Reviewerly/exHarmony huggingface dataset:
Data Hiding in 3D Model Based on Surface Properties

Data hiding strategies have recently gained popularity in different fields; Digital watermark technology was developed for hiding copyright information in the image visually or invisibly. Today, 3D model technology has the potential to alter the field because it allows for the production of sophisticated structures and forms that were previously impossible to achieve. In this paper, a new watermarking method for the 3D model is presented. The proposed method is based on the geometrical and topology properties of the 3D model surface to increase the security. The geometrical properties are based on computing the mean curvature for a surface and topology based on the number of edges around each vertex, the vertices that have negative mean curvature and an odd number of edges around the vertex are selected for embedding. Selecting the vertex with negative mean curvature value means the vertex located in the deep region of the surface, so it not noticeable change to human eyes. To evaluate the performance of the proposed algorithm, the PSNR, and CF are used as a measurement to evaluate the visibility and robustness of the 3D watermarked model. The experimental results have shown the proposed algorithm has good imperceptibility where the PSNR reach up to 44.41 and robustness against attack where the CF is one in many cases.
Implementation of Extractive Text Summarization using Word Frequency in Python

The goal of this research is to develop an extractive text summarization program using word frequency in Python.The steps of text summarization process are: preprocessing text, word-tokenization, creating bag of words, calculating word frequency, sentence-tokenization, calculating sentence score, calculating average score, and making summary.The developed program was examined on an experimental text from Wikipedia.The program performed the steps of text summarization and provided the required summary.
Frequency Response Analysis for T-S Fuzzy Systems

Frequency response is an important physical quantity in audio systems for minimizing audible distortion and in control systems for assessing system relative stability.For linear time-invariant systems (LTI systems), frequency response has one-to-one relationship to system impulse response.However, the LTI systems-based analysis cannot describe nonlinear frequency response well.T-S fuzzy systems are based on fuzzily blending several linear subsystems and are widely used to describe nonlinear systemsbehavior in various fields.Recently, researcherstried to obtain frequency response of T-S fuzzy systems through the assumption thatthe same fuzzy relationship exists in both time domain and frequency domain.In this paper, we focus on deriving fuzzy frequency response from both basic definitions of frequency response and previously proposed neural-network-based fuzzy blending feature to find out the limitations, the range of frequency response for T-S fuzzy systems.Steady-state system response is additionally discussed and tested with two active magnetic bearing systems.
Performance Analysis of Convolutional Neural Network in Image Classification

Deep learning algorithms is designed to mimic the function of a brain.In deep learning algorithms, one of the most prominent deep neural networks used for image recognition and segmentation tasks is the Convolutional Neural Network (CNN).In this paper, various types of CNN architectures like VGGNet, AlexNet, ResNet, and LeNet-5 are built and the performances are compared using a publicly available dataset (CIFAR-10).Furthermore, multiple performance optimizers: Root Mean Square Propagation (RMSProp), Adaptive moment estimation (Adam), and Adaptive gradient estimation (Adagrad), are applied for this study.The performance of these five CNN architectures with three optimizers is evaluated in terms of accuracy, specificity, and sensitivity.The experimental results showed that the Inception-V3 model with RMSProp as an optimizer achieved the highest validation accuracy of 92.97% with a misclassification rate of 7.03%.
Deep learning models used for accuracy and loss detection on twitter dataset by setting tuning parameters

Hate speech on social media post is running now a days. Social media like YouTube, Twitter, and Facebook etc. are responsible for hated speech. Hated speech spreads through digital media, causing individuals to get confused and adopt prejudiced viewpoints. To limit the negative effects of disinformation on the digital platform, it is critical to detect it. Now a days, lots of digital platforms are available. Hate speech detection in dataset is very difficult. As a result, the Twitter dataset is of the size of 25296 is presented in this work. Many deep learning techniques are applied on Twitter dataset. The Google Colab tool is used to scrape dataset material. Different deep learning approaches are utilized to boost the accuracy of the hated speech dataset. For training and validation accuracy and loss some models are used on Twitter dataset like Bi-directional Long Short Term Memory with Glove, Bi-LSTM, and Embedding from Language Model (Elmo) with deep learning, Convolutional Neural Network (CNN), Long Short Term Memory with Glove and LSTM. The performance of the proposed tweet dataset is evaluated using a variety of deep learning classifiers on text dataset. The planned deep learning techniques produced good results on tweet dataset. LSTM with Glove gave the highest accuracy 0.89 and minimum loss 0.19 on tweet dataset. So when compare our model on same dataset that was used earlier then we get highest accuracy and minimum loss.
Facial Skin Type Prediction Based on Baumann Skin Type Solutions Theory Using Machine Learning

The lack of knowledge of different facial skin types is still a frequent problem in Indonesia. The purpose of this research is to build a facial skin type prediction system using machine learning to classify facial skin types based on Baumann Skin Type Solutionswhich provides information on different skin types and suitable skincare ingredients. The dataset is collected manually by distributing a questionnaire among Indonesian citizens. The prediction models are built using three machine learning methods namely SVM, XGBoost, and 1D-CNN, and compared using 5-fold stratified cross-validation. XGBoostachieved the best performance on facial skin type prediction and optimized through hyperparameter tuning using Bayesian Optimization with a result of 93.5% averaged F1-score.
Privacy Issues in Magnetic Resonance Images

Privacy in magnetic resonance imaging (MRI) plays an important role due to violations occurring in scanning, storing, transferring, analyzing, and sharing. This paper reviews privacy concerns in MRI and especially Brain MRI in terms of datasets, models, platforms, violations, solutions used in privacy and security in the literature, discusses important issues based on risks, techniques, policies, rules, and existing and missing points in MRIs. Even if there have been rules, regulations, policies, and laws available for preserving privacy with the available techniques anonymization, differential privacy, federated learning, pseudonymization, synthetic data generation, privacy-utility or anonymization-utility dilemma is still on novel privacy-enhancing, or preserving techniques are always required to handle sensitive data with care. This paper focuses on these issues with some suggestions, and also discusses these issues for future directions.
A Contrastive Analysis In English and Arabic Noun Vocabularies At SMA Islam Ulun Nuha Medan In Academic Year 2020/2021

This research aims to determine (1) the types of nouns in English and Arabic languages and (2) to find out how does noun in English and Arabic languages applied in descriptive text. This research is descriptive qualitative and was conducted in Mei 2021 at SMA Islam Ulun Nuha Medan. In this study the sample was taken based on three levels of students in class, the lowest, the average and the highest. Each of the level taken three students. Data were collected through analyzing the students’ descriptive text task in English and Arabic languages. Then the data which collected is presented in the form of a description. Based on the finding, it is known that noun in English and Arabic have some types. Types of noun in both languages have differences and similarities in the meaning, specification, gender, number, form and the form of other part of speech. &#x0D; Keyword: Contrastive Analysis, Noun Vocabularies, Arabic Language.
A Study on the Pragmatic Information Items in Korean Education - Focus on ‘-느라고’, ‘-던 데’, ‘-는 바람에’

This study examines the pragmatic information shown in Korean language education for Korean learners and identifies items necessary to describe pragmatic information. To this end, we first examined the aspect of pragmatic information necessary for speech education based on the intermediate textbooks used by five university educational institutions. It was found that pragmatic information in these textbooks was implicitly presented through discourse or illustrations rather than explicitly. To identify grammar items that present pragmatic information, the intermediate grammar items of the selected textbooks were sorted to select grammar items that overlapped with the intermediate grammar items of the International Standard Model for Korean Language Education. Eight grammar items overlapped with the “intermediate grammar items for conversational information technology” suggested by Lee, Jun-ho(2018) and we were thus able to extract and present information for three grammar items: the connecting ending (-느라고), the final Ending (-던 데 2), and one expression (- 는 바람에). Future research should focus on developing structure with grammar items in consideration with pragmatic information.
Legal documentation of the future: evolution and content (samples of scientific reflection)

The subject of the study is a legal document, as the basis of document studies in the field of law. The topic of the study is the study of the evolution of the legal document at the modern stage. The purpose of the study is to study trends of further evolution for the future. To achieve the research goal, general theoretical (analysis, induction) and special (generalization, modeling) methods of scientific knowledge were used. The result of the research is the argumentation of the statement that the storage of electronic documents ensure the preservation over time of such documents, which confirms the effectiveness of the evolution of the electronic document, which ensures the improvement of the document circulation system in the field of law. The conclusion of the study is the substantiation of the position that the form of an electronic digital document is more progressive, compared to the traditional one, therefore it will become more widespread in document circulation in the field of law. The resource of the electronic journal ScienceDirect was used during its conduct. The search tool offered 42 scientific articles published during 1994-2022, but 20 were relevant to the research topic, which were used for analysis and study in it.
Improving Group Recommendation using Revised Slope One

The predisposition of researchers in recommender systems (RS) is increasing due to efficient and personalized recommendations. RS is not able to recommend a product to a group of people. Recently, there has been a lot of interest in group recommendations. Currently, several machine learning and deep learning-based techniques are used to understand the group recommender system (GRS) for goods and find the suitable next items for a group. The major issue of GRS is to satisfy individual member choices in a group. The task of GRS has been accomplished in group formation, individual member rating prediction, and aggregation. This study proposed a revised Slope One predictor in GRS to address this issue. We use the revised Slope One predictor to predict individual member ratings. Preliminary outcomes using two real-world datasets are quite encouraging. The proposed method performs well as compared to the original Slope One.
An Optimal Scheduling Tasks in Cloud Computing

In the era of the growth of the internet, things getting highly distributed, and remote resources are shared on users’ demand with the means of cloud computing. As it is procuring high demand over the sharing of resources to the end users virtually leads to many challenges over the cloud network. Among all these, cloud computing is highly responsible for load balancing among virtual machines (VM), task scheduling, and balancing among resources. Even with the availability of many load-balancing algorithms, proposed a novel architecture for scheduling tasks and VM placement with appropriateness. Various parameters like Makespan, Execution Time, Response Time, Resource Utilization, Capacity, and Load are considered for assessing the VM potentiality. The entire procedure is carried out using CloudSim 3.0.3 simulator for evaluating the performance of the present architecture. From the result, it is proved that the proposed architecture has better performance in obtaining quality of service compared to Antlion and PSO Metaheuristic algorithms.
An Approach to Individualization and Control of Students’ Activities During Wathing a Video Lecture

The article is devoted to the issue of the use of digital educational services in the process of teaching students of higher educational institutions. Based on the experience of using various teaching methods and digital resources in teaching students, new digital solutions are proposed that solve some of the didactic tasks and provide new opportunities for organizing the learning process. The main issue under discussion is the possibility of individualization and control of the learning process in the case of the use of teaching video materials by the teacher. By comparing the traditional face-to-face lecture and the training video, the positive and negative effects of each of the approaches to the delivery of lecture material are described. Based on the results of such a comparison, a video lecture model is proposed that includes interactive feedback elements.
TEACHING ENGLISH TO CHILDREN AT MI TAHFIDZ NURUL HUDA JAPURALOR, CIREBON

Since the implementation of the decentralization policy in education management in 2001, many local governments (PEMDAs) have decided to teach English as a local content (Mulok) at the elementary school level (or even in kindergarten). This local government initiative is problematic because the implementation of this mulok is not accompanied by special efforts to prepare some important aspects such as English teachers to teach in elementary schools and some supporting facilities. This research was conducted within 2 months (from January - February 2022). They were an English teacher at MI Tahfidz Nurul Huda Japuralor and some students of MI Tahfidz Nurul Huda Japuralor. The subjects were selected using Purposive Sampling techniques. This article discusses an overview of teaching English to children in MI Tahfidz Nurul Huda Japuralor, teachers’ perception toward teaching English to Young Learners, and Parents’ persepectives on learning English.
The main characteristic features of project management

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Part 1. Riga, Latvia : Baltija Publishing, 2022. 392 pages.
Digital technologies for the acquisition of professional competences by higher education students

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Part 1. Riga, Latvia : Baltija Publishing, 2022. 392 pages.
Bibliometric Analysis on Cryptocurrency

The development of the world economy, especially in Indonesia, cannot be separated from the elements of information technology. The development of information technology will relate to all fields, including the financial sector. Cryptocurrency is a series of cryptographic mechanisms in which there are transaction data and balance sheet data. Cryptocurrency is electronic data so that it does not have a physical form like money in general. There are two ways for someone to get cryptocurrency, namely by mining and/or trading. In Indonesia, most cryptocurrency users currently use their coins for investment, transaction, or payment purposes and remittances, making transfers to different countries. However, along with the increasing interest of the Indonesian people in investing in bitcoin, several obstacles can potentially reduce public interest in cryptocurrencies. This study aims to determine the development of cryptocurrency research trends published by leading journals. The analyzed data consisted of 873 indexed research publications. The data is then processed and analyzed using the VoS viewer application to find out the bibliometric map of cryptocurrency research development.
Extracting Settings from Multilingual Recipes with Various Sequence Tagging Models: an Experimental Study

In this paper we formulate an information retrieval problem as a sequence tagging task and develop and analyze critically various solutions. The specific context is that of extracting settings (such as cooking times, temperatures and cooking function) from online cooking recipes in multiple languages. We lack annotated data, therefore we start with developing a regex-based automatic tagger for three languages - English, German and French, which has proven to produce good enough annotations for learning. The final goal is to obtain a single extensible multilingual solution, and explore proven cross-lingual capabilities of pretrained multilingual language models to easily accommodate new languages. In the experimental evaluations performed we compare the performance of several monolingual and multilingual solutions, evaluate the impact of specific linguistic features on the performance of the models and assess zeroshot capabilities of the best multilingual solutions. The results indicate that a Conditional Random Field (CRF) layer added on top of Bidirectional Long-Short Term Memory (BiLSTM) models produces the best results in both mono-and multi-lingual settings, reaching Fl-scores of over 96% for all classes, in both learning setups.
Instructions Are All You Need: Cooking Parameters Classification for Monolingual Recipes

This paper addresses the problem of determining certain cooking parameters for monolingual recipes extracted from various websites. The problem is framed as multiple classification tasks, which we address with a number of (increasingly complex) solutions, considering various text representation alternatives (both sparse and dense) and several classifiers, investigating the effect of certain domain engineered features, exploring a joint learning strategy to exploit information sharing between the different classification problems and addressing specific challenges for class imbalance and varying text lengths. We found that textual information alone fed into domain adapted pretrained language models is enough to obtain the best classification accuracy. Moreover, a joint training approach, by uniformly adding the losses, significantly improves the accuracy on all classification tasks; performing a class-weighted loss aggregation does not further improve the behavior of the joint training approach.
Misinformation and hate speech

This chapter discusses harmful speech on the Internet. It focuses on misinformation/disinformation and hate speech and highlights a commitment to the idea of constitutional democracy. Consequently, it reflects on the legitimacy of political power, promotion of pluralism and protection of basic rights. Hate speech, falsehoods and speech manipulation weaken the conditions for healthy democratic debate. This demonstrates the necessity of creating specific public policies that address democratic threats on the Internet beyond the already known policies put into place before the Internet’s creation. However, we need to consider strategies to counter such threats to avoid oversights in the response that may, in turn, further the perils of democracy.
Bias correction for nonignorable missing counts of areal HIV new diagnosis

Public health data, such as HIV new diagnoses, are often left‐censored due to confidentiality issues. Standard analysis approaches that assume censored values as missing at random often lead to biased estimates and inferior predictions. Motivated by the Philadelphia areal counts of HIV new diagnosis for which all values less than or equal to 5 are suppressed, we propose two methods to reduce the adverse influence of missingness on predictions and imputation of areal HIV new diagnoses. One is the likelihood‐based method that integrates the missing mechanism into the likelihood function, and the other is a nonparametric algorithm for matrix factorization imputation. Numerical studies and the Philadelphia data analysis demonstrate that the two proposed methods can significantly improve prediction and imputation based on left‐censored HIV data. We also compare the two methods on their robustness to model misspecification and find that both methods appear to be robust for prediction, while their performance for imputation depends on model specification.
Intelligentized Integration of Maritime English Teaching

At present, maritime English teaching is still limited to the traditional single teaching methods and teaching tools, which cannot meet the requirements of the shipping industry for talent training. The combination of questioning method and blackboard, cooperative teaching method and card, teaching method and multimedia, situational teaching method and physical object, and project teaching method and simulator equipment can enrich the classroom content of marine English and improve the teaching quality of marine English. Based on the advantages of mobile learning and smart classroom in the field of English learning, this paper discusses the integration path of mobile technology and English smart classroom, explains its preconditions, teaching design and implementation methods, and provides ideas for the feasibility and normalization of classroom teaching intelligent reform.
Optimizing computational costs of Spark for SARS‐CoV‐2 sequences comparisons on a commercial cloud

Summary Cloud computing is currently one of the prime choices in the computing infrastructure landscape. In addition to advantages such as the pay‐per‐use bill model and resource elasticity, there are technical benefits regarding heterogeneity and large‐scale configuration. Alongside the classical need for performance, for example, time, space, and energy, there is an interest in the financial cost that might come from budget constraints. Based on scalability considerations and the pricing model of traditional public clouds, a reasonable optimization strategy output could be the most suitable configuration of virtual machines to run a specific workload. From the perspective of runtime and monetary cost optimizations, we provide the adaptation of a Hadoop applications execution cost model extracted from the literature aiming at Spark applications modeled with the MapReduce paradigm. We evaluate our optimizer model executing an improved version of the Diff Sequences Spark application to perform SARS‐CoV‐2 coronavirus pairwise sequence comparisons using the AWS EC2's virtual machine instances. The experimental results with our model outperformed 80% of the random resource selection scenarios. By only employing spot worker nodes exposed to revocation scenarios rather than on‐demand workers, we obtained an average monetary cost reduction of 35.66% with a slight runtime increase of 3.36%.