Reviewerly/exHarmony huggingface dataset:
Challenges for Subject Categorization of Fake News about COVID-19 in Brazil: Knowledge Organization against Disinformation

This book explores the latest advances in how knowledge organisation can both draw upon and inform diverse disciplines and technological developments. It explores how best to combine theory and practice, with its contents considering practical solutions as well as the theory behind the design, development and implementation of knowledge organising systems, ranging from controlled vocabularies, classification systems and metadata schemas to ontologies and taxonomies. With contributions by Carmen Agustín-Lacruz, Mayukh Bagchi, Mario Barité, Maria Teresa Biagetti, Briony Birdi, Lynne Bowker, Thiago Henrique Bragato Barros, Ana Inés Brozia, Gema Bueno-de-la-Fuente, Julia Bullard, Rafael Cacciolari Dalessandro, José Augusto Chaves Guimarães, Christian Cole, Stephanie Colombo, Luciana Corts Mendes, Luis Corujo, Niamh Nic Daeid, Patrícia de Almeida, Hadassa De Zen Itepan, Anthony Dunbar, Jonathan Furner, Francisco-Javier García-Marco, Fausto Giunchiglia, Claudio Gnoli, Míriam Gontijo de Moraes, Hazlem Granados-Amaya, Hugo Alberto Guadarrama-Sánchez, Carlos Guardado da Silva, David Haynes, Ania Hernández Quintana, Philip Hider, Chris Holstrom, Eva Hourihan Jansen, Fidelia Ibekwe, Mat Kelly, Michael Kleineberg, Nair Yumiko Kobashi, Deborah Lee, Maël Le Gall, Joaquín López del Ramo, Luís Miguel Oliveira Machado, Hugo Moquet, Catalina Naumis-Peña, Mirtha Rauch, Mariana Ramos Crivelente, Raissa Resende de Moraes, Jorge Revez, Riccardo Ridi, Somayeh Roshan, Clarissa Schmidt, Negin Shokrzadeh Hashtroudi, Richard P. Smiraglia, Mariângela Spotti Lopes Fujita, Adriana Suárez-Sánchez, Erik Stubkjwær, Rick Szostak, Seyed Mahdi Taheri, Marc Tanti, Ana Lúcia Terra, Amanda Terrero Trinquete, Natalia Bolfarini Tognoli, Jéssica Beatriz Tolare, Raquel Torrão Valentim, Hyung Wook Choi and Mohsen Haji Zeinolabedini.
Real-Time Human–Machine Interaction Through Voice Augmentation Using Artificial Intelligence

In real time, there is a huge demand to access dynamic, personalized, and adaptive information. Interacting through voice augmented systems yields efficient and faithful access to great deal of knowledge. And the obtained information becomes more meaningful and understandable because it is appropriately customized in the aspect of real-time physical, digital, and virtual interactions. The objective of this project is to display the interactions of voice augmentation functions to achieve human to machine interaction (HMI). The quality of the services can be improved with the help of artificial intelligence through deep learning models to illustrate the possibilities of its potential. There exist a good range of applications which include vending machines, billing counters, home assistant, etc. This work includes audio processing, artificial intelligence, text-to-speech conversion, speech to text, audio conversion, various machine learning algorithms and models, implementation using the Internet of things, and applications in various fields, to evaluate the model.
Human interactive behaviour recognition method based on multi-feature fusion

Recently, the selection of the overall and individual characteristics in interactive actions and the high-dimensional complexity of features are still important factors affecting the recognition accuracy. In this paper, we propose a human interactive behaviour recognition method based on multi-feature fusion, which includes two parts, feature extraction and behaviour recognition. Firstly, we use histogram feature descriptors to form a three-dimensional gradient histogram of local space-time feature (3D-HOG) and histogram of global optical flow feature (HOF). Then the bag-of-words model is used to reduce the dimensions and the classification matrix is obtained through multilayer perceptron (MLP) classifiers. In the second part, we use recurrent neural network (RNN) to get connections in time. Considering the information of interactive behaviour will be different at different stages, an improved Gauss neural network are proposed for interactive behaviour recognition. The experimental results show that the algorithm can effectively improve the accuracy in the UT-Interaction dataset.
Microblog Emotion Analysis Method Using Deep Learning in Spark Big Data Environment

Aiming at the problem that the existing methods in the big data environment cannot extract the emotional features of microblog sufficiently and the average accuracy of analysis results is low, a microblog emotion analysis method using deep learning in spark big data environment is proposed. First, the Jieba word segmentation method is used to process text comments, so as to reduce the interference of irregular grammar and nonstandard words on the emotion analysis task of microblog text. Then, features based on affective rules, unary word features, syntactic features, and dependent word collocation features are selected. In order to prevent the dimension disaster caused by excessive feature dimensions, the feature selection method of information gain is used to reduce the dimension of features. Finally, a microblog emotion analysis method based on deep belief network (DBN) is established, and the DBN is parallelized through spark cluster to shorten the training time. Experiments show that when the feature set is composed of TOP2000 features, the classification accuracy of the fusion of four features is 90.94%, which is higher than that of the comparison method. In addition, the training time of DBN algorithm parallelized by spark cluster is only 27.78% of that of single machine. Therefore, compared with the comparison method, the proposed method can significantly improve the performance of the microblog emotion analysis system.
When Chatbots Fail: Exploring Customer Responsibility Attributions of Co-Created Service Failures: An Abstract

Extant literature has predominantly viewed value co-creation as a voluntary process of value creation between the customer and the service provider (e.g., Grissemann and Stokburger-Sauer 2012; Sugathan, Ranjan, and Mulky 2017). However, the customer may not always enter into the value co-creation process voluntarily. As chatbots are increasingly replacing traditional customer service options (Belanche et al. 2020), customers may find themselves in situations where they are forced to use automated technologies (Reinders, Frambach, and Kleijnen 2015). Indeed, several service providers have started to offer chatbots as the only customer solution, especially as the first point of contact for customer service queries (Forrester 2017). Such cases, where the customer does not have a choice as to whether to interact with a chatbot or a human representative, represent instances of mandatory customer participation (Dong and Sivakumar 2017; Tsai, Wu, and Huang 2017). Limiting consumers’ perceived freedom of choice in co-creating, may result in severe negative attitudes towards the evaluation of the technology and the service provider (Reinders, Dabholkar, and Frambach 2008). Such attitudes may be further exacerbated when a situation requiring the forced use of a technology leads to service failure. We argue that in cases of service failure, forced co-creation (no choice but to interact with chatbot) will have a distinct impact on customer responsibility attributions of controllability, stability and locus of causality, when compared to voluntary co-creation (choice between chatbot and human representative). We also investigate the role of customer expectations and propose that expectations mediate the relationship between each co-creation setting and the resulting responsibility attributions. Experimental research is being proposed to test the research framework in a customer service setting, taking into consideration two types of service failure: outcome and process failure. Participants will be randomly assigned to two different co-creation settings: forced co-creation and co-creation by choice, and will be required to interact with an actual chatbot, which has been programmed using the IBM Watson platform. Participant responses will be recorded through an online questionnaire. The findings of this experimental research are expected to contribute to a more insightful understanding of the consequences of forcing consumers to use novel technologies. This is an important research area which has been largely overlooked in service literature to date, as the literature has typically examined the introduction of new technologies within a voluntary context. Insights into these behaviours are also important to achieve a more substantive understanding of the implications of AI technologies, and to find ways to reduce negative reactions to such technologies.
Assessment of De-noising Filters for Brain MRI T1-Weighted Contrast-Enhanced Images

Noise in the medical images is always of great concern because it can lead to misinterpretation for advance process. With advancements in technology, the quality of image capturing through MRI improved, but noise is still present; therefore, noise present in the MRI images should be removed to get the good quality of images for accurate diseases detection and its diagnosing. In this work, five different filtering algorithms, named as non-local mean (NLM) filter, anisotropic filter, Weiner filter, bilateral filter, and Gaussian filter, used to eradicate the different types of noise named (salt and pepper, speckle, and Gaussian) through brain MRI images. PSNR, SSIM, and MSE are statistical parameters used for analyzing the performance of the filters. The study shows that for Gaussian noise, Weiner filter is considered the most efficient filter. For salt and pepper noise, Gaussian filter work better than other filters. In the case of speckle noise, anisotropic works better on low noise density, whereas for high noise density, Gaussian filter works better.
Human and machine translation: a comparative analysis of neural machine- and human-translated EN-HU and HU-EN legal texts

As neural machine translation is increasingly more capable of modelling how natural languages work, the traditional tasks of translators are being gradually replaced by new challenges (Castilho et al., 2019). Consequently, more emphasis is placed on pre- and post-editing (revision) skills and competences (Pym, 2013; Robert et al., 2017), enabling the production of higher quality and near human-made translations. Therefore, the efficiency of pre- and post-editing largely depends on how aware translators are of the mechanisms and limitations of neural machine translation tools adopted in given language pairs (Lample et al., 2018). This paper aims to demonstrate through the comparison of the neural machine and human-translated English and Hungarian translations of Hungary’s Fundamental Law and the U.S. Constitution, respectively, the different challenges arising in the course of translation and posed by post-editors, especially from the perspective of comprehensibility and well-formedness.
Constructions of Space and Social Networks – First Insights into a TPACK Survey for Geography Teachers

In the digital condition, social networks are part of the everyday practices that produce constructions of space.To include this phenomenon in the Geography classroom, the corresponding knowledge that teachers require and how to measure it need to be determined.For this purpose, a specific instrument is necessary.We analyse constructions of space as a teacher knowledge domain through the lens of the TPACK model and deduce pedagogical and technological knowledge.We then combine these results with available TPACK survey instruments.The product is a self-assessment survey for Geography teachers on constructions of space and social networks.Scale consistency measures and item discrimination yielded good results.Initial results indicate technological and pedagogical knowledge to be greater than content knowledge.The instrument we present is valid for determining future Geography teachers' TPACK more broadly, and can reveal implications for interventions in Geography teacher education.
Facilitating Learner Centric Decision Making for Massive Open Online Courses

Abstract : Learners opt for MOOCs as it provides multiple benefits and the most important being that these courses can be accessed anytime and from anywhere with internet access. Learners can study their desired course at their convenience of time and desired pace. But as there are multiple options for them, they may get confused about selecting a course. This study highlights the findings of implementing the Analytical Hierarchical Process (AHP), a Multiple Criteria Decision Making (MCDM) technique used to choose the best option in case of multiple alternatives. Attributes were selected from literature and a survey was administered to learners. Responses were analyzed and courses were ranked based on their scores. From the analysis, we interpret that usefulness of the course in the University Curriculum is the most preferred criteria while selecting the course. Keywords : MOOC, Multiple Criteria Decision Making, Analytical Hierarchical Process
Interpretable Lightweight Ensemble Classification of Normal versus Leukemic Cells

The lymphocyte classification problem is usually solved by deep learning approaches based on convolutional neural networks with multiple layers. However, these techniques require specific hardware and long training times. This work proposes a lightweight image classification system capable of discriminating between healthy and cancerous lymphocytes of leukemia patients using image processing and feature-based machine learning techniques that require less training time and can run on a standard CPU. The features are composed of statistical, morphological, textural, frequency, and contour features extracted from each image and used to train a set of lightweight algorithms that classify the lymphocytes into malignant or healthy. After the training, these classifiers were combined into an ensemble classifier to improve the results. The proposed method has a lower computational cost than most deep learning approaches in learning time and neural network size. Our results contribute to the leukemia classification system, showing that high performance can be achieved by classifiers trained with a rich set of features. This study extends a previous work by combining simple classifiers into a single ensemble solution. With principal component analysis, it is possible to reduce the number of features used while maintaining a high accuracy.
Corpus Creation and Annotation Framework for Sentiment Analysis of Hindi News Articles

This research is based on the application of sentiment analysis for Hindi news articles. When it comes to text and sentiment analysis, very less research has been done specifically in the domain of Hindi news articles. The system is targeted toward specific domain of defense news articles (Hindi) to understand and analyze emotion of articles from different news agencies. Custom scrapers were designed for data extraction and corpus creation which was later used for training the model. URLs of one of the selected news agencies are provided to the system to extract the content of the articles. Further Multinomial Naive Bayes is used to predict the polarity of a given sentence in the given article based on the previously trained corpus. The polarity count of individual sentences will be used to calculate the maximum value for polarity which will be considered as the overall sentiment of the article.
Learning Mathematics Online Using Information Technology Tools during COVID-19 in Kosove

The COVID-19 pandemic has brought significant challenges to higher education worldwide.Due to the COVID-19 pandemic, e-learning has begun to be widely used and applied in the teaching and learning processes.However, learning under technological circumstances has proven not always to be a proper solution in education.A highlight challenge, in this regard, is considered to be learning Mathematics online.While some support its positive impact, others greatly oppose it by arguing that neither teaching nor learning has proven successful.Thus, this study examines Kosovo selected universities to see the effectiveness of learning Mathematics online as a case study.Further, it compares the online and traditional learning methods and explores how teachers in higher education in Kosova Universities apply and integrate technology into learning mathematics.This study employed a methodology encompassing questionnaires for students.The results show that students are not overall satisfied with learning Mathematics online leading to the conclusion that online learning is not an effective educational method for learning Mathematics.
The Role of Artificial Intelligence Abilities in Library Services

Artificial intelligence applications are one of the most important modern technologies that have emerged in recent times, which have shaped a major change in the functions of institutions, especially the scope of libraries and information. This study was described the role of Artificial Intelligence Applications of knowledge management in different information organizations, as well as to identify the reality of employing artificial intelligence applications in libraries to enrich the Knowledge Management, exposing the relationship between artificial intelligence applications and its ability to develop technical and administrative processes in libraries of Knowledge Management and Knowing the challenges facing the libraries to apply the artificial intelligence applications. The research is based on descriptive method by content analysis of literature review by listing the most important and related published Arabic and foreign literature it was token the studies that are focused in Artificial Intelligence Applications of knowledge management in information organizations. The study conclude that the most important results of which are: There are a lot of libraries that apply artificial intelligence technology in their services, whether technical in the provision of services, references and others, which undoubtedly facilitated the ease of retrieval and search for the users, the available studies discussed various aspects of the reality that this new application plays in facilitating library services, and most studies agree on that to facilitate services and their quality intelligence and the application of artificial intelligence requires the availability of a set of basic ingredients and requirements, such as strong technical equipment, and qualified human resources capable of using and developing this technology. The study recommended the necessity for libraries and information centers to strive to keep pace with the changes of artificial intelligence and to make the right investment for knowledge management, increasing field studies to explore the requirements of artificial intelligence technologies and diversity in the services provided by artificial intelligence in the library and activating them effectively
A Schema-Free Instance Matching Algorithm Based on Virtual Document Similarity

With the continuous development of semantic web, especially of the web of data, several knowledge bases expressed by ontologies are independently created and added to the Linked Open Data (LOD) cloud, on a daily basis. A major challenge for the LOD paradigm is to discover resources that refer to the same real-world object, in order to interlink web resources and hold large scale data integration and sharing. In this context, instance matching is a promising solution. It aims to link co-referent instances belonging to heterogeneous knowledge bases with owl:sameAs links. Several state-of-the-art existing approaches addressing this issue are based on the prior schema-level matchings, which does not avoid the limitation of heterogeneity at the property-level. In this paper, we propose a schema-free, scalable and efficient instance matching approach that is independent from matching results at the schema-level. We transform the instance matching problem to a document similarity problem and we solve it by a Clustering technique that uses an Ascendant Hierarchical Clustering algorithm to group similar instances in the same clusters. Furthermore, we design multiple validating patterns that use some structural information to validate obtained mappings and eliminate wrong ones. Experiments on instance matching track from Ontology Alignment Evaluation Initiative (OAEI) show that our approach gets prominent results compared to several participating systems in OAEI’2019, OAEI’2020 and OAEI’2021.
Echo State Network Optimization using Hybrid-Structure Based Gravitational Search Algorithm with Square Quadratic Programming for Time Series Prediction

The Echo-State Network (ESN) is a robust recurrent neural network and a generalized form of classical neural networks in time-series model designs. ESN inherits a simple approach for training and demonstrates the high computational capability to solve non-linear problems. However, input weights and the reservoir's internal weights are pre-defined when optimizing with only the output weight matrix. This paper proposes a Hybrid Gravitational Search Algorithm (HGSA) to compute ESN output weights. In Gravitational Search Algorithm (GSA), Square Quadratic Programming (SQP) is united as a local search strategy to raise the standard GSA algorithm's efficiency. Later, an HGSA-SQP and the validation data set to establish the relation configuration of the ESN output weights. Experimental results indicate that the proposed configuration of HGSA-SQP-ESN is more efficient than the other conventional models of ESN with the minimum generalization error.
Visual Heuristics

Research highly depends on third-party funds, raising the importance of preliminary results and research prospects in grant applications. At the same time, historians increasingly disseminate interim findings or data. This development contrasts an older tradition when historians mainly publicized finished works. Therefore, historians face the challenge of thoroughly making the “unfinished” plausible more than ever. Our article focuses on heuristics as an early stage of research and visualizations as suitable heuristics tools to address this challenge. Network graphs, in particular, enable historians (1) to grasp complex historical constellations and patterns better and (2) to lay those configurations open transparently. This helps historians specifically to grapple with complex temporal constellations beyond linear chronology. Unfortunately, the qualities of visualizations responsible for this potential still are underexposed. Moreover, when utilized, those qualities seldomly are being considered. For this reason, our article outlines the heuristic potential of visualizations borrowing from multimodality research
A Review on Big Data Analytics between Security and Privacy Issue

Because of technology, a large number of portable devices, and the great use of networks and the need for them in education, industry, and politics. This widespread has led to the emergence of a huge amount of data, and securing this data has become a very important matter. Big data refers to a very large number of data collected from different sources, this huge amount of data gives us deep insight and the benefit of this is to improve the decision-making process and thus lead to better customer service. Securing this huge amount of data (big data) is very difficult. Because of the large number of data and the continuous flow of this data that's come from different sources. Big data is a term that refers to a large, diverse, and complex amount of data. Big data analytics is a sophisticated and advanced technology used to analyze large, different, and diverse data in order to understand this type of data. However, there are many issues when using and analyzing big data, including the problem of security and privacy. This paper focuses on security and privacy problems and focuses on the biggest privacy issue Big data. The aim of this paper is to study the aspects of security and privacy and to make a comparison of the most important data security methods and the biggest privacy issues.