Reviewerly/exHarmony huggingface dataset:
QG-SKI: Question Classification and MCQ Question Generation Using Sequential Knowledge Induction

E-Learning has emerged as the most effective way of getting information in a range of sectors in the contemporary age. The utilisation of electronic content to provide education and development is referred to as e-learning. While the broadening internet has a plethora of e-learning tools, knowledge acquisition is not just an aspect that adds to an individual's enrichment. Assessment and evaluation are crucial parts of every learning system. Due to more complex assessments and quicker inspection, multiple choice questions are becoming extremely prevalent in current evaluations. However, establishing a diversified pool of MCQs relevant to a certain subject matter presents a hurdle. Manually creating high-quality MCQ exams is a time-consuming and arduous procedure that demands skill. As a result, research has concentrated on the automated construction of well-structured MCQ-based tests. This paper presents a paradigm using natural language processing based on semantic similarity and dynamic ontology. The proposed QG-SKI model uses LOD Cloud and Wiki Data to generate Ontologies dynamically and Knowledge reservoir is performed. The dataset is analysed using TF-IDF algorithm and the Semantic Similarity and Semantic Dissimilarity are computed using Shannon’s entropy, Jaccard Similarity and Normalised Google Distance. These algorithms are executed for a multitude of degrees and levels to generate a similar of similar instances. The suggested model has a 98.15% accuracy and outperforms previous baseline models by dampening resilience.
Topic Modeling Approaches—A Comparative Analysis

Valuable information from a corpus for a specific purpose can be obtained by finding, extracting, and processing the text through text mining. A corpus is a group of documents and the documents could be anything from newspaper articles, tweets, or any kind of data that needs to study. For processing and understanding the structure of a corpus, a technique in text mining is Natural Language Processing (NLP). The study of a corpus in different fields like bioinformatics, software engineering, sentiment analysis, Education, and Linguistics (scientific research) is a challenging task as it contains a vast amount of data. Thus for latent data identification, establishing connections between data and text documents needs topic modeling. Evolutions of topic models are analyzed in this paper from 1990 to present. To better understand the topic modeling concept, a detailed evaluation of techniques is discussed in detail. In this study, we looked into highly scientific articles from 2010 to 2022 based on methods used in different areas to discover current trends, research development, and the intellectual structure of topic modeling.
Binary Classification with Genetic Algorithms. A Study on Fitness Functions

In this article, we propose a new fitness function that can be used in real-value binary classification problems. The fitness function takes into account the iteration step, controlling with it the importance of some elements of the function. The designed genetic algorithm is compared with two other variants of genetic algorithms, and with other state-of-the-art methods. Numerical experiments conducted both on synthetic and real-world problems show the effectiveness of the proposed method.
A Bee Colony Optimization Algorithm to Tuning Membership Functions in a Type-1 Fuzzy Logic System Applied in the Stabilization of a D.C. Motor Speed Controller

In this research a Bee Colony Optimization algorithm (BCO) for stabilization of a D.C Motor Speed Controller is presented. The first idea of the BCO is to find of the optimal design of the Membership Functions (MFs) in the Type-1 Fuzzy Logic System (T1FLS). BCO algorithm shows excellent results when real problems are analyzed in the Fuzzy Logic Controller (FLC). Some types of indices performance implemented in the field of the control are used. With the goal of verifying the efficiency of the BCO a comparative with other bio-inspired algorithms for the stabilization of the case study.
It’s All Connected: Detecting Phishing Transaction Records on Ethereum Using Link Prediction

Digital currencies are increasingly being used on platforms for virtual transactions, such as Ethereum, owing to new financial innovations. As these platforms are anonymous and easy to use, they are perfect places for phishing scams to grow. Unlike traditional phishing detection approaches that aim to distinguish phishing websites and emails using their HTML content and URLs, phishing attacks on Ethereum focus on detecting phishing addresses by analyzing the transaction relationships on the virtual transaction platform. This study proposes a link prediction framework for detecting phishing transactions on the Ethereum platform using 12 local network-based features extracted from the Ether receiving and initiating addresses. The framework was trained and tested on over 280,000 verified phishing and legitimate transaction records. Experimental results indicate that the proposed framework with a LightGBM classifier provides a high recall of 89% and an AUC score of 93%.
Comparative Study of Compact Descriptors for Vector Map Protection

The paper is devoted to the study of compact vector map descriptors to be used as a zero watermark for cartographic data protection, namely, copyright protection and protection against unauthorized tampering. The efficiency of the investigated descriptors in relation to these problems is determined by the resistance of their values to map transformations (adding, deleting vertices and objects, map rotation, etc.). All the descriptors are based on the use of the Ramer-Douglas-Peucker algorithm that extracts the significant part of the polygonal object determining its shape. The conducted study has revealed the preferred descriptor for solving the copyright protection problem, as well as several combinations of other descriptors identifying certain types of tampering. In addition, a modification of the Ramer-Douglas-Peucker algorithm, which is more efficient than the basic algorithm, is proposed.
SA-K2PC: Optimizing K2PC with Simulated Annealing for Bayesian Structure Learning

Bayesian Network is an efficient theoretical model to deal with uncertainty and knowledge representation. Its development process is divided into two stages: (1) learning the structure and (2) learning the parameters. In fact, defining the optimal structure is a big difficulty that has been extensively investigated and still needs improvements. We present, in this paper, an extension of the existing K2PC algorithm with Simulated Annealing optimization for node ordering. Experimentations on well-known networks show that our proposal can extract the original's closest topology efficiently and reliably.
Elections in Twitter Era: Predicting Winning Party in US Elections 2020 Using Deep Learning

Twitter has become one of the leading platforms for people to express their political opinions during elections. Understanding public sentiment by on-ground election polling is expensive and time-consuming. Recently, tweets have gained popularity for analyzing public sentiment toward political parties. However, a large number of tweets have to be analyzed to simulate real-world elections effectively. Most previous works on Twitter sentiment analysis have trained on small tweet datasets, and most have failed to consider negative sentiment tweets. Considering only the positive tweets does not produce the actual on-ground sentiment. In this study, we have analyzed a large corpus of positive, negative, and neutral sentiment tweets about political parties in the US elections 2020 and predicted the election outcome. We constructed three distinct classification models using Bidirectional LSTM, GRU, and Hybrid CNN-LSTM to classify tweets into positive, negative, or neutral sentiments. Our results show that Bidirectional LSTM achieved an accuracy of $$95.99\%$$ , outperforming other deep learning approaches and related works. A custom net-score metric is proposed that considers both positive and negative sentiments. The Democratic Party outperformed the Republican Party with a higher net score, indicating that the Democratic Party is predicted to win. VADER algorithm was used to find the winning margin, which considered polarity, compound sentiment score, and retweet count. We found that the Democratic Party would lead by a marginal gap of $$+6.250\%$$ , which is close to the actual election results.
Task-Cloud Resource Mapping Heuristic Based on EET Value for Scheduling Tasks in Cloud Environment

The most popular and highly scalable computing technology, cloud computing bases its fees on the amount of resources used. However, due to the increase in user request volume, task scheduling and resource sharing are fetching key needs for active load sharing of a capability between cloud resources. This will improve the overall performance of cloud systems. The above aspects contribute to the development of standard, heuristic, and meta-heuristic algorithms, as well as other task scheduling techniques. The job planning problem is typically solved using heuristic job planning algorithms like Min-Min, MET, Max-Min and MCT. This research proposes a novel hybrid cloud computing method based on Min-Min and Max-Min experimental procedures. When using the Cloudsim simulator, this algorithm has been evaluated with a number of optimization settings, including makespan, average source usage, load sharing, typical waiting time, and in-parallel execution of short-duration activities and long-duration tasks. The proposed algorithm TCRM_EET experimental results computed and performance analyzed are carried out based on the analytical benchmark.The findings demonstrate that the proposed method outperforms Min-Min and Max-Min for such values.
Fuzzy Kernel Weighted Random Projection Ensemble Clustering For High Dimensional Data

A clustering ensemble seeks to treat a consensus function by taking multiple base clustering. There are generally two main limitations: (1) High-dimensional data pose a challenge to current ensemble clustering techniques. (2) Generate all the fuzzy base clusterings regardless of their uncertainty and reliability, which makes them susceptible to low-quality. To address this issue, we developed a multi-kernel local weighted fuzzy random projection ensemble clustering. In particular, we develop a fuzzy cluster ensemble method based on a hybrid random projection with multiple KFCM, which can cope with high-dimensional data while guaranteeing the diversity of base clusterings. Based on the fuzzy similarity matrix derived from these local weight kernel fuzzy membership and random projection, an ensemble of diversified base clusterings can be constructed. Then, a fuzzy entropy-based on cluster reliability is used to estimate the fuzzy cluster-wise weighted diversity. Finally The fuzzy similarity matrix corresponding to each base clustering is weighted twice by the block coordinate descent method to find the best clustering result. Three types of consensus clustering are proposed. The experiment results on high-dimensional data demonstrate the efficiency of our method compared to state-of-the-art methods.
Fish School Search Algorithm for Constrained Optimization

In this work we investigate the effectiveness of the application of a niching metaheuristic of the Fish School Search family in solving constrained optimization problems. Sub-swarms are used to allow the achievement of many feasible regions to be exploited in terms of fitness function. The niching approach employed was wFSS, a version of the Fish School Search algorithm devised specifically to deal with multi-modal search spaces. A technique referred as rwFSS was conceived. Tests were performed in seven problems from CEC 2020 and a comparison with other approaches was carried out. Results show that rwFSS can handle some reasonable constrained search spaces and achieve results comparable to two of the CEC 2020 top ranked algorithms on constrained optimization. However, we also observed that the local search operator of wFSS and inherited by rwFSS makes it difficult to find and keep the individuals inside feasible regions when the search space presents a large number of equality constraints.
Attendance Automation System with Facial Authorization and Body Temperature Using Cloud Based Viola-Jones Face Recognition Algorithm

Face recognition works all over the world from various perspectives. In the field of attendance systems, many methodologies have faced various drawbacks. In the headset of attendance monitoring, the attendance is registered immediately by scanning the face and compares with pattern in the database and marking the student attendance along with the detection of their body temperature automatically by using object vision algorithms. In the proposed system, facial feature recognition and detection is performed based on the Viola-Jones face detection algorithm. The system is for school or university students with any large strength, and even a single student with at least 5–6 images with different angle of their face to be stored. Hence, these automation systems need a large amount of storage space. Hence, cloud storage server is also used to store any number of images. The student's attendance is recorded by the camera installed in the class entrance. Students have to record their faces one by one before entering the classes and the camera can create snapshots for a particular set of defined timings. Then, it detects faces in snapshot images, compares them with the cloud database and then attendance is marked. The experimental results show that the proposed Viola-Jones face detection algorithm is better than many existing algorithms.
A Gaussian Mixture Clustering Approach Based on Extremal Optimization

Many machine-learning approaches rely on maximizing the log-likelihood for parameter estimation. While for large sets of data this usually yields reasonable results, for smaller ones, this approach raises challenges related to the existence or number of optima, as well as to the appropriateness of the chosen model. In this paper, an Extremal optimization approach is proposed as an alternative to expectation maximization for the Gaussian Mixture Model, in an attempt to find parameters that better model the data than those provided by the direct maximization of the log-likelihood function. The behavior of the approach is illustrated by using numerical experiments on a set of synthetic and real-world data.
PMFRO: Personalized Men’s Fashion Recommendation Using Dynamic Ontological Models

There is a thriving need for an expert intelligent system for recommending fashion especially focusing on men’s fashion. As it is an area which is neglected both in terms of fashion and modelling intelligent systems. So, in this paper the PMFRO framework for men’s recommendation has been put forth which indicates the semantic similarity schemes with auxiliary knowledge and machine intelligence in a very systematic manner. The framework intelligently creates mapping of the preprocessed preferences and the user records and clicks with that of the items in the profile. So, this model aggregates community user profiles and also maps the men’s fashion ontology using strategic semantic similarity schemes. Semantic similarity is evaluated using Lesk similarity and NPMI measures at several stages and instances with differential set thresholds and the dataset is classified using the feature control, machine learning bagging classifier which is an ensemble model in order to recommend the men’s fashion. The PMFRO framework is an intelligent amalgamation and integration of auxiliary knowledge, strategic knowledge, user profile preferences as well as machine learning paradigms and semantic similarity models for recommending men’s fashion and overall precision of 94.68% and FDR of 0.06 was achieved using the PMFRO model.
Movie Sentiment Analysis Based on Machine Learning Algorithms: Comparative Study

The movie market products many movies each year. This makes hard to select the appropriate film to watch. The viewer, generally, has to read the feedback of the previous ones to make decision. He finds a problem because of the massive quantity of feedbacks. Hence, it is necessary to use Machine learning algorithms because they automatically analyze and classify the collected movies feedbacks into negative and positive reviews. Different works in the literature use machine learning for movies’ sentiment analysis. They select some algorithms for the evaluation. And because it is always complicated to select the appropriate machine learning algorithms according to the requirements that get poor accuracy and performance, we propose, in this work, the implementation of 35 different algorithms for a good comparison. They are evaluated using the following metrics: Kappa and Accuracy.
Sentiment Analysis of Real-Time Health Care Twitter Data Using Hadoop Ecosystem

The term “sentimental analysis” refers to classifying and categorizing user opinions depending on how they express their feelings about specific pieces of data. Trending concepts are gaining popularity, and Twitter is the most utilized to discover people’s thoughts. This approach uses Hive ware, Sqoop, Hadoop, and Flume to capture real-time health information from Twitter via the system setup. Using the Flume agent, the keyword file on the Hadoop cluster obtains similar data, causing the resulting data to synch with HDFS (Hadoop Distributed File Systems). As one of the most well-known social media platforms, Twitter receives an enormous volume of tweets each day. There are various ways that this data can be analyzed in multiple ways, including for business or government purposes. Twitter’s massive volume of data makes it difficult to store and process this information. Hadoop is a framework for dealing with big data, and it features a family of tools that may be used to process various types of data. The health care real-time tweets are used in this research. This work has been using Apache Flume to collect real-time tweets. To address further the proposed system, it is designed to perform sentimental analysis from the tweets, conversations, feelings, and activities on social media and determine the cognition and behavioral state of everyone. In the proposed work for the real-valued and current patterns of health tweets, sentimental analysis is used in real-time. Hadoop ecosystem tools like Hive and Pig are used for execution time and real-time tweet analysis. In real-time work, it is calculated that Pig is more efficient than Hive based on the trial results, as Pig takes less time to execute than Hive.
Mobile Image Compression Using Singular Value Decomposition and Deep Learning

Mobile images generate a high amount of data therefore, efficient image compression techniques are required, which can compress the image while maintaining image quality. This paper proposes a new lossy image compression technique that maintains the psychovisual redundancy using Singular Value Decomposition (SVD) and Residual Neural Networks (ResNet50). Images are compressed by SVD using the rank K of the image. However, it is difficult to predict the correct value of K to compress the image as much as possible while maintaining the quality of the image. First, a relation between the energy of the image and the number of focal points in an image is derived, using which 1500 images are compressed using SVD while maintaining psychovisual redundancy. This data is then used to train ResNet-50 to predict the rank values. The proposed method obtained a compression ratio of 41.9%, with 86.35% accuracy of rank prediction for the entire dataset. Therefore, the proposed method can predict the correct value of rank K, and hence automate the compression process while maintaining the psychovisual redundancy of the image.
GAN Based Restyling of Arabic Handwritten Historical Documents

Arabic handwritten documents consist of unstructured heterogeneous content. The information these documents can provide is very valuable both historically and educationally. However, content extraction from historical documents by Optical Character Recognition remains an open problem given the poor quality in writing. Furthermore, these documents most often show various forms of deterioration (e.g., watermarks). In this paper, we propose a Cycle GAN-based approach to generate a document with a readable font style from a historical Arabic handwritten document using a collection of unlabeled images. We used Arabic OCR for content extraction.
A Systematic Literature Review on Security Aspects of Virtualization

Cloud computing is an emerging technology where organizations can have the flexibility of their infrastructure and store and retrieve data from cloud storage. The main advantages of cloud computing are speed, cost reduction, and scalability. Cloud computing is mainly developed on the virtualization of resources with reusability capabilities. In virtualization, computer systems are deployed virtually as software and hardware resources are shared between virtual machines. It allows multiple operating systems and software applications to run on a single server. Virtualization plays an essential role in the cloud environment. Though cloud computing improves the performance and quality of work for organizations, it has security, privacy, and trust-related threats. In this paper, we have surveyed the security aspects of virtualization. We have discussed cloud computing and virtualization, followed by a detailed analysis of virtualization security. We have also proposed a multi-agent-based model to improve virtualization security.
Selection of Replicas with Predictions of Resources Consumption

The project Continuously Evolving Distributed Ensembles (CEDEs) aims to create a cost-effective environment for distributed training of Machine Learning models. In CEDEs, datasets are broken down into blocks, replicated and distributed through the cluster, so that Machine Learning tasks can take place in parallel. Models are thus a logical construct in CEDEs, made up of multiple base models. In this paper, we address the problem of distributing tasks across the cluster while adhering to the principle of data locality. The presented optimization problem assigns for each block a base model with the objective of minimizing the overall prevision of resources consumption. We present an instance generator and three datasets that will provide a means of comparison while analyzing solution methods to employ in this project. For testing the system architecture, we solved the datasets with an exact method and the computational results validate that to comply with the CEDEs requirements, the project needs for a more stable and less demanding solution method in terms of computational resources.