Reviewerly/exHarmony huggingface dataset:
State of the Art

Measuring and validating QoE of omnidirectional videos using different HMDs have become increasingly important. The resulting user experience plays an important role in making technology successful and acceptable for a wide user group. The respective “QoE” is defined as the “…degree of delight or annoyance of the user of an application or service…” (P. Le Callet et al. (Qualinet white paper on definitions of quality of experience, in European Network on Quality of Experience in Multimedia Systems and Services (COST Action IC 1003) 3.2012, 2012), ITU–T Rec. P.10/G.100, Vocabulary for Performance and Quality of Service. International Telecommunication Union. Geneva, 2017). One component of QoE is the perceptual media quality of the viewing session, resulting from different technical characteristics of the end-to-end chain comprising the recording, post-processing, representation, coding, retrieval, transmission, playback, and display of 360 $${ }^\circ $$ videos. Furthermore, for 360 $${ }^\circ $$ video, exploration behavior, presence, and simulator sickness also represent key aspects of QoE. Therefore, besides media quality, these three further constructs need to be assessed for a holistic understanding of QoE in the case of omnidirectional videos.
Semantic Technologies to Model Battery Data and Knowledge

Data-intensive battery research is here to stay. With open data incentives on the rise and simpler software interfaces granting access to powerful machine learning algorithms, researchers are uncovering a wealth of insights from battery data. However, data without context are just unusable digital bits. Battery data must be appropriately described to render it interoperable and reusable for traditional and data-intensive research. This requires overcoming numerous challenges associated to data heterogeneity, inconsistency, standardization, and meaning. Semantic technologies address these challenges by providing a unified and flexible framework for integrating and reasoning about data from heterogeneous sources. In this contribution, we will introduce Semantic Technologies and how they enable both human and software agents to understand and reuse battery data. We will present the tools we are developing to i) encourage the adoption of a common standard vocabulary to describe the battery domain[1], ii) connect research resources (datasets, experts, reports, publications, projects) within an accessible platform[2], and iii) reduce the effort required to upgrade data with semantic context. Overall, these tools take concrete steps towards guaranteeing compliance with FAIR data principles. We will further outline plans for future development, including strategies for reaching out to the global battery landscape, facilitate the curation of battery data for data-driven research, and enable knowledge exploration via natural language models. [1] S. Clark et al. , "Toward a Unified Description of Battery Data," Adv. Energy Mater. , vol. 2102702, 2021. [2] https://github.com/BIG-MAP/Demo-BatteryDataSemanticSearch
Natural Languages as Origin of Misinforming

This chapter explores linguistic factors that cause misinforming. It presents the origin of misinforming as building in the polysemy of the natural languages. The evolution of natural languages reflects the needs of human life according to its specific properties such as surrounding nature, climate, food, etc. They are developed historically and correspond to the way of formation of a group of people as a society. The language is the key component of the group's culture, and the society is usually identified by the language. The language is a tool to facilitate informing within the members of the group and may serve as a barrier in communication between members of the group with members of other groups. In the computer information systems era, communication via technology added complexity and as well as the risk of misinformation.
Designing a Marketplace to Exchange AI Models for Industry 5.0

Abstract Nowadays, the market for AI services is continuously growing and it is expected to exceed 5 trillion euros in the next 5 years. However, the sharing of knowledge is primarily achieved by the sharing of published AI-related papers. The sharing of the trained AI/ML models is still in its infancy stage and in some domains it does not even exist. In this chapter, a marketplace for exchanging AI models related to smart manufacturing and Industry 5.0 domains is introduced. The proposed AI Marketplace consists of a semantic-based repository that manages the AI models, a blockchain-based framework that adds the business logic and web-based user interfaces that enable models’ exploration and sharing, and transactions among the stakeholders. The purpose of this chapter is to present the implementation details of this AI Model Marketplace by highlighting the key concepts and technologies used along with the main supported functionalities. By using such a marketplace, the manufacturing companies are able to capitalize in a large variety of AI models to solve various problems enabling intelligent, flexible, and cost-effective production.
Transformative Progress in Document Digitization: An In-Depth Exploration of Machine and Deep Learning Models for Character Recognition

Introduction: this paper explores the effectiveness of character recognition models for document digitization, leveraging diverse machine learning and deep learning techniques. The study, driven by the increasing relevance of image classification in various applications, focuses on evaluating Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and VGG16 with transfer learning. The research employs a challenging French alphabet dataset, comprising 82 classes, to assess the models' capacity to discern intricate patterns and generalize across diverse characters. Objective: This study investigates the effectiveness of character recognition models for document digitization using diverse machine learning and deep learning techniques. Methods: the methodology initiates with data preparation, involving the creation of a merged dataset from distinct sections, encompassing digits, French special characters, symbols, and the French alphabet. The dataset is subsequently partitioned into training, test, and evaluation sets. Each model undergoes meticulous training and evaluation over a specific number of epochs. The recording of fundamental metrics includes accuracy, precision, recall, and F1-score for CNN, RNN, and VGG16, while SVM and KNN are evaluated based on accuracy, macro avg, and weighted avg. Results: the outcomes highlight distinct strengths and areas for improvement across the evaluated models. SVM demonstrates remarkable accuracy of 98.63%, emphasizing its efficacy in character recognition. KNN exhibits high reliability with an overall accuracy of 97%, while the RNN model faces challenges in training and generalization. The CNN model excels with an accuracy of 97.268%, and VGG16 with transfer learning achieves notable enhancements, reaching accuracy rates of 94.83% on test images and 94.55% on evaluation images. Conclusion: our study evaluates the performance of five models—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and VGG16 with transfer learning—on character recognition tasks. SVM and KNN demonstrate high accuracy, while RNN faces challenges in training. CNN excels in image classification, and VGG16, with transfer learning, enhances accuracy significantly. This comparative analysis aids in informed model selection for character recognition applications.
Build a Trained Data of Tesseract OCR engine for Tifinagh Script Recognition

This article introduces a methodology for constructing a trained dataset to facilitate Tifinagh script recognition using the Tesseract OCR engine. The Tifinagh script, widely used in North Africa, poses a challenge due to the lack of built-in recognition capabilities in Tesseract. To overcome this limitation, our approach focuses on image generation, box generation, manual editing, charset extraction, and dataset compilation. By leveraging Python scripting, specialized software tools, and Tesseract's training utilities, we systematically create a comprehensive dataset for Tifinagh script recognition. The dataset enables the training and evaluation of machine learning models, leading to accurate character recognition. Experimental results demonstrate high accuracy, precision, recall, and F1 score, affirming the effectiveness of the dataset and its potential for practical applications. The results highlight the robustness of the OCR system, achieving an outstanding accuracy rate of 99.97%. The discussion underscores its superior performance in Tifinagh character recognition, exceeding the findings in the field. This methodology contributes significantly to enhancing OCR technology capabilities and encourages further research in Tifinagh script recognition, unlocking the wealth of information contained in Tifinagh documents.
VERBAL MEANS OF CREATING ARTIFICIAL SPACE IN THE LITERATURE OF THE FANTASY GENRE (BASED ON THE MATERIAL OF THE NARRATIVE ABOUT HARRY POTTER BY JK ROWLING «HARRY POTTER AND THE GOBLE OF FIRE»)

The article is devoted to the analysis of verbal means of creating artistic space in modern fantasy literature. The issue of literary geography and mapping of the narrative text is considered on the example of the fourth part of the septol- ogy of Harry Potter adventures “Harry Potter and the Goblet of Fire” by JK Row- ling. Special attention is paid to the means of transition from the magical world of the story to the muggle world of the main character. The verbal means of creating the artistic space of this narrative are also highlighted and analyzed based on the book material of the original text of the British author and the Ukrainian translation by V. Morozov and S. Andruhovych. The translation analysis of this work was carried out; extralinguistic and linguistic factors of the narrative about the little sorcerer are highlighted. A statistical analysis of selected verbal means of translation of the artistic space was carried out and translation operations were singled out. The toponymic names of the fantasy narrative were separately ana- lyzed and the means of their translation from English to Ukrainian were high- lighted. The results of the study show that Ukrainian translators chose the strategy of communicative-equivalent translation, which is the closest to the text, using adaptive means of translation. Thanks to this, the Ukrainian translation is con- sidered one of the best translations for the Ukrainian recipient.
Sentimental Analysis on Zomato Restaurant Reviews using Bi-LSTM

Sentiment analysis is a vital aspect of understanding public opinion and sentiment towards products and services. This paper presents a sentiment analysis work focused on Zomato restaurant reviews in Bangalore, aiming to classify restaurants into positive, negative, or neutral sentiment categories based on customer reviews. Bi-LSTM and Bi-GRU models are employed to capture contextual information in the sequential data of reviews. Additionally, sentiment analysis techniques, including Word2Vec, VADER, and Sentiment Intensity Analyzer, are integrated to enhance the sentiment classification process. Through rigorous experimentation, the performance of these models and techniques is evaluated. The proposed models demonstrate promising accuracy rates in sentiment classification. By enhancing and expanding the sentiment analysis framework, this paper contributes to a deeper understanding of public sentiment towards Zomato restaurants in Bangalore. The insights derived from this study can facilitate informed decision-making for both restaurant owners and customers, ultimately improving the dining experience and customer satisfaction with accuracy of 98.6%.
Hybrid Approach for Accurate Vocal Anomaly Detection Using State-of-the-Art

Accurate voice anomaly detection is achieved by the suggested method's combination of signal processing for feature extraction and improvement with deep learning. Preprocessing, feature extraction, and anomaly detection are the three pillars upon which this approach rests. The raw vocal signal is improved during preprocessing by denoising and filtering. From the preprocessed signal, the feature extraction stage derives Mel-Frequency Cepstral Coefficients (MFCCs) and their derivatives. Finally, an ensemble of three algorithms, including Support Vector Machines (SVM), Long Short-Term Memory (LSTM) Networks, and Convolutional Neural Networks (CNN), is used in the anomaly identification stage to conduct an in-depth evaluation of the detection of voice anomalies. The suggested hybrid method is meant to be more flexible and efficient in practical settings.
Exploring Thermometer Code Addition and Subtraction in Quantum Computing

Quantum computing, a new technology, has the potential to alter a wide range of fields, from encryption and chemistry to optimisation. Among its fundamental operations, addition and subtraction of integers are the most important. This study looks into the fascinating world of quantum computation, focusing on the clever thermometer code method to addition and subtraction. We reveal the underlying workings of these operations using Qiskit, an open-source quantum computing platform, by analysing the algorithms and quantum circuits that fuel them. We uncover the performance complexities of these quantum processes by thorough research over a wide range of input conditions. The findings show the way forward in a world filled with quantum promise. Thermometer code arithmetic emerges as a potent weapon in the quantum toolbox, with the potential to transform the way we calculate. This report is more than simply a look into quantum possibilities; it's a call to action, urging researchers to push their boundaries and realise the full potential of this revolutionary technology.
Optimal Feature Selection for Retweet Prediction in Indian Election

Retweet is a Twitter feature use for information propagation to the other user over the platform. During any political event it extensively used for campaigning, spreading information, show political agendas by political parties. The interesting question arises how to predict the retweet probability of the posted text. For this numerous features have been applied in the past. This work aims to find the optimal set of features for retweet prediction using meta heuristic algorithms. Feature selection is one of the important aspects in text classification to reduce the time complexity of the scalable data. We have used genetic algorithm and particle swarm optimization to obtain the minimal set of features to predict the virality of the tweet. We have used three machine learning models K Nearest Neighbors, Naive Bayes and Support Vector Machines to measure the performance of the meta heuristic algorithms in context of features reduction.
Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments

After the COVID-19 pandemic caused internet usage to grow by 70%, there has been an increased number of people all across the world using social media. Applications like Twitter, Meta Threads, YouTube, and Reddit have become increasingly pervasive, leaving almost no digital space where public opinion is not expressed. This paper investigates sentiment and semantic relationships among comments across various social media platforms, as well as discusses the importance of shared opinions across these different media platforms, using word embeddings to analyze components in sentences and documents. It allows researchers, politicians, and business representatives to trace a path of shared sentiment among users across the world. This research paper presents multiple approaches that measure the relatedness of text extracted from user comments on these popular online platforms. By leveraging embeddings, which capture semantic relationships between words and help analyze sentiments across the web, we can uncover connections regarding public opinion as a whole. The study utilizes pre-existing datasets from YouTube, Reddit, Twitter, and more. We made use of popular natural language processing models like Bidirectional Encoder Representations from Transformers (BERT) to analyze sentiments and explore relationships between comment embeddings. Additionally, we aim to utilize clustering and Kl-divergence to find semantic relationships within these comment embeddings across various social media platforms. Our analysis will enable a deeper understanding of the interconnectedness of online comments and will investigate the notion of the internet functioning as a large, interconnected brain.
Blockchain Powered IoT Access Control Model for Secure Data Sharing and Management: Performance Analysis

Typically, Internet of Things (IoT) generated data comes from many trust domains and is eventually shared to offer a wide range of services. However, the sensitive data transmitted among many sources is vulnerable to unauthorized access or leaks. The conventional access control solutions which are based on a centralized system are not efficient in adapting to the dynamic and distributed IoT environment and are even confronted with many other limitations such as single point of failure and censorship of data. We have proposed a Blockchain and IPFS-based dual authorization scheme to address the challenges of conventional access control models. In this model, IPFS has replaced central storage and thus improved data availability. Additionally, Blockchain is utilized to record access policies and cryptographic hash codes of IoT-generated data while the actual data resides over IPFS, which makes the proposed architecture more scalable. Experimental results illustrate the higher performance of the proposed access control solution as compared to state-of-the-art techniques.
Secure Data Sharing in Cloud Environment using Mobile Technology

The developing requirement for the far-off minding of patients at home joined with the consistently expanding fame of cell phones because of their omnipresent nature has brought about numerous applications being created to empower portable telecare. The Cloud, in blend with portable advancements has empowered specialists to helpfully screen and survey a patient's wellbeing while the patient is at the solace of their own home. This request sharing of wellbeing data between medical care groups, for example, specialists and attendants to give better and more secure consideration of patients. Be that as it may, the sharing of wellbeing data presents protection and security issues which may struggle with HIPAA principles. In this paper, we endeavor to address the issues of protection and security in the area of portable telecare and Cloud processing. We initially show a telecare application that will permit specialists to distantly screen patients by means of the Cloud. We at that point utilize this framework as a premise to feature our model that will permit patients to impart their wellbeing data to different specialists, attendants or clinical expert in a safe and classified way. The critical highlights of our model incorporate the capacity to deal with huge information sizes and effective client disavowal.
Android-based Didactical Design for Learning Lines and Angles: An Interpretive Didactical Design Research

Lines and angles are quite important mathematical concepts. This is proven because lines and angles cannot be separated in life. However, lines and angles actually become a problem/obstacle in the classroom. The purpose of this research is to determine the description of students' learning obstacles in the material on lines and angles for class VIII MTs NW Pringgajurang, then a didactic design for learning lines and angles will be prepared. The research design used is didactical design research-interpretive. The participants in this research were Class VIII students at MTs NW Pringgajurang with an age range of 12 to 14 years. The researcher is the main instrument in this research, with several additional instruments such as tests for understanding lines and angles, student biodata, interview guides and didactic designs for lines and angles. The data analysis technique used in this research is thematic data analysis. This thematic analysis is used as a basic foundation for the analytical process in qualitative research, by paying attention to the results of the data collection carried out. The results of the research show that students experience learning obstacles with ontogenic psychological and ontogenic conceptual types. With this Android-based didactic design, it is hoped that it will be able to minimize barriers to students' learning on line and angle material at MTs NW Pringgajurang.
OntoCnE, characterizing Learning Resources for training Computational Thinking

A Computational Thinking (CT) training program proposes a set of instruments to be used in the training, and also a set of recommendations on how to use them. To design such a program it is necessary to have a deep knowledge about the meaning of CT and its targets. Although CT skills are of paramount importance to all (since they bring numerous advantages for problem solving activities), in our context we see them as a pre-requisite for the learning of Computer Programming (CP). In order to design a CT training program supported by a rigorous framework, we decided to build an ontology that goes deeper into the underlying concepts and relates both domains, CT and CP. The outcome, OntoCnE (that stands for Ontologia para Computação na Escola in Portuguese), made clear what shall be trained in terms of reasoning to understand programming and being able to use it to solve problems using the computer. Moreover, OntoCnE also provides a clear insight on the concepts to be taught in each scholar year, as well as, on the instruments (hereafter called Learning Resources, LR) to be used in each moment for each specific purpose. This paper aims to introduce OntoCnE and demonstrates how to use it to classify LRs aiming to build a repository from where they can be picked up in a clear way. Finally, we demonstrate, through the PathIt resource, how the characterization of a concrete LR is performed in OntoCnE.
The Use of Immersive Virtual Reality in Educational Practices in Higher Education: A Systematic Review

This study aims to contribute to the expansion of discussions and considerations on the use of immersive technologies in an educational context. With the advancement of information technologies, immersive technologies, especially Virtual Reality (VR), can be important allies in the digital transformation of education. The purpose of this study is to understand how immersive virtual reality contributes to student learning in higher education. The objective is to analyse how learning occurs in immersive environments, their characteristics, benefits, and limitations. A systematic literature review methodology was adopted following the PRISMA protocol. The selection process of academic publications was based on an initial set of 764 articles, resulting from a search of the EBSCO database, published between 2017 and 2022. Three stages of article selection were carried out, resulting in the selection of 39 publications for analysis. The results show that the number of publications investigating empirical experiences has increased in recent years. Virtual Reality enables the creation of immersive virtual environments in which students can actively interact and explore. These simulated environments offer students the opportunity to experience authentic situations and apply their knowledge in a practical way.