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Cyberbullying poses a digital threat to society. In this survey, we explain what cyberbullying is and its various forms. We focus on social media platforms and instant messaging apps that are susceptible to cyberbullying, discussing how we can identify such behavior in these spaces. Moving on, we conduct a systematic review of publicly available datasets in different languages, exploring techniques for data preprocessing, feature representation, and methodologies used in textual analysis for cyberbullying detection. We specifically look at natural language-based and platform-specific preprocessing methods. We also cover popular feature representation techniques like sentiment analysis, user information, text summarization, symbols, images, and word embedding for detecting cyberbullying. Next, we categorize existing techniques, including machine learning and neural networks, highlighting research gaps. Additionally, we discuss the challenges associated with current datasets and methods. This survey aims to provide early researchers with insights into cyberbullying literature and guide them in exploring potential research directions. • Analysis of prior literature for cyberbullying detection. • Proposes possible future research directions based on identified research challenges. • Identifies the need for high-quality datasets with contextual information. • Develop mitigation systems relevant in real-time. [ABSTRACT FROM AUTHOR]

The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural Language Processing (NLP) during the past decade. However, the demands of long document analysis are quite different from those of shorter texts, while the ever increasing size of documents uploaded online renders automated understanding of lengthy texts a critical issue. Relevant applications include automated Web mining, legal document review, medical records analysis, financial reports analysis, contract management, environmental impact assessment, news aggregation, etc. Despite the relatively recent development of efficient algorithms for analyzing long documents, practical tools in this field are currently flourishing. This article serves as an entry point into this dynamic domain and aims to achieve two objectives. First of all, it provides an introductory overview of the relevant neural building blocks, serving as a concise tutorial for the field. Secondly, it offers a brief examination of the current state-of-the-art in two key long document analysis tasks: document classification and document summarization. Sentiment analysis for long texts is also covered, since it is typically treated as a particular case of document classification. Consequently, this article presents an introductory exploration of document-level analysis, addressing the primary challenges, concerns, and existing solutions. Finally, it offers a concise definition of "long text/document", presents an original overarching taxonomy of common deep neural methods for long document analysis and lists publicly available annotated datasets that can facilitate further research in this area. [ABSTRACT FROM AUTHOR]

Mathematical teaching techniques encompass a diverse range of approaches for students to grasp mathematical concepts. Currently, various mathematical teaching techniques exist, and it is crucial to extract personalized teaching techniques based on students' knowledge level. Cognitive Computing systems in the learning domain open a substantial range of possible alternatives, thereby helping both students and teachers. These systems enhance the human cognitive capabilities by developing cognitive assistant, which is a flexible and adaptive learning software and a continuous learning system. In this paper, a personalized cognitive layered framework is proposed to map the student's reasoning level with the mathematical teaching techniques. The analysis is done with real time data collected from the School for Grades 1 to 6 and mathematical teaching techniques extracted from mathematical research articles. The Hypothesis Evidence Extraction (HypoEE) algorithm is proposed to extract optimal teaching techniques. The proposed system follows the pipeline of cognitive computing processes such as extraction of factors of importance, knowledge assertion graph construction, node prioritization, correlation extraction, text summarization, hypothesis identification, evidence extraction and scoring, hypothesis refinement, inference and decision formulation. The system is evaluated using performance metrics and cognitive metrics. The validation is done by three mathematical subject experts. The results showcase a Confidence Weighted Score (CWS) of 0.96, an accuracy of 0.994, 0.92 precision, 0.88 recall, 0.89 F1 score, 0.83 specificity and cognitive metric scores indicating a taskability of 0.89, 0.86 knowledge capacity and 0.89 knowledge utilization. [Display omitted] • A personalized cognitive layered framework for deriving the most effective and personalized mathematical teaching strategy. • A cognitive system driven by evidence-based inference to personalize education. • A text summarization algorithm crafted to produce concise summaries from large volumes of text. • Application of the Hypothesis Evidence Extraction (HypoEE) algorithm to extract optimal mathematical teaching techniques based on individual reasoning levels. • Analyzing performance through expert assessments and cognitive evaluation metrics. [ABSTRACT FROM AUTHOR]

Cyberbullying poses a digital threat to society. In this survey, we explain what cyberbullying is and its various forms. We focus on social media platforms and instant messaging apps that are susceptible to cyberbullying, discussing how we can identify such behavior in these spaces. Moving on, we conduct a systematic review of publicly available datasets in different languages, exploring techniques for data preprocessing, feature representation, and methodologies used in textual analysis for cyberbullying detection. We specifically look at natural language-based and platform-specific preprocessing methods. We also cover popular feature representation techniques like sentiment analysis, user information, text summarization, symbols, images, and word embedding for detecting cyberbullying. Next, we categorize existing techniques, including machine learning and neural networks, highlighting research gaps. Additionally, we discuss the challenges associated with current datasets and methods. This survey aims to provide early researchers with insights into cyberbullying literature and guide them in exploring potential research directions. • Analysis of prior literature for cyberbullying detection. • Proposes possible future research directions based on identified research challenges. • Identifies the need for high-quality datasets with contextual information. • Develop mitigation systems relevant in real-time. [ABSTRACT FROM AUTHOR]

Mathematical teaching techniques encompass a diverse range of approaches for students to grasp mathematical concepts. Currently, various mathematical teaching techniques exist, and it is crucial to extract personalized teaching techniques based on students' knowledge level. Cognitive Computing systems in the learning domain open a substantial range of possible alternatives, thereby helping both students and teachers. These systems enhance the human cognitive capabilities by developing cognitive assistant, which is a flexible and adaptive learning software and a continuous learning system. In this paper, a personalized cognitive layered framework is proposed to map the student's reasoning level with the mathematical teaching techniques. The analysis is done with real time data collected from the School for Grades 1 to 6 and mathematical teaching techniques extracted from mathematical research articles. The Hypothesis Evidence Extraction (HypoEE) algorithm is proposed to extract optimal teaching techniques. The proposed system follows the pipeline of cognitive computing processes such as extraction of factors of importance, knowledge assertion graph construction, node prioritization, correlation extraction, text summarization, hypothesis identification, evidence extraction and scoring, hypothesis refinement, inference and decision formulation. The system is evaluated using performance metrics and cognitive metrics. The validation is done by three mathematical subject experts. The results showcase a Confidence Weighted Score (CWS) of 0.96, an accuracy of 0.994, 0.92 precision, 0.88 recall, 0.89 F1 score, 0.83 specificity and cognitive metric scores indicating a taskability of 0.89, 0.86 knowledge capacity and 0.89 knowledge utilization. [Display omitted] • A personalized cognitive layered framework for deriving the most effective and personalized mathematical teaching strategy. • A cognitive system driven by evidence-based inference to personalize education. • A text summarization algorithm crafted to produce concise summaries from large volumes of text. • Application of the Hypothesis Evidence Extraction (HypoEE) algorithm to extract optimal mathematical teaching techniques based on individual reasoning levels. • Analyzing performance through expert assessments and cognitive evaluation metrics. [ABSTRACT FROM AUTHOR]

Automatic text summarization plays a vital role in text retrieval. However, very little interest, as well as attention to Hindi text summarization and the wide use of hybridization of an evolutionary and swarm-based method for solving optimization problems, motivates us to develop a technique for achieving better results. We have used a combination of the Title feature, Sentence length, Sentence position, Numerical Data, Thematic word, Proper Noun and Term frequency & Inverse Sentence Frequency to find the results. The proposed BPSO-IGA comprising Binary Particle Swarm Optimization (BPSO) and an improved genetic algorithm finds a well-formatted summary from multiple documents. The method for calculating the score of the sentence is proposed initially. A combination of BPSO and IGA finds the features' optimal scores and then is used to generate the final summary. The proposed method is compared with six well-known techniques, SummaRuNNer, ChunkRank, TGraph, UniRank, FF-SE, and MDS-ACS, on FIRE 2011 Hindi dataset. To strengthen the hypothesis that all seven features are required in the summary generation, the summary is also generated using only ONE and a combination of more than one feature. It is found that the best summary is generated when all seven features are considered. The BPSO-IGA performs best among all the methods for precision, recall, f-measure, cohesion, and non-redundancy. This research aims to design a multi-document extractive text summarizer for Hindi documents using a swarm intelligence and evolutionary algorithm-based hybrid approach. [ABSTRACT FROM AUTHOR]