Reviewerly/exHarmony huggingface dataset:
Text Classification based on Discriminative Semantic Features and Variance of Fuzzy Similarity

Due to the rapid growth of the Internet, large amounts of unlabelled textual data are producing daily. Clearly, finding the subject of a text document is a primary source of information in the text processing applications. In this paper, a text classification method is presented and evaluated for Persian and English. The proposed technique utilizes variance of fuzzy similarity besides discriminative and semantic feature selection methods. Discriminative features are those that distinguish categories with higher power and the concept of semantic feature takes into the calculations the similarity between features and documents by using only available documents. In the proposed method, incorporating fuzzy weighting as a measure of similarity is presented. The fuzzy weights are derived from the concept of fuzzy similarity which is defined as the variance of membership values of a document to all categories in the way that with some membership value at the same time, the sum of these membership values should be equal to 1. The proposed document classification method is evaluated on three datasets (one Persian and two English datasets) and two classification methods, support vector machine (SVM) and artificial neural network (ANN), are used. Comparing the results with other text classification methods, demonstrate the consistent superiority of the proposed technique in all cases. The weighted average F-measure of our method are %82 and %97.8 in the classification of Persian and English documents, respectively.
A Financial Literacy AI-Enabled Voice Assistant System for Educational Use

Financial literacy is crucial for saving money, avoiding debt, establishing strong credit, and many other skills that help build wealth throughout an individual's life. A very large percentage of Americans from various demographics and backgrounds do not have the basic financial and economic knowledge to sustain themselves financially. Our proposed solution to tackle financial illiteracy is by ensuring students are taught the foundational expertise at a young age so that they make wise financial choices by the time they reach adulthood. We have developed a virtual voice assistant that will improve financial literacy by offering lessons that will cover all topics within the National Standards in K-12 Personal Finance Education educational curricula. Data was collected and analyzed in order to assess the effectiveness, robustness, and engagement of the voice assistant. While further analysis on engagement should be conducted, the bot met baseline goals of effectiveness and robustness which can further be improved through more intent training and testing on potential users.
Systematization of the evaluation model of vocabulary control in repositories: research report with the Unesp Institutional Repository

Introduction: Vocabulary control in information resource storage, treatment and retrieval systems is necessary to obtain consistency between indexing and retrieval in order to avoid informational dispersion. Digital repositories in universities are currently fundamentalin the organization and management of knowledge generated by scientific, technological, artistic, and administrative production, however, it is necessary to verify the availability of controlled vocabulary and how vocabulary control is carried out. Objective: With the objective of systematizing a proposal for vocabulary control and the use of controlled vocabularies in university repositories managed by libraries, an evaluation model was developed that proposes to systematize methods, procedures, resources, and techniques. Methodology: For this, the development of the investigation carried out exploratory research with bibliographic and documentary research and applied research in the Unesp Repository. Results: The results obtained constituted an Action Plan, discussed and elaborated by the Study Group, composed of six actions and nine studies to evaluate and control vocabulary in university repositories about: vocabulary control in indexing by professionals and non-professionals; vocabulary control in retrieval; use of subject metadata from academic papers; keyword matching; analysis of terminological variations at semantic, syntactic and pragmatic levels; analysis of transaction logs for searches by subject. Conclusion: Itis concluded that the systematization of actions in an evaluation model is relevant for university repositories to incorporate the advances offered by vocabulary control in their routines and, mainly, for the contribution of new terms arising from scientific and technological evolution
Superadditivity and Convex Optimization for Globally Optimal Cell Segmentation Using Deformable Shape Models

Cell nuclei segmentation is challenging due to shape variation and closely clustered or partially overlapping objects. Most previous methods are not globally optimal, limited to elliptical models, or are computationally expensive. In this work, we introduce a globally optimal approach based on deformable shape models and global energy minimization for cell nuclei segmentation and cluster splitting. We propose an implicit parameterization of deformable shape models and show that it leads to a <i>convex</i> energy. Convex energy minimization yields the <i>global</i> solution independently of the initialization, is fast, and robust. To <i>jointly</i> perform cell nuclei segmentation and cluster splitting, we developed a novel iterative global energy minimization method, which leverages the inherent property of <i>superadditivity</i> of the convex energy. This property exploits the lower bound of the energy of the union of the models and improves the computational efficiency. Our method provably determines a solution close to global optimality. In addition, we derive a closed/form solution of the proposed global minimization based on the superadditivity property for non/clustered cell nuclei. We evaluated our method using fluorescence microscopy images of five different cell types comprising various challenges, and performed a quantitative comparison with previous methods. Our method achieved state/of/the/art or improved performance.
Emotion-Aware Explainable Artificial Intelligence for Personality, Emotion, and Mood Simulation

This paper is twofold. First, the authors analysed the state of the art on the explainable artificial intelligence (XAI) applied to affective computing, emotion analysis, and sentiment analysis. Second, based on Kaptein’s approach and using the personality, emotion, and mood model proposed by Egges, the authors tested an Emotion-aware XAI framework to determine the affective state of predefined-behaviour autonomous entities in uncertainty contexts. The affective state analysis of eight selected formal use cases was divided into four stages: personality settings, event evaluation, emotions and moods generation, and results classification. Findings suggest that the affective state depends on the type of predominant personality and the influence of the events in the environment.
Evaluation and analysis of digital language archives development platforms: a parametric approach

Purpose While the obstacles of archiving endangered languages are significant, the question of which platform is best for building a digital language archive is constantly present. The purpose of this study is to evaluate and analyse digital language archives development platforms, such as content management systems (CMSs), digital repositories and archival collections management systems (ACMSs) using parameters that have been specified. The authors selected Mukurtu CMS, which is based on Drupal CMS; DSpace as the digital repository software; and ArchivesSpace as an ACMS in this study. Design/methodology/approach The current research is supported by a study of the literature and a detailed exploration of different systems used to develop digital language archives. The whole research is carried out in three steps: literature searching; identification of relevant literature; and parameter identification, exploration of tools and data reporting and analysis. Findings Following the technical and feature analysis of these tools, it can be concluded that they are more or less comparable, as well as constantly evolving, updating and having a bigger community base. It may be determined that DSpace is the most popular platform, but the other two, particularly ArchivesSpaces, are fierce competitors. Research limitations/implications This study outlines the technical prerequisites for creating a digital language archive, which will be useful to IT personnel working on these projects. The research is also useful for tool developers as it allows them to incorporate missing functionality and technical standards by comparing them to alternatives. The parameters established in this study can be used for similar studies in other domains, as well as for evaluating existing digital language archives. Practical implications The findings of this study have broad practical implications, and they can assist archivists, linguists, language communities and library and information science professionals in choosing an appropriate platform for building a digital language archive. Originality/value This study finds that there is relatively little effort made towards reviewing digital language archiving and the systems that are used to do it; thus, this study is carried out to assess and analyse digital language archive creation systems based on defined parameters. The parameters were discovered through a combination of the available literature and tool discovery. Using a parametric approach to evaluate tools yields unique insights and quickly reveals system flaws.
Interactive Music Teaching Method Based on Big Data and Cloud Computing

With the development of contemporary technology and education, there are more and more innovative teaching methods that are helpful to the education and teaching procedure. Interactive teaching method is one of them. Interactive teaching is a teaching modus based on developed on the scaffolding teaching theory that attaches importance to the communication between teachers and learners. Its biggest goal is to develop learner-specific and specific autonomous learning strategies through the communication and collaboration between the teacher and the learner. The advanced nature and effectiveness of interactive teaching methods make it more and more widely used in education, such as in the field of music teaching. Music teaching is a kind of teaching that pays more attention to the communication between teachers and Teachees. This paper aims to study the interactive music teaching method based on BD and CC, that is to design an interactive music teaching method based on big data and cloud computing technology, and conduct practical application experiments on it. The experiment concluded that the interactive music teaching method based on BD and CC has made the students’ participation rate in music teaching classrooms reach 88%, and the excellent rate of in-class test scores has reached 90%.
Design and Management of Microteaching Mode of Innovation and Entrepreneurship Education in Colleges and Universities Driven by Big Data

There have been few studies on the quality assessment at the meso- or microlevel in my country’s colleges and universities despite the fact that innovation and entrepreneurship (IAE) education is constantly progressing. A new assessment method based on big data is employed in this paper to investigate the design and management of microteaching modes for IAE education and to promote the development of IAE at these institutions while also enhancing the quality of innovative talent training. This paper addresses the urgent need to reform the evaluation system for IAE education. This paper has completed the following work: (1) this paper firstly analyzes the current situation of IAE education evaluation at home and abroad on the basis of literature research and summarizes the current theoretical thinking, methods, and shortcomings of IAE education evaluation. (2) Analyzing the present condition of IAE education assessment, relevant ANN technologies are introduced, and a suitable quality index system for IAE education is created. (3) With the use of big data, we were able to gather samples and data sets for the experiment, and then, we utilized the BP algorithm and the GA-BP algorithm to compare their accuracy in predicting the quality of IAE education, and the GA-BP algorithm has better performance.
Dampak Game Online terhadap Pemerolehan Bahasa Kedua pada Anak

Currently playing online games is one of the hobbies that is being carried out by all circles, including children. In addition to entertainment, online games are a means of learning for children to acquire foreign language vocabulary that they do not learn at school. This study aims to see the effect of online games on children's second language acquisition and to find out what factors influence this acquisition. The research method we use is a qualitative descriptive method with an observational approach. The subjects of our study were elementary school children aged 6 and 10 years. We conducted observations and interviews for one month so that we could find out how the influence of online games on second language acquisition in children aged 8 years and 11 years. The games played are Free fire, Mobile Legends, Minecraft, and Roblox. The results we get are that the children are familiar with new English vocabulary and know their meaning, the English vocabulary obtained is error, lag, battle, build, room, by one, cheat, shoot, and the other.
Improving Privacy-preserving Healthcare Data Sharing in a Cloud Environment Using Hybrid Encryption

In recent years, cloud computing has been widely used in various fields and is gaining importance in healthcare systems.Patients' health data are outsourced to cloud storage, enabling healthcare professionals to easily access health information from anywhere and at any time to improve health services.Once patient data are stored in the cloud, they are vulnerable to attacks such as data loss, denial of service (DoS), distributed denial of service (DDoS) and other sorts of cyberattacks.Data confidentiality and patient privacy are more of a problem in the cloud computing context due to their public availability.If a patient's personal information is stolen, he or she may face a range of problems.These are concerns that necessitate more security.The transmission of this sensitive information over the internet is always susceptible to hacking.Therefore, the privacy of patients' data is considered one of healthcare organizations' main issues.To overcome this problem, encryption mechanisms that place a significant emphasis on securing data within the cloud environment are used to preserve sensitive health data.A hybrid cryptography approach is employed in this paper to ensure the secure sharing of health data over the cloud.To maintain data privacy and secrecy, a hybrid cryptography mechanism for storing and transporting data to and from the cloud is used.To protect data from malevolent insiders, the encryption key is separated into two halves, controlling access to patient records via a specific technique.This paper shows the implementation and performance evaluation of the proposal as a functional system prototype.The evaluation is based on the key generation time, the record encryption time, the record decryption time, the record upload time and the record download time for different user numbers and different file sizes varying from 0.1 MB to 500 MB.The findings show that the proposal performs better than other state-of-the-art systems and can practically share secure health data in cloud environments.
On selection of topics that users are interested in but are not familiar with

In this research, we constructed a topic selection system in order to provide “topics that users are interested in and are not familiar with” from the dialogue system side. To achieve this goal, we used a concept distance to provide topics that the user is not familiar with, and improved the accuracy by removing those with a short conceptual distance. Many words in the conversation are not included in the thesaurus dictionary. We constructed a conceptual distance measurement model using machine learning so that the conceptual distance can be measured even for words that are not included in the thesaurus dictionary. As a result of the subject experiment, it was confirmed that the proposal system can be used to provide topics that the user is interested in and is not familiar with.
Big Data Value Calculation Method Based on Particle Swarm Optimization Algorithm

SI is a relatively recent technology that was inspired by observations of natural social insects and artificial systems. This system comprises multiple individual agents who rely on collective behavior in decentralized and self-organized networks. One of the biggest difficulties for existing computer techniques is learning from such large datasets, which is addressed utilizing big data. Big data-based categorization refers to the challenge of determining which set of classifications a new discovery belongs to. This classification is based on a training set of data that comprises observations that have been assigned to a certain category. In this paper, CIN-big data value calculation based on particle swarm optimization (BD-PSO) algorithm is proposed by operating in local optima and to improve the operating efficiency. The convergence speed of the particle swarm optimization (PSO), which operates in the local optima, is improved by big data-based particle swarm optimization (BD-PSO). It improves computing efficiency by improving the method, resulting in a reduction in calculation time. The performance of the BD-PSO is tested on four benchmark dataset, which is taken from the UCI. The datasets used for evaluation are wine, iris, blood transfusion, and zoo. SVM and CG-CNB are the two existing methods used for the comparison of BD-PSO. It achieves 92% of accuracy, 92% of precision, 92% of recall, and 1.34 of F1 measure, and time taken for execution is 149 ms, which in turn outperforms the existing approaches. It achieves robust solutions and identifies appropriate intelligent technique related to the optimization problem.
Anomaly Detection of Smart Grid Equipment Using Machine Learning Applications

Many application systems in today’s smart grid network comprise a variety of middleware components, such as vibrating or spinning electrical equipment, data bases, storage, caches, and identification services, among others. Each component is a discrete bundle of physical or virtual computers that will generate a large amount of data in the form of logs and metrics, and failure of these high-vibrating machines will result in the system’s entire shutdown. As a result, the condition monitoring system for this smart grid equipment is more dependable and efficient in predicting the machine’s health ahead of time. To analyze the data and derive any inferences for future analysis and anomaly detection, we’ll need a separate system, which will take longer to handle data given by each component and will demand additional processing resources. As a result, in this chapter, machine learning methods are used to identify abnormalities or condition monitoring for smart grid equipment and machines. Here, we used two separate methods: multivariate statistical analysis by calculating Mob distance and auto encoders, which is an artificial neural network approach. Furthermore, the findings demonstrate that these apps are effective in identifying anomaly ahead of time, i.e. before a few days.
Employing Bibliometric Analysis to Identify Emerging Technologies in the Insurance Industry

Purpose: This chapter aims to review the research literature on the insurance industry and map the emerging research trends in this field through a bibliometric analysis and network visualisation exercise.Design/methodology/approach: The research literature gathered from the Web of Science (WoS) databases was applied to bibliometric analysis in this article. With the help of Biblioshiny, this research was utilised to identify documents, most prolific institutions, countries, resource titles, and WoS categories in the insurance industry. In addition, bibliometric mapping was used to identify national and institutional collaboration networks.Findings: The author discovered that the literature had increased drastically in the academic discourse during the last two decades. According to the bibliometric data, developed countries such as the United States and the United Kingdom reign research in this sector. The research highlights the most prominent studies and writers in the insurance field and the evolution of the domain from its inception to the contemporary. It also highlights theoretical disagreements and contradictions between theoretical conceptualisation and empirical measures by presenting the significant concerns in the literature.Originality/value: This chapter delivers the first comprehensive bibliometric analysis of the insurance sector’s literature production in connection to emerging technology, which will aid researchers and practitioners in better understanding the relationships between themes and outsiders to understand the domain area better. The author makes recommendations for future perspectives study directions and highlights the critical conceptual framework that can build future research. Overall, this research contributes to a better understanding of the insurance industry and offers new perspectives.
Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning

This paper proposes a new sequential model learning architecture to solve partially observable Markov decision problems. Rather than compressing sequential information at every timestep as in conventional recurrent neural network-based methods, the proposed architecture generates a latent variable in each data block with a length of multiple timesteps and passes the most relevant information to the next block for policy optimization. The proposed blockwise sequential model is implemented based on self-attention, making the model capable of detailed sequential learning in partial observable settings. The proposed model builds an additional learning network to efficiently implement gradient estimation by using self-normalized importance sampling, which does not require the complex blockwise input data reconstruction in the model learning. Numerical results show that the proposed method significantly outperforms previous methods in various partially observable environments.
e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce

Understanding vision and language representations of product content is vital for search and recommendation applications in e-commerce. As a backbone for online shopping platforms and inspired by the recent success in representation learning research, we propose a contrastive learning framework that aligns language and visual models using unlabeled raw product text and images. We present techniques we used to train large-scale representation learning models and share solutions that address domain-specific challenges. We study the performance using our pre-trained model as backbones for diverse downstream tasks, including category classification, attribute extraction, product matching, product clustering, and adult product recognition. Experimental results show that our proposed method outperforms the baseline in each downstream task regarding both single modality and multiple modalities.