Reviewerly/exHarmony huggingface dataset:
ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTION OF MECHANICAL PROPERTIES OF STEEL

The prediction of mechanical properties is very important task in the quality control of steel, which is one of the world's most important engineering and construction materials. To the main factors affecting the mechanical properties of steel belongs thermomechanical treatment and the chemical composition. The chemical composition may vary for a special high-strength low-alloy steel to meet certain mechanical property requirements. This paper presents a machine learning approach for prediction of tensile strength of structural steels. A single hidden layer Artificial Neural Network (ANN) model was developed and validated based on industrial data set. The ANN model inputs were parameters of interatomic interaction, characterizing the chemical and structural state of the melt. The modeling results showed that the ANN model might be an attractive and useful tool for forecasting the mechanical properties of steel. It offers also a potential for the further derivative free optimization of chemical composition for special quality criteria.
The effect of Mentimeter on students’ Writing achievement in online learning

This study examines the use of Mentimeter on students’ writing descriptive text in online learning at Hasyim Asy'ari University Tebuireng Jombang. This study used a quantitative approach with a quasi-experimental design. The subjects in this study were Management class students which consists of twenty-two students as the experimental class and Elementary School Teacher Program class students which consist of seventeen students as the control class in English for Specific Purposes course. The research instrument is an essay test, which will be given before (Pre Test) the treatment to determine students’ prior knowledge and after (Post Test) treatment to determine the effect of using Mentimeter. After analyzing the data using Mann-Whitney U Test, the results showed that there was a significant difference in the mean rank of 23.59 on the use of Mentimeter and 13.35 on the use of power point. The statistical test obtained Asymp.Sig. (2-tailed) of 0.022, it is lower than the 0.05 level of significance. It means that the use of Mentimeter can influence students’ writing ability in online learning.DOI: 10.26905/enjourme.v7i1.7900
The State and Trends of Crime in Modern Russia

This chapter presents a general description of modern Russia and its police. The activity of the police on registration of crimes and the main types and causes of latency of crime are considered. The features of the postmodern society influencing the state and trends of crime are shown. The state and trends of crime in Russia and some of its forms (organized crime, corruption) are described.
TEXT SUMMARIZATION USING MACHINE LEARNING AND DEEP NEURAL NETWORK

The creation of summaries and their evaluation have grown in popularity over time. The two primary techniques for creating summaries from a given text corpus are extractive text summarization and abstractive text summarization. To create abstractive and extractive summaries, a variety of techniques and methodologies are available. Using machine learning and deep neural networks, an automatic extractive and abstractive text summarization technique is developed. To produce a summary from a given text, the Extractive model utilizes (Spacy, TF-IDF Vectorizer, and Latent semantic Analysis algorithm (LSA)) and the Abstract model employs (Encoder-decoder RNN with LSTM units and attention mechanism). The goal of the model is to demonstrate the benefits of applying the Extractive and Abstractive approach to text summarization in practical settings.
HIGH RESOLUTION 3D HUMAN DIGITIZATION

Though noticeable development in the recent few years, the availability of 3D subjects is still shadowed by that of its 2D equivalent, many 2D-to-3D images, and video conversion methods have been exposed. Techniques that have human operators are mostly fruitful but also time-consuming and expensive, methods that classically make use of a deterministic 3D model, have not yet attained an equal range of excellence for they depend on hypotheses that are frequently disrupted in practical. In this paper, we put forth a new flair of methods that are based on a profoundly unalike line of attack to learn the 2D-to-3D conversion from paradigms. We demonstrate both the value and the computational efficiency of our methods on abundant 2Dimagesand discuss their limitations and remunerations.
VISUAL QUESTIONING AND ANSWERING

In general, a VQA system is an algorithm that takes a picture and natural language query about the image as input and produces natural language response as output. The nature of a multi-discipline research problem necessitates this. This is a diagnostic dataset that assesses a variety of visual reason skills. VQA features few biases and through annotations defining the type of reasoning required for each question. The dataset is used to examine a number of recent visual reasoning systems, revealing new information about their capabilities and limitations.
VEMANA INFORMATION BOT [VIBO]

Generally websites contain large volume of data or information. A person visiting the website for the first few times will find it difficult to search for the required information. To reduce the difficulty and time of a person by providing quick access to the needed information, user friendly chatbots were developed. Similarly, in order to make our college website more interactive and to provide ease of access to the information VIBO is developed which is a text-based chatbot.
3D BRAIN TUMOR DETECTION

The efficacy of self-supervised learning techniques in a variety of application fields has helped them become more popular recently. In this work, we give 3D proxy tasks for five various self-supervised learning approaches using these principles. By enabling neural network feature learning from unlabelled 3D images, we hope to minimise the cost of expert annotation. The techniques include Relative 3D Patch Location, 3D Exemplar Networks, 3D Rotation Prediction, 3D Jigsaw Puzzles, and 3D Contrastive Predictive Coding. Our findings show that pretraining models using our 3D tasks generates more rich semantic representations and enables completing downstream tasks more accurately and quickly compared to training the models from scratch and pretraining them on 2D slices.
KRJS SCHOLARSHIP PORTAL

A scholarship can assist you in achieving your higher education goals while reducing your stress levels. Many families may not be able to send their children to higher education institutions, but thanks to scholarships, every student who meets the precise qualifying conditions can pursue higher education. The awards given by scholarship are renewable. The project helps in providing scholarship for only eligible students. Module is completely based on train &amp; test, EDA for regression, missing value treatment, feature selection &amp; scaling, prediction using model. The way to support students to pursue higher study is the Scholarships. Rejecting the deserved students can destroy student’s carrier. So, it is important select deserved candidates for scholarship. The Human based selection is biased, difficult, resource and time consuming. So, we want to automate the task so that we can produce reliable, unbiased, fast results.
SECURED REMOTE DATA AUDITING IN DISTRIBUTED CLOUD ENVIRONMENT USING ELGAMAL CRYPTOSYSTEM

Managing a large amount of data in cloud is current trend and the challenging task is to secure it against unauthorized manipulations.Cloud computing provides a platform to access data remotely for businesses and individual needs.A remote data integrity check is suggested to ensure the integrity of the data stored in the cloud.The file stored in cloud may contain sensitive information.The private data should not be imparted to unauthorized user when the cloud document is discharged.By scrambling the whole mutual document, Privacy is maintained for confidential information that need to be secured and the personal information can't be viewed or modified by unauthorized person.A remote data integrity checking scheme is proposed that implements information trade with private data that is covered up in the record.A sterilization program is utilized to clean up the data blocks that correspond to the confidential information of document and to change over the signature of the information hinders into legitimate signature for the cleaned record.Subsequently, the plan permits the record put away in the cloud to be shared and utilized by others, provided the secret data is covered up, while the remote data integrity audit can continue to run efficiently.
Hate Language Produced by Indonesian Figures in Social Media: From Philosophical Perspectives

This study aims to: (1) describe the linguistic forms typically used in hate speech; (2) reveal the interpretation and intention of hate speech, and (3) propose its pedagogical implication. This was descriptive qualitative research with content and a philosophic analysis approach. The data were excerpts containing hate language produced by figures collected from YouTube videos. The hate speech was linguistically manifested through the use of swear words, mental abnormality terms, the animal metaphor, insults which refer to characters, Arabic terms with negative meaning, and nicknames that call out physical characteristics. According to the interpretation in the Indonesian context, the speakers use hate language to vent negative feelings, insult, condemn, accuse, show disagreement, show dissatisfaction, wish bad luck, and threaten. The hate language appears to be an Indonesian phenomenon of language use, however, its forms and functions are determined by the contextual or philosophic background of the users. Some hate language in this paper was found to mask the speakers’ defeat and incapability against the target of hate.
Debiased Scene Graph Generation for Dual Imbalance Learning

Scene graph generation (SGG) is one of the hottest topics in computer vision and has attracted many interests since it provides rich semantic information between objects. In practice, the SGG datasets are often dual imbalanced, presented as a large number of backgrounds and rarely few foregrounds, and highly skewed foreground relationships categories (i.e., the long-tailed distribution). How to tackle this dual imbalanced problem is crucial but rarely studied in literature. Existing methods only consider the long-tailed distribution of foregrounds classes and ignore the background-foreground imbalance in SGG, which results in a biased model and prevents it from being applied in the downstream tasks widely. To reduce its side effect and make the contributions of different categories equally, we propose a novel debiased SGG method (named DSDI) by incorporating biased resistance loss and causal intervention tree. We first deeply analyze the potential causes of dual imbalanced problem in SGG. Then, to learn more discriminate representation of the foreground by expanding the foreground features space, the biased resistance loss decouples the background classification from foreground relationship recognition. Meanwhile, a causal graph of content and context is designed to remove the context bias and learn unbiased relationship features via casual intervention tree. Extensive experimental results on two extremely imbalanced datasets: VG150 and VrR-VG, demonstrate our DSDI outperforms other state-of-the-art methods. All our models will be available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhouhao0515/unbiasedSGG-DSDI</uri> .
Healthcare System 4.0 Driven by Quantum Computing and Its Use Cases

COVID pandemic has created much havoc worldwide by putting human lives at stake. Vaccination and COVID-appropriate behaviour are the only options available to manage it. The present healthcare industry is not able to meet this requirement due to its traditional computing base and conduction of human trials. A healthcare system based on quantum computing (QC) is ideal. The main contribution of this work is to present the objectives drawn from the usage of the underlying features such as qubits, superposition, entanglement, and uncertainty of QC in the area of healthcare. First, QC provides high and speedy computing resources. Second, they replace clinical trials with silico trials. Third, genome sequencing can be done precisely and results be obtained quickly. Finally, it provides for predictive healthcare through which medicines be made for patients at an early stage of their disease, process automation, and high security of medical data. Observations are speeding up any development process, avoiding risks of human trials, and checking efficacy of the vaccine by allowing trails to be conducted many times. Besides, medical practitioners can perform patient-tailored diagnosis and treatment. In the end, the QC-based healthcare system gets update to version 4.0 that can manage future situations better.
Risk Management by Grid Computing in AWS

Financial services businesses use High-performance computing (HPC) infrastructure grids to compute risk, value portfolios, and give data to internal and external control units of organization. Since, the infrastructure&#x2019;s size, expense, and complexity are becoming increasingly difficult to manage; this paper discusses how Amazon Web Services (AWS) offers a variety of services that help clients to go beyond their present abilities by providing outcomes faster and for less money than on-premises resources. This research work discusses the challenges faced due to on-premises grids and to deal with these issues financial organizations are increasingly turning to AWS to meet their grid computing requirements, and are reaping a variety of benefits, which includes speeding up of processing, total costs reduction, and better accessibility.
Sentiment Analysis on Tweets with Deep Learning and Natural Language Processing

Today&#x2019;s social media users are very large, where everyone expresses opinions, comments, criticisms, and so on. The data provides valuable information to be able to help people or groups in opinion evaluation. The enormous quantity of information is impractical for humans to interpret and evaluate manually. The Sentiment Analysis (SA) is a task that has become very important for the scientific and business community. Thus, SA can be used to diagnose a population regarding specific issues. There are many machine learning techniques, Deep Learning (DL) is one of them that has a deeper architecture than other ML techniques in solving prediction and classification problems. Using DL Models and NLP, this work tries to categorise content on social media into positive, negative, and neutral groups as sentiments. While NLP is being used to extract features, which enhances accuracy and performance, other DL models for classification, such as CNN, RNN, and LSTM models, can synthesize or create acceptable sentiments with a voluminous data; however, they take longer to train.
Scientific use of archival documets of Moscow Spiritual Consistory under the departmental restrictions of the Synodal period

The article deals with a little-known and hardly imaginable in the context of pre-revolutionary clerical administration scientific direction of the Moscow Ecclesiastical Consistory Archive. Based on archival material and works of ecclesiastical Moscovites of the 19th - early 20th centuries the principles of the introduction of diocesan materials into science, as well as the parameters of departmental influence within the structures of church institutions on the example of the Moscow Ecclesiastical Consistory Archive are revealed. By the second quarter of the XIX century, the Consistory had acquired a large collection of diocesan documents with undisclosed information potential. Apart from departmental rules, the interest in the archive depended on psychological factors influencing research motivation. The establishment of science in the pre-revolutionary consistory archives was helped by a special committee which was set up in the first half of the 19th century to work with the archive complex. Its employees had to bear the main burden of searching for the necessary documents. The development of archives by the scientists was nonlinear: the periods of growth and decline alternated. Attention to the archive's documents was dulled by the change in OLDP's educational methodology, which replaced historical reconstructions with visual preaching. Nevertheless, the Archive's processing mechanisms were preserved and only the First World War interrupted further development of the information capacity of the Consistory archives. Thanks to the combined efforts of the clergy employed in the Archive, as well as the improved quality of the documents preserved, the Archive of the Diocesan Institution is gradually becoming a breeding ground for scholars studying the history of the Russian Church.
Measuring the extent of the trend towards organizational improvisation: a survey study in the power stations of the northern region

The study aimed to measure the extent of the trend towards organizational improvisation in a number of power stations in the northern region, where the importance of improvisation increased with the organizations orientation to work in turbulent and complex environments, which includes decision-making in conditions of high uncertainty. Hence, the problem of the study emerged from raising a major question: To what extent are the administrations oriented towards organizational improvisation in the power stations of the northern region, the study sample. For the purpose of achieving the goal of the study, the study adopted the descriptive and analytical approach, and power stations (Qayyarah, Al-Mansour, Al Sharqiya, Baiji First, Baiji Second) were chosen as fields for the study. In the study, the researchers adopted the questionnaire as a main tool for that. The study reached several conclusions, the most important of which is the positive orientation of the surveyed stations’ administrations towards the practices of organizational improvisation in emergency conditions and crises that occur in the stations. Suggested developing an effective strategy to focus on organizational improvisation, and invest in individuals who have experience and directions towards discovering and exploiting opportunities.
The Role of Knowledge Capital in Promoting Organizational Citizenship Behavior: A Study of a Number of Iraqi Public Universities

The main objective of the research is to know the role of knowledge capital in enhancing organizational citizenship behavior. The research relied on the descriptive analytical approach in presenting and discussing data, as the method works to associate the description of the case with its analysis and conclusion of its own. To the purpose of collecting the data required to solve the research problem, the questionnaire that it was distributed to a sample of (187) administrative leaders at Tikrit University, Anbar University, and Fallujah University. By using the statistical analysis program (SPSS V.25), the data of the practical side was analyzed, which included analyzing their answers and opinions, and when testing the hypotheses, a set of conclusions was reached. The most important of them: that knowledge capital is the basic building block in building and strengthening citizenship behavior, especially structural capital, while human capital and relational capital came to a medium degree. Moreover, the results indicate that the level of practicing organizational citizenship behavior came to a high degree in universities the research sample.
Multi-Lingual Language Variety Identification using Conventional Deep Learning and Transfer Learning Approaches

Language variety identification tends to identify lexical and semantic variations in different varieties of a single language. Language variety identification helps build the linguistic profile of an author from written text which can be used for cyber forensics and marketing purposes. Investigating previous efforts for language variety identification, we hardly find any study that experiments with transfer learning approaches and/or performs a thorough comparison of different deep learning approaches on a range of benchmark datasets. So, to bridge this gap, we propose transfer learning approaches for language variety identification tasks and perform an extensive comparison of them with deep learning approaches on multiple varieties of four widely spoken languages, i.e., Arabic, English, Portuguese, and Spanish. This research has treated this task as a binary classification problem (Portuguese) and multi-class classification problem (Arabic, English, and Spanish). We applied two transfer learning Bidirectional Encoder Representations from Transformers (BERT), Universal Language Model Fine-tuning (ULMFiT), three deep learning-Convolutional Neural Networks (CNN), Bidirectional Long Short Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and an ensemble approach for identifying different varieties. A thorough comparison between the approaches suggests that the transfer learning based ULMFiT model outperforms all other approaches and produces the best accuracy results for binary and multi-class language variety identification tasks.
An examination of convicted fraud cases in Ghana between 1997 and 2016: Studies from a Gender Perspective.

It is often argued that men are more susceptible to fraud and corruption compare to their female counterparts.This argument is further supported by recent evidence from Anas Areme yaw investigative report on Judiciary fraud and Corruption of which a female judge refused bribe and threatened to arrest the investigator.This is further reflected in the Rwandan governance model which remains female dominant with its correspondence clean record of fraud in the Africa continent comparatively.There is also argument among politicians regarding perception of fraud within a particular regime.The paper examines data from the Ghana Prisons Service to determine who is the most fraudulent or corrupt is it the male or the female.Hence, the objective sought to examine data on the convicted cases of fraud to ascertain whether males or females are more convicted for committing fraud.Again, the paper assessed from the provided data to determine whether or not a particular government regime was fraud-free.Data was collected using the quantitative method.Questionnaires were specifically distributed to the officials of the Ghana Prison at the Prisons Headquarters Accra.The result showed females were less convicted of fraud as compared to their male counterpart.Furthermore, no political era with the selected time frame was found to have recorded fraud free convicts or significantly low fraud convicts in Ghana.It is therefore recommended that value based education and ethical training should continue to be strengthened in school especially at the formative ages.Female should be encouraged to apply for top positions in leading anticorruptions institutions and delicate positions that requires a lot of transparency and self-disciplined in accountability and honesty.It is further recommended that political parties should avoid propaganda in the fight against fraud and take genuine steps to make fraud and corruption very costly by disciplining offenders to serve as deterrent to others.
A System for Mind Map Generation from Arabic Text Using Machine Learning

We proposed a system to convert Arabic text into a Mind Map. We use natural language processing, CAMeL tools, text classification, text similarity, Arabic ontology and BabelNET. We extracted the main titles of the text and the sub-headings, the main topics and subtopics, linked them together, and extracted the main Mind Map subject. We suggest a mechanism based on deep learning to link titles and topics with appropriate images. Then, we are generating a Mind Map to modify on the open-source program FreeMind. We supported converting the Arabic Mind Map to other languages. The proposed system gets a good accuracy when comparing it with the real manual Mind Maps work. We covered most of the main and subtitles.