Reviewerly/exHarmony huggingface dataset:
Analysis of Emotion Recognition Model Using Electroencephalogram (EEG) Signals Based on Stimuli Text

Recognizing emotions through the brain wave approach with facial or sound expression is widely used, but few use text stimuli. Therefore, this study aims to analyze the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely Support Vector Machine and decision tree as benchmarks. The raw data used comes from the results of scrapping Twitter data. The dataset of emotional annotation was carried out manually based on four classifications, specifically: happiness, sadness, fear, and anger. The annotated dataset was tested using an Electroencephalogram (EEG) device attached to the participant's head to determine the brain waves appearing after reading the text. The results showed that the random forest model has the highest accuracy level with a rate of 98% which is slightly different from the decision tree with 88%. Meanwhile, in SVM the accuracy results are less good with a rate of 32%. Furthermore, the match level of angry emotions from the three models above during manual annotation and using the EEG device showed a high number with an average value above 90%, because reading with angry expressions is easier to perform.&#x0D; For this reason, this study aims to test the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely SVM and decision tree as benchmarks. The dataset used comes from the results of scrapping Twitter data.
The Effect of Literacy Skills on the Critical Thinking Skills of Mathematics Education Students

This study aims to see the effect of literacy skills on students' critical thinking skills. This research type is a correlational study to know the relationship between literacy skills and students' critical thinking skills. The total population is 168 students, and the level of precision is set at 10%. A sample of 63 respondents will be obtained, taken using a simple random sampling technique. The data analysis used in this study is a simple regression analysis with the product-moment correlation coefficient formula. The results showed that literacy skills positively correlate with students' critical thinking skills by 80%. It shows that besides literacy, there are still 20% of other factors that can affect students' critical thinking skills.
Secured Multi-Party Data Release on Cloud for Big Data Privacy-Preserving Using Fusion Learning

Previous computer protection analysis focuses on current data sets that do not have an update and need one-time releases. Serial data publishing on a complex data collection has only a little bit of literature, although it is not completely considered either. They cannot be used against various backgrounds or the usefulness of the publication of serial data is weak. A new generalization hypothesis is developed on the basis of a theoretical analysis, which effectively decreases the risk of re-publication of certain sensitive attributes. The results suggest that our higher anonymity and lower hiding rates were present in our algorithm. Design and Implementation of new proposed privacy preserving technique: In this phase proposed technique is implemented for demonstrating the entire scenario of data aggregation and their privacy preserving data mining. Comparative Production between the proposed technology and the traditional technology for the application of C.45: In this stage, the performance is evaluated and a comparative comparison with the standard algorithm for the proposed data mining security model is presented
Exploring the main factors driving a satisfactory use of the Moodle platform

E-learning platforms are largely used in educational institutions around the world, in hybrid, mixed or blended learning formats.The power of those platforms has been proved extensively in the period of the global pandemic being used as main frameworks for offering and achieving educational services.This work is an exploratory study aiming to analyze the contribution of the perceived ease of use, usefulness, and enjoyment to the satisfaction of using a Moodle e-learning platform.To do this, a structural modeling approach has been taken.The model testing results show that perceived enjoyment was the most important factor which suggests that, apart from being usable and useful, a learning platform should be also attractive and interesting.
Automated Evaluation ofTelugu Text Essays Using Latent Semantic Analysis

The most productive strategy to improve students' ability to write is to have direct and as much as possible teacher input. However, the workload of the teacher is greatly increased. Automated systems are increasingly required to help students write essays. In the field of educational assessment technology, automated test evaluation is becoming more and more common. We present a framework that is modelled on the programme, following which the school-teachers in the BPDAV School and Govt. High School Hyderabad, Telangana, India present the automatic evaluator of student essays in the Telugu language. Language skills; the structure of the essay and the contents that fit the subject are the principal requirements for evaluating the essays. In this context, we have established a scheme focused on latent semantical analysis and the theory of rhetorical structure. The method has been evaluated in more than 600 different essays, written in different manuscripts by schoolchildren. Overall 0.82 with the teacher's assessment was achieved in our method.
Scientific mentoring in the educational practice of a student of a pedagogical university

Based on the analysis of scientific research, the paper shows the essence of mentoring as a pedagogical phenomenon, examines the meaning and content of scientific mentoring, its role in the educational practice of a student of a pedagogical university.&#x0D; Modern approaches to mentoring in Russian pedagogical science allowed the author to review the content of scientific publications, to propose the following working definitions of the terms mentoring, scientific mentoring, mentor in education, mentoring of bachelors of teacher education.&#x0D; Relying on domestic and foreign sources, the author reveals the features of scientific mentoring in comparison with mentoring in other professional fields, focusing on the extremely wide coverage of the sphere of mentoring in the implementation of modern educational practices of a student of a pedagogical university.&#x0D; The actualization of scientific mentoring reflects the conceptual aspects of studying the historical and pedagogical heritage of teachers of the past in modern conditions by organizing an international competition for research and creative works of students In the world of wise thoughts of domestic and foreign scientists and teachers.&#x0D; In the paper, the author pays special attention to the popularization and continuity of humanistic ideas, the pedagogical heritage of the outstanding Russian doctor and pedagogical humanist thinker N.I. Pirogov in the system of modern higher education in the direction of scientific mentoring the management of scientific research activities of students in the educational practice of a pedagogical university (in the form of writing scientific competitive works of different levels).&#x0D; The authors vision of the practical significance of the study in rethinking the historical and pedagogical heritage from the standpoint of modern approaches of scientific mentoring as an effective mechanism for transferring experience and knowledge from leading scholars and mentors and student research work is substantiated.
Headcount of the Crowd in a Congested Scene

Crowd Counting and estimation of density is really challenging and an important problem if we visually analyze the crowd. Crowd Monitoring and Analyzing Crowd behavior has been an important aspect for every research field. A lot of already existing approaches use techniques based on regression on heat maps(density) to count people present in from a single frame. These techniques however cannot restrain an individual walking and further cannot approximate the original distribution of pedestrian in the locality. Whereas, detection-based techniques detect and restrain walking men’s in the frame, but the efficiency of these techniques challenged when implemented in high-density crowd situations. To get the better of the limitations of above-mentioned problem, we have used the (Congested Scene Recognition) Neural Network. By using this type of Neural network, we are able to visualize the detection and form density map according to produce accurate outputs for the given scene. The experimental outcomes of the successfully showcases the effectiveness of the approach used.
Social Media as a Student Career Selection Preference

The lives of the majority of students are currently absorbed by activities in social media such as Facebook, Instagram, WhatsApp, Twitter, and the others that allow a wide variety of both positive and negative information for the development in life and career selection activities.The purpose of this study is to find out how much the preference or tendency for students to use social media as a media literacy in career selection is.This article will reveal important data on the role of social media in influencing a student's career decision making.The research method used was a multistage random sampling technique involving 250 students from vocational high schools in Kudus Regency.The results of this study indicate that 61% of students use social media to find information that is used to consider advanced career selection.Meanwhile, the rest 12% chose a career based on their understanding of talent, 14% chose a career because of the opinions from parents or siblings, and 13% followed their friends and closest friends.This shows that social media have a significant role in helping students determine the future of their careers.
Multi‐scale capsule generative adversarial network for snow removal

Snowflakes captured on photos may severely decrease the visual quality and cause difficulties for vision analysis systems. Most noise removal frameworks are designed for de-raining or de-hazing, regarding rain or haze as translucent masks on clean images. However, snowflakes are different from them in terms of sizes, shapes, transparencies and floating trajectories, which decreases the performance of de-raining or de-hazing models in processing snowy images. In this work, we propose an effective multi-scale generative adversarial network framework for single-image snow removal, which is built with a multi-scale structure to identify various scales of snowflakes and a capsule-based structure to fuse the features extracted from the multi-scale encoding branches, so that different scaled features could be summarised and learnt by a joint framework. The overall framework is supervised by a weighted joint loss with an iterative training procedure to keep the training stability for the multi-branch-based structure. The experimental results demonstrate that our model outperforms the state-of-the-art comparisons.
VoiSeR: A New Benchmark for Voice-Based Search Refinement

Voice assistants, e.g., Alexa or Google Assistant, have dramatically improved in recent years.Supporting voice-based search, exploration, and refinement are fundamental tasks for voice assistants, and remain an open challenge.For example, when using voice to search an online shopping site, a user often needs to refine their search by some aspect or facet.This common user intent is usually available through a "filter-by" interface on online shopping websites, but is challenging to support naturally via voice, as the intent of refinements must be interpreted in the context of the original search, the initial results, and the available product catalogue facets.To our knowledge, no benchmark dataset exists for training or validating such contextual search understanding models.To bridge this gap, we introduce the first large-scale dataset of voicebased search refinements, VoiSeR, consisting of about 10,000 search refinement utterances, collected using a novel crowdsourcing task.These utterances are intended to refine a previous search, with respect to a search facet or attribute (e.g., brand, color, review rating, etc.), and are manually annotated with the specific intent.This paper reports qualitative and empirical insights into the most common and challenging types of refinements that a voicebased conversational search system must support.As we show, VoiSeR can support research in conversational query understanding, contextual user intent prediction, and other conversational search topics to facilitate the development of conversational search systems.
The Survey: Advances in Natural Language Processing using Deep Learning*

Natural Language Processing using Deep Learning is one of the critical areas of Artificial Intelligence to focus in the next decades. Over the last few years, Artificial intelligence had evolved by maturing critical areas in research and development. The latest developments in Natural Language Processing con- tributed to the successful implementation of machine translations, linguistic models, Speech recognitions, automatic text generations applications. This paper covers the recent advancements in Natural Language Processing using Deep Learning and some of the much-waited areas in NLP to look for in the next few years. The first section explains Deep Learning architecture, Natural Language Processing techniques followed by the second section that highlights the developments in NLP using Deep learning and the last part by concluding the critical takeaways from my article.
Customer Choice Modelling: A Multi-Level Consensus Clustering Approach

Customer Choice Modeling aims to model the decision-making process of customers, or segments of customers, through their choices and preferences identified by the analysis of their behaviors in one or more specific contexts. Clustering techniques are used in this context to identify patterns in their choices and preferences, to define segments of customers with similar behaviors, and to model how customers of different segments respond to competing products and offers. However, data clustering is an unsupervised learning task by nature, that is the grouping of customers with similar behaviors in clusters must be performed without prior knowledge about the nature and the number of intrinsic groups of data instances, i.e., customers, in the data space. Thus, the choice of both the clustering algorithm used and its parameterization, and of the evaluation method used to assess the relevance of the resulting clusters are central issues. Consensus clustering, or ensemble clustering, aims to solve these issues by combining the results of different clustering algorithms and parameterizations to generate a more robust and relevant final clustering result. We present a Multi-level Consensus Clustering approach combining the results of several clustering algorithmic configurations to generate a hierarchy of consensus clusters in which each cluster represents an agreement between different clustering results. A closed sets based approach is used to identified relevant agreements, and a graphical hierarchical representation of the consensus cluster construction process and their inclusion relationships is provided to the end-user. This approach was developed and experimented in travel industry context with Amadeus SAS. Experiments show how it can provide a better segmentation, and refine the customer segments by identifying relevant sub-segments represented as sub-clusters in the hierarchical representation, for Customer Choice Modeling. The clustering of travelers was able to distinguish relevant segments of customers with similar needs and desires (i.e., customers purchasing tickets according to different criteria, like price, duration of flight, lay-over time, etc.) and at different levels of precision, which is a major issue for improving the personalization of recommendations in flight search queries.
Lightweight Models for Multimodal Sequential Data.

Human language encompasses more than just text; it also conveys emotions through tone and gestures. We present a case study of three simple and efficient Transformer-based architectures for predicting sentiment and emotion in multimodal data. The Late Fusion model merges unimodal features to create a multimodal feature sequence, the Round Robin model iteratively combines bimodal features using cross-modal attention, and the Hybrid Fusion model combines trimodal and unimodal features together to form a final feature sequence for predicting sentiment. Our experiments show that our small models are effective and outperform the publicly released versions of much larger, state-of-the-art multimodal sentiment analysis systems.
Critical Success Factors and Challenges in Agile Requirements Engineering

Nowadays, the requirements of the software are changing rapidly in order to meet clients’ needs, which increases the complexity of developing software. Thus, Agile requirements engineering has arisen and it focuses on how to deal with the increasing changes in software requirements by gathering requirements iteratively and collaboratively with the clients. Thus, the clients’ satisfaction could be met more easily. On the other side, researchers have tried to improve agile requirements engineering from time to time, however, there are still limitations and challenges faced, which need more attention. Thus, this study is conducted by performing a systematic literature review technique to investigate the challenges, critical success factors, and the topics that need more attention in the agile requirements engineering field. In the beginning, the study obtained 178 articles related to this topic which were published from 2002 until 2019. After a thorough analysis of the articles, the study reviewed the ten (10) challenges and the proposed solutions that mentioned in the previous studies. Besides that, the study found six (6) critical success factors, and highlighted four (4) topics that need more attention from the researchers in future studies in agile requirements engineering.
Economic Culture And Media Aesthetics: Things In Common

The formation of economic culture is an urgent issue facing the mass media.The present article is devoted to the mass media implementation of the phenomena.The article examines the issue of the interaction between media aesthetics and economic culture.The research's theoretical and methodological basis is represented by a set of scientific studies devoted to researching various issues of modernization of domestic culture and economy, the interaction of economic institutions with the norms and values of culture, and media aesthetics and visual image.The research is based on the principles of rational knowledge -general philosophical, cultural, and general scientific principles of analysis, as well as comparison and generalization methods; and systemic method.The analysis of economic materials from the ria.ru website is carried out.The visual language is the most accurate in the transmission of economic information.The following observation is important since attention is focused not only on the visual presentation of materials but also on their emotional expression.The set of media aesthetic factors, including visual content design, undoubtedly affect the perception of economic information and, accordingly, the formation of economic culture.
T2NER: Transformers based Transfer Learning Framework for Named Entity Recognition

Recent advances in deep transformer models have achieved state-of-the-art in several natural language processing (NLP) tasks, whereas named entity recognition (NER) has traditionally benefited from long-short term memory (LSTM) networks.In this work, we present a Transformers based Transfer Learning framework for Named Entity Recognition (T2NER) created in PyTorch for the task of NER with deep transformer models.The framework is built upon the Transformers library as the core modeling engine and supports several transfer learning scenarios from sequential transfer to domain adaptation, multi-task learning, and semi-supervised learning.It aims to bridge the gap between the algorithmic advances in these areas by combining them with the state-of-theart in transformer models to provide a unified platform that is readily extensible and can be used for both the transfer learning research in NER, and for real-world applications.The framework is available at: https://github.com/suamin/t2ner.
Analysis of Bitcoin Prices using Deep Learning Techniques

Project training - these projects are done for specific subjects without discussing methods that require knowledge and level of technical material. This paper discusses the design and implementation of project training in information technology as a major project that requires student creativity and describes the project clearly and concisely. These projects not only develop basic or technical skills but also practical skills. The goal of the modern student project is to show how trained machine models can predict cryptocurrency security if we allow appropriate levels of information and computing power. The diagram shows the approximate value. Conventional technology is way out of technology that can help people predict future events. With so much data being generated and stored daily, we are finally approaching an age where predictions are made from accurate and accurate data. Besides, with the advent of the digital currency era, more and more leaders have entered the digital investment market. This allows us to create models that can predict cryptocurrencies, especially Bitcoin. This can be done through several types of machine learning methods and in-depth learning and learning methods.
Voice Biometric: A Novel and Realistic Approach

Speaker identification uses the basic speaker's wave information to recognize the speaker. The device validates the speaker's identity, which makes the person eligible for the various services the voice can provide. This will fortify every device. Attributed voice is an algorithm focused on a speaker's physiological and behavioral characteristics. Speech analysis provides it with the distinguishing characteristics of identity, allowing the speaker to be distinguished from the others.
A Python package for text processing for Serbian: nlpheart

Within the past two decades, text processing became an important part of most state-of-the-art advanced automation systems. However, for many under-resourced languages it is still challenging to perform textual data preparation, due to the lack of adequate tools. In this work, a python package for text processing for Serbian called nlpheart is presented. This package has been developed in industry and it is planned to be released as an open-source text processing tool for Serbian for academic purposes as well.