Reviewerly/exHarmony huggingface dataset:
A New Fuzzy Lexicon Expansion and Sentiment Aware Recommendation System in e-Commerce

Customers' feedbacks are necessary for an online business to enrich themselves. The customers' feedback reflects the quality of the products and the e-commerce services. The companies are in a position to concentrate more and analyze the customers' feedback or reviews carefully by applying new techniques for predicting the current trends, customers' expectations, and the quality of their services. The e-business will succeed when one accurately predicts customer purchase patterns and expectations. For this purpose, we propose a new fuzzy logic incorporated sentiment analysis-based product recommendation system to predict the customers' needs and recommend suitable products successfully. The proposed system incorporates a newly developed sentiment analysis model which incorporates the classification through fuzzy temporal rules. Moreover, the basic level data preprocessing activities such as stemming, stop word removal, syntax analysis and tokenization are performed to enhance the sentiment classification accuracy. Finally, this product recommendation system recommends suitable products to the customers by predicting the customers' needs and expectations. The proposed system is evaluated using the Amazon dataset and proved better than the existing recommendation systems regarding precision, recall, serendipity and nDCG.
Hierarchical Convolutional Neural Networks using CCP-3 Block Architecture for Apparel Image Classification

In fashion applications, deep learning has been applied automatically to recognize and classify the apparel images under the massive visual data, emerged on social networks.To classify the apparel correctly and quickly is challenging due to a variety of apparel features and complexity of the classification.Recently, the hierarchical convolutional neural networks (H-CNN) with the VGGNet architecture was proposed to classify the fashion-MNIST datasets.However, the VGGNet (many layers) required many filters (in the convolution layer) and many neurons (in the fully connected layer), leading to computational complexity and long training-time.Therefore, this paper proposes to classify the apparel images by the H-CNN in cooperated with the new shallow-layer CCP-3-Block architecture, where each building block consists of two convolutional layers (CC) and one pooling layer (P).In the CCP-3-Block, the number of layers can be reduced (in the network), the number of filters (in the convolution layer), and the number of neurons (in the fully connected layer), while adding a new connection between the convolution layer and the pooling layer plus a batch-normalization technique before passing the activation so that networks can learn independently and train quickly.Moreover, dropout techniques were utilized in the feature mapping and fully connected to reduce overfitting, and the optimizer adaptive moment estimation was utilized to solve the decaying of gradients, which can improve the networkperformance.The experimental results showed that the improved H-CNN model with our CCP-3-Block outperformed the recent H-CNN model with the VGGNet in terms of decreased loss, increased accuracy, and faster training.
Offensive Language Identification in Low Resource Languages using Bidirectional Long-Short-Term Memory Network

Offensive language identification is a critical task in today's digital era, enabling the development of effective content moderation systems.However, it poses unique challenges in low resource languages where limited annotated data is available.This research paper focuses on addressing the problem of offensive language identification specifically in the context of a low resource language, namely the Kazakh language.To tackle this challenge, we propose a novel approach based on Bidirectional Long-Short-Term Memory (BiLSTM) networks, which have demonstrated strong performance in natural language processing tasks.By leveraging the bidirectional nature of the BiLSTM architecture, we capture both contextual dependencies and long-term dependencies in the input text, enabling more accurate offensive language identification.Our approach further utilizes transfer learning techniques to mitigate the scarcity of annotated data in the low resource setting.Through extensive experiments on a Kazakh offensive language dataset, we demonstrate the effectiveness of our proposed approach, achieving state-of-the-art results in offensive language identification in the low resource Kazakh language.Moreover, we analyze the impact of different model configurations and training strategies on the performance of our approach.The findings from our study provide valuable insights into offensive language identification techniques in low resource languages and pave the way for more robust content moderation systems tailored to specific linguistic contexts.
Technology Adoption and Usage Behaviors in Field Incident Management System Utilization

This study utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) model to analyze the adoption and utilization of field incident management system (IMS) in a manufacturing organization.The study specifically focused on the role of behavior as a key factor in the adoption and utilization of incident management system.Data was collected through a survey of employees who had utilized the IMS system and the UTAUT model was used to analyze the data.The results indicated that behavior in the system significantly influenced the adoption and utilization of IMS.The study also found that the UTAUT model provided a useful framework for understanding the adoption and utilization of IMS, particularly the importance of performance expectancy, effort expectancy, social influence, and facilitating conditions.The study provides valuable insights for organizations looking to implement IMS and improve their incident management processes.It highlights the importance of building behavior in the system through appropriate user experience and user training.The findings of this study have important implications for manufacturing organizations seeking to enhance their incident management procedures through the adoption and utilization of IMS.
Enhanced Gravitational Search Algorithm Based on Improved Convergence Strategy

Gravitational search algorithm (GSA) is one of the metaheuristic algorithms that has been popularly implemented in solving various optimization problems.The algorithm could perform better in highly nonlinear and complex optimization problems.However, GSA has also been reported to have a weak local search ability and slow searching speed to achieve its convergence.This research proposes two new parameters in order to improve GSA's convergence strategy by improving its exploration and exploitation capabilities.The parameters are the mass ratio and distance ratio parameters.The mass ratio parameter is related to the exploration strategy, while the distance ratio parameter is related to the exploitation strategy of the enhanced GSA (eGSA).These two parameters are expected to create a good balance between the exploration and the exploitation strategies in eGSA.There are seven benchmark functions that have been tested on eGSA.The results have shown that eGSA has been able to produce good performance in the minimization of fitness values and execution times, compared with two other GSA variants.The testing results have shown that the enhancements made to GSA have successfully improved the algorithm's convergence strategy.The improved convergence has also been able to improve the algorithm's solution quality and the processing time.It is expected that eGSA could be applied in many fields and solve various optimization problems efficiently.
Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

In the construction process of high-rise buildings, it is necessary to predict the settlement and deformation of the foundation, and the current prediction methods are mainly based on empirical theoretical calculations and methods and more accurate numerical analysis methods.In the face of the interference of complex and ever-changing terrain and parameter values on prediction methods, in order to accurately determine the settlement of building foundations, this study designed a smart city building foundation settlement prediction method based on BP neural network.Firstly, a real-time dynamic monitoring unit for building foundation settlement was constructed using Wireless Sensor Network (WSN) technology.Then, the monitoring data was used to calculate the relevant parameters of building foundation settlement through layer sum method.Finally, input the monitoring data into the BP network results, adjust the weights of the output layer and hidden layer using settlement related parameters, and output the settlement prediction results of the smart city building foundation through training.The study selected average error and prediction time as evaluation criteria to test the feasibility of the method proposed in this article.This method can effectively predict foundation settlement, with an average prediction error always less than 4% and a prediction process time always less than 49ms.
Facial Image Generation from Bangla Textual Description using DCGAN and Bangla FastText

The synthesis of facial images from textual descriptions is a relatively difficult subfield of text-to-image synthesis.It is applicable in various domains like Forensic Science, Game Development, Animation, Digital Marketing, and Metaverse.However, no work was found that generates facial images from textual descriptions in Bangla; the 5th most spoken language in the world.This research introduces the first-ever system to generate facial images from Bangla textual descriptions.The proposed model comprises two fundamental constituents, namely a textual encoder, and a Generative Adversarial Network(GAN).The text encoder is a pre-trained Bangla text encoder named Bangla FastText which is employed to transform Bangla text into a latent vector representation.The utilization of Deep Convolutional GAN (DCGAN) allows for the generation of face images that correspond to text embedding.Furthermore, a Bangla version of the CelebA dataset, CelebA Bangla is created for this study to develop the proposed system.CelebA Bangla contains images of celebrities, their corresponding annotated Bangla facial attributes and Bangla Textual Descriptions generated using a novel description generation algorithm.The proposed system attained a Fréchet Inception Distance (FID) score of 126.708,Inception Score(IS) of 12.361, and Face Semantic Distance(FSD) of 20.23.The novel text embedding strategy used in this study outperforms prior work.A thorough qualitative and quantitative analysis demonstrates the superior performance of the proposed system over other experimental systems.
A Hybrid Method Based on Gravitational Search and Genetic Algorithms for Task Scheduling in Cloud Computing

Cloud computing has emerged as a novel technology that offers convenient and cost-effective access to a scalable pool of computing resources over the internet.Task scheduling plays a crucial role in optimizing the functionality of cloud services.However, inefficient scheduling practices can result in resource wastage or a decline in service quality due to under-or overloaded resources.To address this challenge, this research paper introduces a hybrid approach that combines gravitational search and genetic algorithms to tackle the task scheduling problem in cloud computing environments.The proposed method leverages the strengths of both gravitational search and genetic algorithms to achieve enhanced scheduling performance.By integrating the unique search capabilities of the gravitational search algorithm with the optimization and adaptation capabilities of the genetic algorithm, a more effective and efficient solution is achieved.The experimental results validate the superiority of the proposed method over existing approaches in terms of total cost optimization.The experimental evaluation demonstrates that the hybrid method outperforms previous scheduling methods in achieving optimal resource allocation and minimizing costs.The improved performance is attributed to the combined strengths of the gravitational search and genetic algorithms in effectively exploring and exploiting the solution space.These findings underscore the potential of the proposed hybrid method as a valuable tool for addressing the task scheduling problem in cloud computing, ultimately leading to improved resource utilization and enhanced service quality.
Hybrid Encryption Algorithm for Information Security in Hadoop

Network security has gained importance in recent years.Information system security is greatly aided by the development of encryption as a solution.To safeguard the shared information, several strategies are required.Thanks to the cutting-edge Internet, networking corporations, health information, and cloud applications, our data is growing exponentially every minute.In order to handle enormous amounts of data efficiently, a new application called Hadoop distributed file system (HDFS) was created.However, HDFS doesn't come with any built-in data encryption tools, which poses serious security risks.In order to increase data security, encryption techniques are established; nevertheless, standard algorithms fall short when dealing with bigger files.In this study, huge data will be secured using a novel hybrid encryption algorithm that combines CP-ABE (encryption based on the features of the encryption policy), AES (advanced standard encryption), and RSA (Rivest-Shamir-Adleman).The suggested model's performance is compared against that of traditional encryption algorithms like DES, 3DES, and Blowfish in order to demonstrate improved performance as it relates to decryption time, encryption time, and throughput.The results of the studies demonstrate that our suggested method's algorithm is more secure.
Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

This study aims to provide an overview of the current research on detecting abusive language in Indonesian social media.The study examines existing datasets, methods, and challenges and opportunities in this field.The research found that most existing datasets for detecting abusive language were collected from social media platforms such as Twitter, Facebook, and Instagram, with Twitter being the most commonly used source.The study also found that hate speech is the most researched type of abusive language.Various models, including traditional machine learning and deep learning approaches, have been implemented for this task, with deep learning models showing more competitive results.However, the use of transformer-based models is less popular in Indonesian hate speech studies.The study also emphasizes the importance of exploring more diverse phenomena, such as islamophobia and political hate speech.Additionally, the study suggests crowdsourcing as a potential solution for the annotation approach for labeling datasets.Furthermore, it encourages researchers to consider code-mixing issues in abusive language datasets in Indonesia, as it could improve the overall model performance for detecting abusive language in Indonesian data.The study also suggests that the lack of effective regulations and the anonymity afforded to users on most social networking sites, as well as the increasing number of Twitter users in Indonesia, have contributed to the rising prevalence of hate speech in Indonesian social media.The study also notes the importance of considering code-mixed language, out-of-vocabulary words, grammatical errors, and limited context when working with social media data.
An Empirical Deep Learning Approach for Arabic News Classification

In this paper, we tackle the problem of Arabic news classification.A dataset of 5,000 news articles from various Saudi Arabian news sources were gathered, classified into six categories: business, entertainment, health, politics, sports, and technology.We conducted experiments using different preprocessing techniques, word embeddings, and deep learning architectures, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, as well as a hybrid CNN-LSTM model.Our proposed model achieved an accuracy of 93.15, outperforming other models in terms of accuracy.Moreover, our model is evaluated on other Arabic news datasets and obtained competitive results.Our approach demonstrates the effectiveness of deep learning methods in Arabic news classification and emphasizes the significance of careful selection of preprocessing techniques, word embeddings, and deep learning architectures.
Intelligent Recommendation of Open Educational Resources: Building a Recommendation Model Based on Deep Neural Networks

Information overload is a challenge for the development of online education.To address the problem of intelligent recommendation of educational resources, the study proposes an intelligent recommendation model of educational resources based on deep neural networks.First, a deep neural network-based custom recommendation model for educational resources is constructed after a multilayer perceptron-based prediction model is established.The results showed that the prediction model proposed in the study steadily reduced the average absolute error as the number of iterations increase, reaching an average of 0.704, with the loss value stabilising at around 0.6, which is lower than that of the deep neural network prediction model.Compared to the deep neural network prediction model, the normalised discounted cumulative gain is typically 0.01 higher and in terms of hit rate, 0.03 higher.The prediction time of the similarity algorithm is faster than that of the neural network.The mean squared error ranged from a high of 1.29 to a low of 1.19, both lower than other algorithms, and the mean absolute error ranged from a high of 0.56 to a low of 0.54, lower than all other algorithms except the support vector machine algorithm.The average absolute error of the deep neural network resource representation algorithm ranged from a high of 1.46 to a low of 1.45, lower than all other algorithms except the support vector machine algorithm, and the average squared error ranged from a high of 3.43 to a low of 3.24, better than all other algorithms.In conclusion, the model constructed by the study has a good application effect in recommending educational resources, and has a certain promoting effect on the development of online education.
Software Cost Estimation using Stacked Ensemble Classifier and Feature Selection

Predicting the cost of the development effort is essential for successful projects.This helps software project managers to allocate resources, and determine budget or delivery date.This paper evaluates a set of machine learning algorithms and techniques in predicting the development cost of software projects.A feature selection algorithm is utilized to enhance the accuracy of the prediction process.A set of evaluations are presented based on basic classifiers and stacked ensemble classifiers with and without the feature selection approach.The evaluation study uses a dataset from 76 university students' software projects.Results show that using a stacked ensemble classifier and feature selection technique can increase the accuracy of software cost prediction models.
Proof of Spacetime as a Defensive Technique Against Model Extraction Attacks

When providing a service that utilizes a machine learning model, the countermeasures against cyber-attacks are required.The model extraction attack is one of the attacks, in which an attacker attempts to replicate the model by obtaining a large number of input-output pairs.While a defense using Proof of Work has already been proposed, an attacker can still conduct model extraction attacks by increasing their computational power.Moreover, this approach leads to unnecessary energy consumption and might not be environmentally friendly.In this paper, the defense method using Proof of Spacetime instead of Proof of Work is proposed to reduce the energy consumption.The Proof of Spacetime is a method to impose spatial and temporal costs on the users of the service.While the Proof of Work makes a user to calculate until permission is granted, the Proof of Spacetime makes a user to keep a result of calculation, so the energy consumption is reduced.Through computer simulations, it was found that systems with Proof of Spacetime, compared to those with Proof of Work, impose 0.79 times the power consumption and 1.07 times the temporal cost on the attackers, while 0.73 times and 0.64 times on the non-attackers.Therefore, the system with Proof of Spacetime can prevent model extraction attacks with lower energy consumption.
Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification

Precision, Recall, and F1-score are metrics that are often used to evaluate model performance.Precision and Recall are very important to consider when the data is balanced, but in the case of unbalanced data the F1-score is the most important metric.To find out the importance of these metrics, a comparative analysis is needed in order to determine which metric is appropriate for the data being analyzed.This study aims to perform a comparative analysis of various evaluation metrics on unbalanced data in multi-class text classification.This study uses an unbalanced multi-class text dataset including: association, negative, cause of disease, and treatment of disease.This study involves five classifiers as algorithm-level approach, namely: Multinomial Naive Bayes, K-Nearest Neigbors, Support Vector Machine, Random Forest, and Long Short-Term Memory.Meanwhile, data-level approach, this study involves under sampling, over sampling, and synthetic minority oversampling technique.Several evaluation metrics used to evaluate model performance include Precision, Recall, and F1-score.The results show that the most suitable evaluation metric for use on unbalanced data depends on the purpose of use and the desired priority, including the classifier that is suitable for handling multi-class assignments on unbalanced data.The results of this study can assist practitioners in selecting evaluation metrics that are in accordance with the goals and application needs of multi-class text classification.
Effect of ethical leadership and ethical climate on corporate social responsibility and firm performance: a mediated model

Leadership has a decisive role in the success of organisations irrespective of size and type, and ethical behaviour of the leader and ethical the climate in the organisation play a key role. This research investigates the effect of ethical leadership and ethical climate on corporate social responsibility and firm performance in the telecom sector. A closed-ended questionnaire was administered through an online survey to collect data from 415 telecom employees across the country. The proposed relationship was tested using partial least square structural equation modelling (PLS-SEM) approaches (i.e., PLS algorithm and bootstrapping). The findings of study results showed that there is a positive and significant effect of ethical leadership and ethical climate on firm performance and corporate social responsibility. Corporate social responsibility also had a positive and significant influence on firm performance. In addition, corporate social responsibility indirectly influences the link between ethical climate and firm performance. This study explained 50.3 variances in corporate social responsibility and 44.8% in firm performance.