Reviewerly/exHarmony huggingface dataset:
LongSpam: Spam Email Detection using LSTM Algorithm

The email has swiftly become one of the most popular forms of communication because of its ease of use and speed on the internet. Not only is the email used for formal and informal correspondence, but it is also frequently utilized to transmit confidential information. Despite its widespread use, email has its drawbacks due to the persistent issues that its users continue to confront. Today's internet community faces serious challenges due to spam or unsolicited commercial communication. These flaws allow spammers to utilize email to spread their unwanted communications to tens of thousands of people. Here, we propose a detection model based on the LSTM algorithm for identifying spam and non-spam emails using a dataset from Kaggle comprising a total of 5.572 entries. The experimental findings show that the proposed model can successfully collect 1.115 samples as the test data and 4.457 samples as the training data. LongSpam has promise as a countermeasure against spam attacks since it can result in a lower threshold.
Realistic Face Masks Generation Using Generative Adversarial Networks

Understanding facial expressions is important for the interactions among humans as it conveys a lot about the person's identity and emotions. Research in human emotion recognition has become more popular nowadays due to the advances in the machine learning and deep learning techniques. However, the spread of COVID-19, and the need for wearing masks in the public has impacted the current emotion recognition models' performance. Therefore, improving the performance of these models requires datasets with masked faces. In this paper, we propose a model to generate realistic face masks using generative adversarial network models, in particular image inpainting. The MAFA dataset was used to train the generative image inpainting model. In addition, a face detection model was proposed to identify the mask area. The model was evaluated using the MAFA and CelebA datasets, and promising results were obtained.
Analysis for Data Mobility and Covid-19 Positive Rate with Multilayer Perceptron

COVID-19 has been affecting human mobility to avoid the risk of infection. Movement restriction was one of the government policies to reduce the rate of infection. However, the mobility was still occurred to be recorded during the policy. This action has led to the problem of the number of beds on hospital have to be prepared for the peak of infection. This study developed a model using Multilayer perceptron as a useful theorem in regression analysis to see the fitness approximation over this problem. Five layers neural networks combination have been used to see the performance of the model to reach the best fit of the model. The process of the study includes data acquisition of the influence of community mobility over the positive number of COVID-19, managed hyperparameters, and calculate the results of prediction in the form of the length of time the patient would be infected with COVID-19 from 2020 to 2021. This study found that the infection was happening mostly after 12 days of human mobility activity in public area such as ATM, market, park, and any public area recorded by Google mobility data. It was also showed the number of infections after 12 days in order to prepare the number of beds on hospital. Furthermore, this study found the best model with smallest loss value on 0.01452617616472448 with the gap number of infection from public area as much as 77 persons.
A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization

Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous characteristics and high-element dimensions. Many pattern classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) for FS are the main topics of our thorough assessment of the literature on the Swarm Intelligence (SI) algorithm. Furthermore, it illustrates how the hybrid SI technique is used in FS across various sectors. The hybrid SI technique uses applicable data from various FS methods to find feature subsets with smaller sizes and better classification performance than those found by regular FS algorithms.
Sentiment Classification of Visitors in Yogyakarta Palace using Support Vector Machine

Yogyakarta is one of Indonesia's most popular tourist destinations with its Yogyakarta Palace “Keraton”. Trip Advisor, one of the most extensive traveller guides, provides visitor reviews about the Keraton. Since the review is very important for Keraton management and was not analyzed yet, this research aims to classify the visitor sentiment on Keraton using the Support Vector Machine (SVM) method. The research method involved collecting comments from the website, preprocessing, including data cleaning, tokenizing, transforming, and stopwords filtering, as well as classifying the sentiment as positive and negative. This study showed that the SVM could classify the sentiment with 75.79% accuracy.
Games for Scrum Team Collaboration in the Global Software Development Environment: A Literature Review

Software development companies commonly use Global Software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies.
Implementation of Modified Linear Congruent Methods in Randomizing Exam Questions to Optimize the Learning Environment

Linear congruent methods are pseudo-random number generator algorithms often used for randomizing exam questions. The problem with LCM is that the questions that appear will be repeated several times, making the exam ineffective. If we select an improper parameter in LCM, the system will raise the same question in the next period. In this study, we modify LCM by converting linear equations into logarithmic equations. Furthermore, the proposed modification makes the quasi-random results into a full period so that the randomization of the exam questions produces different questions. This LCM modification is applied to a question randomization application using Visual Basic 6.0 software.
Sentiment Analysis of “Hepatitis of Unknown Origin” on Social Media Using Machine Learning

Hepatitis of Unknown Origin worries civil society around the world. The public's fear of the emergence of the mysterious hepatitis disease caused panic and expressed views and criticisms and poured them on social media Twitter. The government's policy in considering the spread of unknown hepatitis can be taken from the public's view, especially on social media, which accommodates various public sentiments. This study aims to analyze public sentiment on “hepatitis of the unknown origin” using the Twitter dataset. We conducted sentiment analysis using various machine learning algorithms to get the best model accuracy in analyzing public sentiment against “hepatitis of unknown origin”. The algorithms used in this research are Random Forest, Extra Tree Classifier, Decision Tree, Naïve Bayes, Support Vector Machine, and XGBoost Classifier. We tested the model's accuracy against the data set using 20-fold cross-validation. The results showed that the public discussed the spread of unknown hepatitis to children, with each algorithm detecting a greater number of negative sentiments than neutral and positive sentiments. In addition, the results of the comparison of the Decision Tree algorithm obtained a model accuracy of 81.92%%, Random Forest 78.99%, Extra Tree Classifier 79.82%, Naïve Bayes 77.79%, Support Vector Machine 75.52%, and XGBoost Classifier 71.49%. We conclude that the Decision Tree is the best model for analyzing public sentiment about “Hepatitis of Unknown Origin”.
Experimental Implementation of Shor's Quantum Algorithm to Break RSA

Nowadays, quantum computing is an important topic since it allows computations to be completed in a small amount of the time. The goal of using quantum computers (QC) is to solve an increasing number of problems that were previously intractable to address with traditional computing. The classical approaches work with bits, which are made up of 0s and 1s, whereas QC uses qubits. Classical computing is limited by storage capacity and speed of calculations, even when parallel computation is conducted on it. Compared to traditional methods, quantum parallelism allows the storage to be reduced exponentially with the potential of a shorter amount of time. Besides, Peter Shor's algorithm can solve the factorization problem used in RSA in polynomial time, whereas in a classical computer, factoring any big integer is intractable. The purpose of this research is to explore and implement quantum computing schemes and how Shor's algorithm can break RSA algorithms.
Neural Network for Solving Ordinary Differential Equations

Deep learning and machine learning are immensely prevalent and highly interactive in a myriad of fields, typically neural networks is widely used in mathematics. We outline a technique for employing artificial neural networks (ANN) to solve ordinary differential equations. For better illustration, we present the basic logic and formula of ANN and gradient computation, following with one typical first order differential equation as example. In order to research the flexibility and feasibility of our model, we compare several hyperparameters and different optimizer using control variable method. Finally, our neural networks model is applied into the second order differential equations with innovative modification by analogy. In this article, we illustrate the relatively novel method to solve the ordinary differential equations and examine our model through adjustable parameters, then convert into the second order which shows a wide application range.
NoSQL Cloud based Bigdata technologies for efficient performance evaluation in the modern era

Modern society's most recent technology breakthroughs have given rise to innovations that, when handled effectively, can result in value-added outcomes or, when handled incorrectly, can cause disruptions. One such technology is the plethora of NoSQL cloud databases that have developed. There isn't a single store that combines all of these properties, despite the fact that these provide a wide range of features like consistency, availability, fault-tolerance, scalability, and security. This aspect gains wide support if integrated with various learning platforms to yield an added outcome. Regardless of the dataset, applying pre-processing techniques and identifying data splits is a necessary step to do in order to infer better outcomes. Towards this end, an efficient NoSQL cloud datstore based analysis is presented to showcase the significance of NoSQL over traditional relational databases to overcome bigdata analysis based difficulties. Based on the type of data to be examined and the evaluation of the results, an appropriate cloud datastore is chosen.
Performance Evaluation of Machine Learning Models on Apache Spark: An Empirical Study

Artificial intelligence (AI) and machine learning significantly improve many sectors, such as education, healthcare, and industry. Machine learning techniques mainly depend on the volume and diversity of training data. With the digital transformation we live in, an abundant amount of data can be collected from different sources. However, the problem that needs to be addressed is how this amount of data can be processed and where it can be stored. Cloud services and distributed file systems (DFSs) help address this issue. Many DFSs such as Hadoop, Quantcast, and Apache Spark differ in many aspects, including scheduling algorithms, data management protocol, throughput, and runtime. Some DFSs may be better for working with specific applications than others. Apache Spark is capable of handling iterative operations like machine learning operations as well as it provides an integrated library of different machine learning algorithms called MLlib. In this paper, we evaluated the use of Spark using two machine learning algorithms, namely Logistic Regression (LR) and Random Forests (RF). We investigated the effect of varying the memory allocation configuration and the use of GPU. We concluded that the use of Spark greatly improves the runtime and memory consumption. However, its use has to be justifiable and needed for the size of the data due to different factors that affect the machine learning model's accuracy. The memory allocation should be kept to the minimum needed, and GPU should only be used when the machine learning algorithm used supports parallelization.
Artificial Intelligent approach for Colorful Image Colorization Using a DCNN

Coloring grayscale photos manually or using traditional coloring methods takes extensive user interaction. This may involve applying many colored scribbles, viewing related images, or doing segmentation. Even the most sophisticated software available in this day and age can take up to a month to color an image that was originally black and white. This occurs because the image contains a wide variety of color tones and tints. In this research work, we offer an innovative method for colorizing grayscale photographs that use deep learning techniques. First, we can separate the subject matter and aesthetic of several images and then recombine them into a single image by using a pre-trained convolutional neural network that was first developed for image categorization. Following this, we present an approach that may colorize a black-and-white image by combining the content of the black-and-white image with the style of a color image that has semantic similarities with the black-and-white image.
Intelligent Trust Ranking Security Preserving Model for B5G/6G

Trust assessment is a crucial parameter in the next generation WCN (Wireless Communication Network) for the secure collaboration of nodes with each other and the detection of the possible security menaces in the network. The trust ranking of nodes especially in the platforms of defense networks and healthcare networks is of vital importance to the security threats inside the network. In this paper, we have suggested an RM dataset based on the current possible attacks along with advanced authentication attributes for the next generation WCN (B5G/6G). Further, we proposed a trust-ranking model based on the SVM machine learning technique to define the trust ranks of the users present in the network. The model consists of five trust rankings. The fifth trust rank is defined as the highest level of trust and the first trust rank designates the lowest level of trust. The attained rank of trust determines the specific services offered to the corresponding node. The proposed prediction model based on the SVM (Support Vector Machine) algorithm maintains the triple-off balance between the computational time, accuracy, and security. The simulation results indicate that the proposed SVM-based trust ranking model intelligently identifies possible malicious threats and provides an advancement in network security.
<i>SAMA</i>: Spatially-Aware Model-Agnostic Machine Learning Framework for Geophysical Data

Geophysical data is a form of spatial data that suffers from various limitations when applying conventional machine learning algorithms and evaluation techniques.A key limitation facing models trained on geophysical data is their inability to generalize well when deployed to predict from new unseen data.We address the problem of inaccurate performance assessments of machine learning models, that stems from violating independence assumptions during the feature selection and evaluation phases of the learning process.Our proposed spatially-aware and model-agnostic (SAMA) framework provides a suite of spatially-aware feature generation, feature selection, and model validation algorithms that account for spatial characteristics of geophysical data.The framework is model agnostic, as it tackles data-related challenges that are not affected by the specific machine learning algorithm used to fit the data.To demonstrate the effectiveness of the proposed approach, it is applied to the water saturation mapping problem using a novel geophysical dataset to train a prediction model.The proposed spatially-aware models obtains an R 2 of 0.620, an RMSE of 0.220 for predicting water saturation for the Whole Region of the reservoir model box and an R 2 of 0.161, an RMSE of 0.263 for the Interwell Region.Extensive experiments on 5 additional unseen datasets show that the model maintains stable performance across different datasets, which demonstrates the ability of the SAMA framework to produce robust models that are transferable to new datasets.
Selecting optimal cut-off points for multiple variables in binary outcome settings

Categorization is an important and common practice in medical research, and finding cut-off points is a direct way of classification. The existing methods are mainly about computing cut-off points for only one variable. This paper proposes an algorithm to select cut-off points for multiple variables in binary outcome settings. This algorithm extends the search for cut-off points of one variable to multiple variables and is applied to the Breast Cancer Coimbra dataset. It shows a good classification effect and gives the confidence intervals of the cut-off points of the prognostic variables.
Parallel design and evaluation of GFSR (521,32) on FPGA

GFSR is a common parallel Random Number Generator (RNG). In this paper, the high-performance parallelization of GFSRc(521,32) on Field Programmable Gate Array (FPGA) is realized by using the hybrid method. The serial and the parallel random number sequence of different numbers are tested using the Diehard test, graphical test and application test. It is found that the random number sequences parallelized by GFSR (521,32) have good randomness. For 100 million random numbers generated, speedups of 5755× over single-threaded CPU platform and 453× over 32-threaded MIC platform are achieved. Experimental results show that GFSR (521,32) is a good random number generator suitable for large scale parallel Monte Carlo simulation on FPGA.
Some stylometric remarks on Ovid’s<i>Heroides</i>and the<i>Epistula Sapphus</i>

Abstract This article contributes to two well-worn areas of debate in classical Latin philology, relating to Ovid’s Heroides. The first is the question of the authenticity (and, to a lesser extent the correct position) of the letter placed 15th in almost every collection—the so-called Epistula Sapphus (henceforth ES). The secondary question, although perhaps now less fervently debated, is the authenticity of the ‘Double Heroides’, placed by those who accept them as letters 16–21. I employ a variety of methods drawn from the domain of computational stylometry to consider the poetics and the lexico-grammatical features of these elegiac poems in the broader context of a corpus of ‘shorter’ (from 20 to 546 lines) elegiac works from five authors (266 poems in all) representing most of classical elegy. Based on a variety of techniques, every measure gives clear indication that the poetic style of the Heroides is genuinely Ovidian, but distinctive; they can be accurately isolated from Ovid more broadly. The Single and Double Heroides split into two clear groups, with the ES grouped consistently with the single letters. Furthermore, by comparing the style of the letters with the ‘early’ (although there are complications in this label) works of the Amores and the late works of the Ex Ponto, the evidence supports sequential composition—meaning that the ES is correctly placed—and, further, supports the growing consensus that the double letters were composed significantly later, in exile.
Two-stage coevolution method for deep CNN: A case study in smart manufacturing

Smart manufacturing system is very complex and there are a lot of different types of data to deal with, which lead to the difficulty of usage. Frequently manually tuning hyperparameters and modifying the architecture of the network have become a major problem for participants, and it has seriously affected the application and promotion of Deep learning (DL) in industry. In order to solve this problem, a novel self-evolving deep CNN method: two-stage coevolution method (TSC) is proposed in this paper to automatically optimize the hyperparameters and effectively evolve the most suitable network by summarizing the characteristics of the excellent artificial architectures. The first stage is mainly to optimize the hyperparameters with Orthogonal experimental algorithm. The second stage is to produce the best deep CNN with the optimized hyperparameters through self-evolving computation. In the second stage, three well-known deep-CNN architectures are used as the initialization seeds and each seed is presented by a particle and a gene to coevolve the necessary factors for a deep CNN driven by particle swarm optimization (PSO) and genetic algorithm (GA). At last, a case study for smart manufacturing systems was carried out to demonstrate the effectiveness and convenience of the proposed method. And the TSC method was also compared with other two self-evolving methods. The experiment results show that TSC method is superior over other well-known algorithms.
RDF Subgraph Query Based on Common Subgraph in Distributed Environment

With the gradual development of the network, RDF graphs have become more and more complex as the scale of data increases; how to perform more effective query for massive RDF graphs is a hot topic of continuous research. The traditional methods of graph query and graph traversal produce great redundancy of intermediate results, and processing subgraph collection queries in stand-alone mode cannot perform efficient matching when the amount of data is extremely large. Moreover, when processing subgraph collection queries, it is necessary to iterate the query graph multiple times in the query of the common subgraph, and the execution efficiency is not high. In response to the above problems, a distributed query strategy of RDF subgraph set based on composite relation tree is proposed. Firstly, a corresponding composite relationship is established for RDF subgraph set, then the composite relation graph is clipped, and the redundant nodes and edges of the composite relation graph are deleted to obtain the composite relation tree. Finally, using the composite relation tree, a MapReduce-based RDF subgraph set query method is proposed, which can use parallel in the computing environment, the distributed query batch processing is performed on the RDF subgraph set, and the query result of the RDF subgraph set is obtained by traversing the composite relation tree. The experimental results show that the algorithm proposed in this paper can improve the query efficiency of RDF subgraph set.