Reviewerly/exHarmony huggingface dataset:
Data Clustering based on Modified Differential Evolution and Quasi-Opposition-based Learning

Differential Evolution (DE) has become an advanced, robust, and proficient alternative technique for clustering on account of their population-based stochastic and heuristic search manners. Balancing better the exploitation and exploration power of the DE algorithm is important because this ability influences the performance of the algorithm. Besides, keeping superior solutions for the initial population raises the probability of finding better solutions and the rate of convergence. In this paper, an enhanced DE algorithm is introduced for clustering to offer better cluster solutions with faster convergence. The proposed algorithm performs a modified mutation strategy to improve the DE’s search behavior and exploits Quasi-Opposition-based Learning (QBL) to choose fitter initial solutions. This mutation strategy that uses the best solution as a target solution and applies three differentials contributes to avoiding local optima trap and slow convergence. The QBL based initialization method also contributes to increasing the quality of the clustering results and convergence rate. The experimental analysis was conducted on seven real datasets from the UCI repository to evaluate the performance of the proposed clustering algorithm. The obtained results showed that the proposed algorithm achieves more compact clusters and stable solutions than the competing conventional DE variants. Moreover, the performance of the proposed algorithm was compared with the existing state of the art clustering techniques based on DE. The corresponding results also pointed out that the proposed algorithm is comparable to other DE based clustering approaches in terms of the value of the objective functions. Therefore, the proposed algorithm can be regarded as an efficient clustering tool.
Customized BERT with Convolution Model : A New Heuristic Enabled Encoder for Twitter Sentiment Analysis

The Twitter messaging service has turned out to be a domain for news consumers and patrons to convey their sentiments.Capturing these emotions or sentiments in an accurate manner remains a major challenge for analysts.Moreover, the Twitter data include both spam and authentic contents that often affects accurate sentiment categorization.This paper introduces a new customized BERT (Bidirectional Encoder Representations from Transformers) based sentiment classification.The proposed work consists on pre-processing and tokenization step followed by a customized BERT based classification via optimization concept.Initially, the collected raw tweets are pre-processed via "stop word removal, stemming and blank space removal".Prevailing semantic words are acquired, from which the tokens (meaningful words) are extracted in the tokenization phase.Subsequently, these extracted tokens will be subjected to classification via optimized BERT, which weights and biases are optimally tuned by Standard Lion Algorithm (LA).In addition, the maximum sequence length of BERT encoder is updated with standard LA.Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to different performance measures.
Financial Crisis, Globalisation and Development in Africa

This study unites two streams of research by simultaneously focusing on the impact of financial globalisation on financial development and pre- and post-crisis dynamics of the investigated relationship. The empirical evidence is based on 53 African countries for the period 2004–2011 and Generalised Method of Moments. The following findings are established. First, whereas marginal effects from financial globalisation are positive on financial dynamics of activity and size, corresponding net effects (positive thresholds) are negative (within range). Second, while decreasing financial globalisation returns are apparent for financial dynamics of depth and efficiency, corresponding net effects (negative thresholds) are positive (not within range). Third, financial development dynamics are more weakly stationary and strongly convergent in the pre-crisis period. Fourth, the net effect from the: pre-crisis period is lower on money supply and banking system efficiency; post-crisis period is positive on financial system efficiency and pre-crisis period is positive on financial size. JEL Codes: F02, F21, F30, F40, O10
Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

Active learning is the category of partially supervised algorithms that is differentiated by its strategy to combine both the predictive ability of a base learner and the human knowledge so as to exploit adequately the existence of unlabeled data. Its ambition is to compose powerful learning algorithms which otherwise would be based only on insufficient labelled samples. Since the latter kind of information could raise important monetization costs and time obstacles, the human contribution should be seriously restricted compared with the former. For this reason, we investigate the use of the Logitboost wrapper classifier, a popular variant of ensemble algorithms which adopts the technique of boosting along with a regression base learner based on Model trees into 3 different active learning query strategies. We study its efficiency against 10 separate learners under a well-described active learning framework over 91 datasets which have been split to binary and multi-class problems. We also included one typical Logitboost variant with a separate internal regressor for discriminating the benefits of adopting a more accurate regression tree than one-node trees, while we examined the efficacy of one hyperparameter of the proposed algorithm. Since the application of the boosting technique may provide overall less biased predictions, we assume that the proposed algorithm, named as Logitboost(M5P), could provide both accurate and robust decisions under active learning scenarios that would be beneficial on real-life weakly supervised classification tasks. Its smoother weighting stage over the misclassified cases during training as well as the accurate behavior of M5P are the main factors that lead towards this performance. Proper statistical comparisons over the metric of classification accuracy verify our assumptions, while adoption of M5P instead of weak decision trees was proven to be more competitive for the majority of the examined problems. We present our results through appropriate summarization approaches and explanatory visualizations, commenting our results per case.
Eksperimen Pengenalan Wajah dengan fitur Indoor Positioning System menggunakan Algoritma CNN

Facial recognition work combined with the facial owner's position estimation feature can be utilized in various everyday applications such as face attendance with position detection. Based on this, this study offers a system testing experiment that can be run with facial recognition features and an Indoor Positioning System (IPS) to automatically check the location of the owner of the face. Recently, deep learning algorithms are the most popular method in the world of artificial intelligence. Currently, the Deep Learning algorithm toolbox has provided various programming language platforms. Departing from research findings related to deep learning, this study utilizes this method to perform facial recognition. The system we offer is also capable of checking the position or whereabouts of objects using Indoor Positioning System (IPS) technology. Facial recognition evaluation using CNN obtained a maximum value = 92.89% and an accuracy error value of 7.11%. Meanwhile, the average accuracy obtained is 91.86%. In the evaluation of the estimated position tested using DNN, the highest value of r2 score is 0.934, the lowest is 0.930 and an average is 0.932 and the highest value is MSE is 4.578, the lowest is 4.366 and the average is 4.475. This shows that the facial recognition process that is tested is able to produce good values but not the position estimation process. Keywords: Face Recognition, IPS, CNN, MSE, Accuraccy.
The GraphNet Zoo: An All-in-One Graph Based Deep Semi-supervised Framework for Medical Image Classification

We consider the problem of classifying a medical image dataset when we have a limited amounts of labels. This is very common yet challenging setting as labelled data is expensive, time consuming to collect and may require expert knowledge. The current classification go-to of deep supervised learning is unable to cope with such a problem setup. However, using semi-supervised learning, one can produce accurate classifications using a significantly reduced amount of labelled data. Therefore, semi-supervised learning is perfectly suited for medical image classification. However, there has almost been no uptake of semi-supervised methods in the medical domain. In this work, we propose an all-in-one framework for deep semi-supervised classification focusing on graph based approaches, which up to our knowledge it is the first time that an approach with minimal labels has been shown to such an unprecedented scale with medical data. We introduce the concept of hybrid models by defining a classifier as a combination between an energy-based model and a deep net. Our energy functional is built on the Dirichlet energy based on the graph p-Laplacian. Our framework includes energies based on the \(\ell _1\) and \(\ell _2\) norms. We then connected this energy model to a deep net to generate a much richer feature space to construct a stronger graph. Our framework can be set to be adapted to any complex dataset. We demonstrate, through extensive numerical comparisons, that our approach readily compete with fully-supervised state-of-the-art techniques for the applications of Malaria Cells, Mammograms and Chest X-ray classification whilst using only 20% of labels .
Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia

The COVID-19 pandemic is the first and foremost health crisis in the world. Coronavirus is a collection of viruses from the subfamily Orthocronavirinae in the Coronaviridae family and the order of Nidovirales. This group of viruses that can cause disease in birds and mammals, including humans. In humans, coronaviruses cause generally mild respiratory infections, such as colds, although some forms of disease such as; SARS, MERS, and COVID-19 are more deadly. Anticipating and reducing the number of corona virus sufferers in Indonesia has been carried out in all regions. Among them by providing policies to limit activities out of the house, school activities laid off, work from home (work from home), even worship activities were laid off. This has become a government policy based on considerations that have been analyzed to the maximum, of course. Therefore this research was carried out as an anticipation step towards the Covid-19 pandemic by predicting the spread of Covid-19, especially in Indonesia. The research method applied in this research is problem analysis and literature study, collecting data and implementation. The application of the naive bayes method is expected to be able to predict the spread rate of COVID-19 in Indonesia. The results of the Naive Bayes method classification show that 16 data from 33 data were tested in Covid-19 cases per province with an accuracy of 48.4848%, where of the 33 data tested in the Covid-19 case per province tested there were 16 data that were successfully classified correctly.
Six sigma application for raising student academic achievement

This paper presents a case study on how Six Sigma methodology can be used to improve student academic performance in three main subjects (Math, Arabic, and English) in a private secondary school located in the Middle East. The Six Sigma methodology of Define, Measure, Analyze, Improve and Control (DMAIC) was used to improve student performance along with several improvement tools such as Fishbone diagram, Pareto charts, and Critical-To-Quality (CTQ) tree. Introducing quality improvement concepts and tools resulted in a series of improvements leading to an increase in student academic achievement quantified by Sigma Quality Level (SQL). The SQL improved in Math from (2.01) to (2.37) and in Arabic from (1.86) to (2.38). On the other hand, English grades did not increase from baseline. The results of this study provide a clear, step-by-step approach to systematically approaching and solving educational problems using the DMAIC model. Practically speaking, the results of this study provides clear evidence that the DMAIC can be used in the educational sector by school administrators and teachers to improve student grades.
On the Strategies to Cultivate College Students’ Autonomous English Learning Ability in the New Era

The cultivation of students&amp;rsquo; autonomous learning ability is one of the significant and essential tasks of college English teaching. In this paper, the current situation of college students&amp;rsquo; insufficient autonomous English learning ability is thoroughly analyzed. Some effective and feasible strategies to cultivate students&amp;rsquo; autonomous English learning ability are put forward based on the factors that influence this ability. Interaction, continuous thinking, cultural introduction and a good network environment are indispensable to develop and enhance this autonomous learning ability.
Is College Students' Reading Habit Truly Related to Their Writing Ability?

The research aimed to describe college students’ reading habits and find the relationship between their reading habits and their writing ability. The data were collected from 50 non-English Department students who took English subject whose content was English language skills, including reading and writing. A questionnaire about reading habits was created and distributed to these students. Then in the classroom, they were assigned to write an essay about a topic they like twice. The analysis was done by analyzing the questionnaire’s results on reading habits, then comparing these results with writing scores using a statistical program, SPSS. The findings show that generally, more than half of the respondents usually read, even though they do not read quite often. Most students think that reading contributes to writing, especially in vocabulary and gathering ideas. However, their reading habits do not have a correlation with their writing ability.
Disruption Caused by the COVID-19 Pandemic in Peruvian University Education

The objective of the research was to analyze the effects of the disruption that Peruvian university education has suffered due to COVID-19 in 2020. The type of research was basic under the design of grounded theory (De la Espriella &amp; Gómez Restrepo, 2020). The study scenario was university education in the first semester of 2020, considering more than twenty academic texts; between articles, texts and reports related to university educational disruption. The technique implemented was that of documentary analysis. And in the procedure for collecting information, the inclusion and exclusion criteria were taken into account. The results show that disruption is the break, abrupt or sudden interruption caused within a current paradigm. Therefore, the paradigm of face-to-face Peruvian university education due to the COVID-19 pandemic has suffered a sudden interruption; so much so that article 47 of University Law N° 30220 was modified; The modalities of face-to-face, semi-face-to-face and distance or non-face-to-face teaching were established, introducing university higher education in the new paradigm of online education. For this reason, universities should be required to leave their comfort zone and incorporate information and communication technologies as a possibility and opportunity for academic development.
Mobile Forensic Tools Validation and Evaluation for Instant Messaging

Mobile technology is experiencing rapid development from year to year. Various types of models and operating systems are available on the market, followed by the development of applications for mobile devices. Behind the development of mobile technology, mobile devices are often used for crime. To handle a case related to a mobile device, an investigator needs to use forensic methodologies. Investigator also needs to know which tools are capable of handling mobile forensics of a specific artefact or mobile devices since each forensic tool has its limitation. The rapid development of mobile technology and the lack of understanding of forensic tools sometimes become an obstacle for an investigator in handling a case. This research conducted a forensic analysis of WhatsApp (WA) application on the Samsung Galaxy S4 and Samsung A3 using the logical acquisition of 3 forensic tools, namely: WA Key/DB Extractor, Oxygen Forensics, and Magnet AXIOM. National Institute of Standards and Technology (NIST) forensic tool parameters and additional parameters related to WA artefact s were used to evaluate forensic tools which will then be calculated to find acquisition capability index for each forensic tool. Acquisition capability index is expected to provide an overview and recommendations regarding forensic tools for conducting WA forensic analysis. Based on the acquisition capability index, Magnet AXIOM has advantages over Oxygen Forensics, and WA Key/DB Extractor in conducting forensic analysis of WA artefact s on Samsung Galaxy S4 and Samsung A3 with 77.77%. Thus it can be concluded that Magnet AXIOM is recommended to be used in handling WA artefacts.
Prediksi Perkembangan Jumlah Pelanggan Listrik Menurut Pelanggan Area Menggunakan Algoritma Backpropagation

Electricity is one of the vital needs of humanity. Without electricity, it is certain that the wheels of the economy will not be able to run properly. So that electricity customers are increasingly increasing, as they increase the needs and population of the community. Therefore this study aims to determine the development of the number of electricity customers using the backpropagation algorithm. The research data used was electricity customer data by area (customer) in North Sumatra in 2013-2017, obtained from the Central Statistics Agency of North Sumatra. This study uses 5 architectural models, namely 4-2-1, 4-3-1, 4-4-1, 4-5-1, and 4-6-1. Of the five architectural models used, one of the best architectural models is obtained 4-4-1 with an accuracy rate of 88%, epoch 716 iterations in a short amount of time, 15 seconds, with MSE Training 0,00099763 and MSE testing 0.00109935. Based on the best architectural model, this will be used to predict the Development of Electricity Customers by Area Customers in North Sumatra from 2018 to 2020
Handwritten Gurmukhi Digit Recognition System for Small Datasets

In the present study, a method to increase the recognition accuracy of Gurmukhi (Indian Regional Script) Handwritten Digits has been proposed. The proposed methodology uses a DCNN (Deep Convolutional Neural Network) with a cascaded XGBoost (Extreme Gradient Boosting) algorithm. Also, a comprehensive analysis has been done to apprehend the impact of kernel size of DCNN on recognition accuracy. The reason for using DCNN is its impressive performance in terms of recognition accuracy of handwritten digits, but in order to achieve good recognition accuracy, DCNN requires a huge amount of data and also significant training/testing time. In order to increase the accuracy of DCNN for a small dataset more images have been generated by applying a shear transformation (A transformation that preserves parallelism but not length and angles) to the original images. To address the issue of large training time only two hidden layers along with selective cascading XGBoost among the misclassified digits have been used. Also, the issue of overfitting is discussed in detail and has been reduced to a great extent. Finally, the results are compared with performance of some recent techniques like SVM (Support Vector Machine) Random Forest, and XGBoost classifiers on DCT (Discrete Cosine Transform) and DWT (Discrete Wavelet Transform) features obtained on the same dataset. It is found that proposed methodology can outperform other techniques in terms of overall rate of recognition.
Wavelet Based Deep Recursive Pyramid Convolution Residual Network for Single Image Rain Removal

Image rain removal aims to separate the background image from the rainy image.During the past three years, the image rain removal with deep convolutional neural networks has achieved impressive performance.However, how to reach tradeoff between high de-raining performance and low model parameters is still a challenge.To address the issue, the paper is devoted to exploring a novel method based on wavelet deep recursive pyramid convolution residual network (WDRPRN), in which discrete wavelet transform is embedded to decompose the rainy image in different frequency domains, and the deep recursive pyramid convolution residual network (DRPRN) can well predict the residual coefficients between rainy image and clean image.In addition, compared with other neural networks, the DRPRN adopts recursive model that can cost fewer parameters.Plentiful of experiments on synthetic and real-world datasets show that the proposed method is significantly superior to the recent state-of-the-art algorithms.
Non-Interactive Dealer-Free Dynamic Threshold Secret Sharing Based on Standard Shamir’s SS for 5G Networks

Wireless group communications and mobile computing have demonstrated its potential capacity in the next generation of mobile communication networks and wireless systems (5G), where devices have the particularity of being heterogeneous and so have different capabilities in terms of storage, computing, communication and energy. Conventional protocols are not suitable for 5G networks since this environment needs more flexible and simple protocols for secure group communications. Hence, how to realize the dynamical security is a big challenge for 5G networks. In data security management, the longer the system runs, the greater the attacker's capabilities become. A threshold changeable secret sharing scheme (TCSS) in which shares of a (t, n) SS generated by the dealer initially can be used to reconstruct the secret but having a larger thresholdj, (i.e., t <; j ≤ n), is a secure way to protect the secret for a longer period of time. A straightforward approach to design a non-interactive dealer-free TCSS is to let the dealer follow Shamir's SS to generate multiple shares for different thresholds, i, for i = t, t + 1, · · · , n. Using this approach, each shareholder needs to store n - t + 1 shares. In this article, we propose a non-interactive ⌈n-1/t⌉ dealer-free TCSS in which each shareholder only needs to store shares. Our proposed TCSS can t support standard Shamir's (t, n) SS. Our technique can thus be applied to existing Shamir schemes even if they were set up without consideration to future threshold increases. It is unconditionally secure and simpler than most of the existing schemes.
Lifting Based Object Detection Networks of Remote Sensing Imagery for FPGA Accelerator

The on-board object detection of remote sensing images is of great significance and challenges in both military and civilian fields. To achieved on-board object detection, we require a high-performance and low power hardware architecture. Inspired by the hardware implementation advantages of the lifting scheme in JPEG2000, we introduce the lifting scheme into object detection networks to promote the hardware implementation capability. Based on the equivalence between the lifting scheme and the convolution, we proposed a lifting based FPGA accelerator. The lifting scheme provides a parallel strategy for every convolutional layer to obtain high performance, and high resource utilization. The proposed accelerator can improve the throughput of networks, and promote the utilization efficiency of hardware resources. The experimental results on lifting based YOLOv3-tiny (LS-YOLOv3-tiny) show that the proposed accelerator has better power efficiency than other hardware implementation, and have better DSP efficiency than other FPGA-based designs.