier in our last paper. By evaluating on our PoseTReID datasets, even though those deep learning re-identification methods are designed for only short-term re-identification across multiple cameras or videos, it is worth showing that they give impressive results which boost the overall tracking performance of PoseTReID framework regardless the type of tracking method. At the same time, we also introduce our research-friendly and open source Python toolbox pyppbox, which is purely written in Python and contains all sub-modules which are used in this study along with real-time online and offline evaluations for our PoseTReID datasets. This pyppbox is available on GitHub https://github.com/rathaumons/pyppbox.
Mobile application for participatory simulation between business experts

To better manage an enterprise, but above all to preserve and extend its lifespan, it is necessary to manage the skills and knowledge of experts in the field. This paper deals with this aspect by proposing a mobile application for the capitalization of business expert's knowledge in the form of business rules, which facilitates their management, in the industrial maintenance field. The proposed approach is based on the following steps: the introduction of business rules, the consistency check, the recommendation of business experts who are grouped into clusters with the unsupervised classification algorithm K-means, and finally, the automation of the negotiation phase using the extended CNP (Contract Net Protocol) protocol. The results obtained are very promising and make it possible to deal with inconsistency problems such as: invalid rules, contradiction, redundancy and rules that are never applicable.
The price and promise of cryptocurrencies

While cryptocurrencies have been analysed from various angles, a governance perspective focusing on the price and promise of currencies has not been sufficiently explored. This chapter argues that cryptocurrencies can have emerging elements of complex semi-public goods by relying on theories of exchange rate governance. Most cryptocurrencies have flexible exchange rates leading to high price volatility encouraging speculative behaviour. However, focus on price flexibility reduces capacity to serve as a trusted medium of exchange for a wider public. However, exchange rate volatility as well as associated speculation is also associated with traditional currencies – particularly in less well-governed jurisdictions. Most importantly, among cryptocurrencies are so-called stablecoins with fixed exchange rate focusing of promise of exchange rate stability with the aims to reduce price volatility. Similar to traditional fixed exchange rate regimes, the credibility of this exchange rate commitment needs to be tested. The chapter concludes by discussion on how insights from governance of complex semi-public goods can improve broader acceptance of cryptocurrencies as a medium of exchange.
Access Beyond Borders: Linked Open Data Applications on Cultural Heritage

The main mission of cultural memory institutions is to protect their collections and to provide access to a wider audience. World Wide Web technology offers different tools to fulfill this mission. Among a range of applications, the most effective is the search engines’ search for metadata of collections. However, search engines are not always able to see all sources caused by various problems. Linked data is a publishing technique that uses standard web technologies to associate data with other data and makes them available on the Web. In available literature, it is seen that many memory institutions use Linked Open Data as a solution to the search engine problem with the main motivation of making their collections more accessible. However, these applications are still not at proficient level. In this study, Linked Open Data studies in the available literature were scanned and a process map was created based on these applications. These applications, which especially require technical knowledge and infrastructure, can be confusing for memory institutions that have not yet participated in the process. In this context, it is clear that the process map created as a result of this study will be enlightening for memory institutions. The most important point on the map is that an extensive decision-making mechanism should be formed with the stakeholders before starting the process and the implementation plan should be developed in detail over the problems of existing projects.
The impact of money supply on the development of virtual currencies

The total market capitalization of the most popular cryptocurrency, Bitcoin, reached one trillion dollars in 2021, equivalent to more than 1 percent of the world’s GDP. At the same time, in recent years, central banks have increased their money supply on an unprecedented scale. The volume of money supply between 2009 and 2019 has increased over 1.5 times more than between 1999 and 2009, while it has been 3.2 times more than in the years 1990 and 1999. In this chapter, a question was raised whether the monetary easing and the consequent increase in the worldwide money supply have contributed to the rapid development of the virtual currency market. To this extent, the effect of money supply was analysed measured by M2 in 16 countries on several virtual currency market indicators such as: Bitcoin capitalization, Bitcoin price, Google trends, number of cryptocurrencies, and number of transactions over the period 2008–2019. In this chapter, it was proved that quantitative easing has spurred the development of the virtual currency market; however, there are significant differences in the contributions of individual countries to this effect.
Analysis and Comparison of Chinese News Text Classification Methods Based on Deep Learning

As people in today's world consume an increasing amount of information, the number of Internet News is also vastly increasing. Facing all sorts of different kinds of news, how to accurately distinguished different types of news becomes the direction of many scholars' study.This article uses word cloud to represent keywords used in different domains of news. Moreover, we used two methods: TF-IDF and TextRank, to identify and analyze keywords of different fields of news. To understand the performance using various classification methods, we choose the THUCNews data sets. This data set collects ten fields of news in the history of Weibo. Moreover, we choose nine different kinds of machine learning algorithms, including SVM, XGBoost, RandomForest, GBDT, GRU, LSTM, CNN, RNN, and MLP, to investigate their performance. Among these nine models, GRU has an accuracy of 96.93%, SVM has an accuracy of 96.39%, CNN has an accuracy of 94.72%, and RandomForest has an accuracy of 92.97%, which make them stand out in their similar models. We used word-embedding vectorization for the Neural Network algorithm and TF-IDF vectorization for the others.