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Honoured as a Nobel Laureate in 2016, Bob Dylan's song lyrics have garnered well-deserved recognition and appreciation for their themes, content and artistic performances. Part-of-speech characteristics are effective in denoting stylistic features of texts in stylometric studies. The present study carried out a quantitative observation of stylistic features of Dylan's lyrics. Specifically, the study focuses on parts of speech like verbs, adjectives, adverbs, nouns and pronouns. Results of the present study reveal that: (1) Based on the distribution of verbs and adjectives, Dylan's lyrics are significantly active texts, and the activity sequences (Q-sequences) have validated the result in a dynamic way; (2) Dylan's lyrics tend to present the characteristics of a written body; (3) Individualism is prominent in Dylan's lyrics accompanied by the wide use of the first-person singular pronouns. [ABSTRACT FROM AUTHOR]

The automatic identification of code authors based on their programming styles—known as authorship attribution or code stylometry—has become possible in recent years thanks to improvements in machine learning-based techniques for author recognition. Once feasible at scale, code stylometry can be used for well-intended or malevolent activities, including: identifying the most expert coworker on a piece of code (if authorship information goes missing); fingerprinting open source developers to pitch them unsolicited job offers; de-anonymizing developers of illegal software to pursue them. Depending on their respective goals, stakeholders have an interest in making code stylometry either more or less effective. To inform these decisions we investigate how the accuracy of code stylometry is impacted by two common software development activities: code formatting and code minification. We perform code stylometry on Python code from the Google Code Jam dataset (59 authors) using a code2vec-based author classifier on concrete syntax tree (CST) representations of input source files. We conduct the experiment using both CSTs and ASTs (abstract syntax trees). We compare the respective classification accuracies on: (1) the original dataset, (2) the dataset formatted with Black, and (3) the dataset minified with Python Minifier. Our results show that: (1) CST-based stylometry performs better than AST-based (51.00%→68%), (2) code formatting makes a significant dent (15%) in code stylometry accuracy (68%→53%), with minification subtracting a further 3% (68%→50%). While the accuracy reduction is significant for both code formatting and minification, neither is enough to make developers non-recognizable via code stylometry. [ABSTRACT FROM AUTHOR]

Phishing is the most common and potentially dangerous cyber attack that organizations are forced to deal with on a constant basis, rendering its automated detection as early as possible a necessity to ensure the security of computer systems. Focusing on the email level, this work improves content-based phishing email detection by integrating stylometric features with the commonly-used vectorization techniques, as well as by utilizing classifier stacking. Leveraging a diverse set of stylometric features, we systematically explore different methods of combining them with vectorized text as well as multiple stacking configurations for the machine learning algorithms. Our findings demonstrate that the proposed methods consistently outperform vectorization-only baselines on an imbalanced dataset, with a smaller improvement to a balanced one. Specifically, we achieved an F 1 measure of 0.9843 on the balanced set and 0.9656 on the imbalanced one by stacking multiple different classifiers that were trained on the content and stylometric features separately, improving baselines by more than 2.2% for the imbalanced dataset. As such, our work contributes to the ongoing efforts in cybersecurity by further enhancing the performance of state-of-the-art phishing email detection systems. [ABSTRACT FROM AUTHOR]

Within the literary domain, genres function as fundamental organizing concepts that provide readers, publishers, and academics with a unified framework. Genres are discrete categories that are distinguished by common stylistic, thematic, and structural components. They facilitate the categorization process and improve our understanding of a wide range of literary expressions. In this paper, we introduce a new dataset for genre classification of Russian books, covering 11 literary genres. We also perform dataset evaluation for the tasks of binary and multi-class genre identification. Through extensive experimentation and analysis, we explore the effectiveness of different text representations, including stylometric features, in genre classification. Our findings clarify the challenges present in classifying Russian literature by genre, revealing insights into the performance of different models across various genres. Furthermore, we address several research questions regarding the difficulty of multi-class classification compared to binary classification, and the impact of stylometric features on classification accuracy. [ABSTRACT FROM AUTHOR]

This article addresses the problematic authorship of The Constitutions of the Free-Masons (1723). Traditionally associated with James Anderson, using stylometry, we examine whether and, if so, where John T. Desaguliers, the prime mover of early English institutionalized Freemasonry, contributed to this publication. Our corpus includes writings by Anderson, Desaguliers, and two contemporary Freemasons used as distractors. The transcribed works contain texts from different genres and of varying lengths. In our methodology, we employ a wide range of robust, multivariate, unsupervised, and cross-validated supervised tests, verified through significance testing, which can hopefully contribute to the establishment of standards for historical authorship attribution. Our results suggest, in line with historical evidence, that the legendary history of the Constitutions was most likely primarily authored by Anderson. However, several of the Charges including the first one ' Concerning God and religion', one of the most disputed texts in the history of Freemasonry, are closer to the style of Desaguliers. The General Regulations concerning the organization of the lodges, hitherto attributed to George Payne, played a fundamental role in spreading Freemasonry worldwide. Our analyses show that the stylistic affinity of fifteen of the thirty-nine regulations has a pronounced closeness to Anderson's style, five align more closely with Desaguliers' style. The authorship of the rest remains inconclusive partly due to the insufficient length of texts by Payne. These novel findings are also supported by a close reading of the Constitutions and other contemporary primary sources. [ABSTRACT FROM AUTHOR]

The automatic assignment of a text to one or more predefined categories presents multiple applications. In this context, the current study focuses on author attribution in which the true author of a doubtful text must be identified. This analysis focuses on the style of sixty-six French comedies in verse written by seventeen supposed authors during the 17th century. The hypothesis we want to verify assumes that the real author is the name appearing on the cover (called the signature hypothesis). In order to validate the reliability of two attribution procedures, we used two additional corpora based on 200 extracts of novels written in French, with thirty authors and 140 Italian novels authored by forty persons. After this verification, we propose an improvement of the Delta method as well as a new analysis grid for this model. Finally, we applied these approaches to our French comedy corpus. The results demonstrate that the signature hypothesis must be discarded. Moreover, these works present similar styles, making any attribution difficult to support with a high degree of certainty. [ABSTRACT FROM AUTHOR]

The dialogue of a large number of American feature films of the last 30 years is analysed with the stylometric tools contained in the R-stylo package. Various interesting results showing the capabilities and restrictions of this statistical package emerge. [ABSTRACT FROM AUTHOR]

The article is devoted to the stylometric analysis of stories written by Jorge Luis Borges and Adolfo Bioy Casares under the common pseudonym Honorio Bustos Domecq. The work poses two questions: 1) Is Bustos Domecq’s style different from the writing style of Borges and Bioy Casares? 2) What is the share of influence of each of the co-authors on the formation of Bustos Domecq's style? To solve research problems, it is used Delta method, one of the most reliable stylometric tools to date; as well as the support vector machine, a common machine learning method that is used to solve classification problems. It turns out that Bustos Domecq's style differs from the style of the stories of Borges and Bioy Casares. However, in the texts of Bustos Domecq the authorial signal of Bioy Casares predominates, which is revealed on the basis of both stylometric and historical-literary analysis. The influence of Borges is more clearly manifested only in the first stories (1940s), when Bioy Caceres is a literary disciple of Borges, and in the second half of the 1960s, at a time of serious emotional upheavals for Borges, as well as his world recognition. Analysis of the topics of the stories, where Borges's authorial signal predominates, also confirms the results of stylometric experiments: the story “The Long Search for Tai An”, as well as the first texts of the collection The Chronicles of Bustos Domec “Tribute to Cesar Paladion”, “An Evening with Ramon Bonavena” and others really correspond to the poetics of Borges.

Supervised discretisation is widely considered as far more advantageous than unsupervised transformation of attributes, because it helps to preserve the informative content of a variable, which is useful in classification. After discretisation, based on employed criteria, some attributes can be found irrelevant, and all their values can be represented in a discrete domain by a single interval. In consequence, such attributes are removed from considerations, and no knowledge is mined from them. The paper presents research focused on extended transformations of attribute values, thus combining supervised with unsupervised discretisation strategies. For all variables with single intervals returned from supervised algorithms, the ranges of values were transformed by unsupervised methods with varying numbers of bins. Resulting variants of the data were subjected to selected data mining techniques, and the performance of a group of classifiers was evaluated and compared. The experiments were performed on a stylometric task of authorship attribution. [ABSTRACT FROM AUTHOR]

The nature of the input features is one of the key factors indicating what kind of tools, methods, or approaches can be used in a knowledge discovery process. Depending on the characteristics of the available attributes, some techniques could lead to unsatisfactory performance or even may not proceed at all without additional preprocessing steps. The types of variables and their domains affect performance. Any changes to their form can influence it as well, or even enable some learners. On the other hand, the relevance of features for a task constitutes another element with a noticeable impact on data exploration. The importance of attributes can be estimated through the application of mechanisms belonging to the feature selection and reduction area, such as rankings. In the described research framework, the data form was conditioned on relevance by the proposed procedure of gradual discretisation controlled by a ranking of attributes. Supervised and unsupervised discretisation methods were employed to the datasets from the stylometric domain and the task of binary authorship attribution. For the selected classifiers, extensive tests were performed and they indicated many cases of enhanced prediction for partially discretised datasets. [ABSTRACT FROM AUTHOR]

Software development teams establish elaborate continuous integration pipelines containing automated test cases to accelerate the development process of software. Automated tests help to verify the correctness of code modifications decreasing the response time to changing requirements. However, when the software teams do not track the performance impact of pending modifications, they may need to spend considerable time refactoring existing code. This article presents PACE, a program analysis framework that provides continuous feedback on the performance impact of pending code updates. We design performance microbenchmarks by mapping the execution time of functional test cases given a code update. We map microbenchmarks to code stylometry features and feed them to predictors for performance predictions. Our experiments achieved significant performance in predicting code performance, outperforming current state-of-the-art by 75% on neural-represented code stylometry features. [ABSTRACT FROM AUTHOR]

This paper will discuss the possibility of attributing authorship to translations by carrying out a comparative stylometric analysis of an author's poems and their translations of other writers' works. The paper puts forward two hypotheses. Firstly, its aim is to test whether an author's poems and translations share stylistic patterns. Secondly, it will test whether these shared patterns can be used to attribute authorship of translations. The complete works of Antonio Colinas and Eloy Sánchez Rosillo, together with their respective translations of Giacomo Leopardi's Cantos, will be used. Based on these texts, we will build computational representations that correspond to the stylistic profiles of each author, using various style cues related to metrics, grammar, and lexicon. These representations will serve as input for the calculation of similarity. Its result will allow us to determine to what extent characteristics of an author's own verses remain in the translated poems and whether there is more closeness between the two translations or between each author's poetry and their translation. [ABSTRACT FROM AUTHOR]

This article investigates the stylometric usefulness of morphosyntactic annotation. Focusing on the style of literary texts, it argues that including morphosyntactic annotation in analyses of style has at least two important advantages: (1) maintaining a topic agnostic approach and (2) providing input variables that are interpretable in traditional grammatical terms. This study demonstrates how widely available Universal Dependency parsers can generate useful morphological and syntactic data for texts in a range of languages. These data can serve as the basis for input features that are strongly informative about the style of individual novels, as indicated by accuracy in classification tests. The interpretability of such features is demonstrated by a discussion of the weakness of an "authorial" signal as opposed to the clear distinction among individual works. [ABSTRACT FROM AUTHOR]

In addition to being a widely recognized novelist, Milan Kundera has also authored three pieces for theatre: The Owners of the Keys (Majitelé klíčů 1961), The Blunder (Ptákovina 1967), and Jacques and his Master (Jakub a jeho pán 1971). In recent years, however, the hypothesis has been raised that Kundera was the true author of a fourth play, Juro Jánošík , first performed in a 1974 production under the name of Karel Steigerwald, who was Kundera's student at the time. In this study, we make use of supervised machine learning to settle the question of authorship attribution in the case of Juro Jánošík , with results strongly supporting the hypothesis of Kundera's authorship. [ABSTRACT FROM AUTHOR]

Journey to the West (西游记; Xi You Ji) is one of the best-known classic novels in Chinese literature, and it has been translated into English by various notable translators. This study compares the retranslation of the novel by Julia Lovell published in "Penguin Classics" in 2021 with Arthur Waley's translation in "Penguin Classics" in 1973. Employing a stylometric approach to compare the styles of the two translations, it uses the corpus tools of word lists, keyword analysis, and function word analysis. The results reveal distinct stylistic differences in the translators' styles, including lexical density, sentence length, and function words, which point to different translation strategies employed by the two translations of this classic Chinese novel. The book reviews published so far indicate that Lovell's version has been well received by contemporary Anglophone readers and that the changes in this retranslation may have helped to explain the recanonization of the original classic. This study provides new insights into the understanding of the differences between translation and retranslation, and thus has implications for the fields of translator style studies and stylometry research. [ABSTRACT FROM AUTHOR]

Principal components analysis (PCA) has been one of the staple methods used in stylometry. In a 2021 article, Pervez Rizvi casts doubt on this method and argues that some widely cited results based on it should be set aside. In the current article, I show that none of Rizvi's theoretical claims or experimental results stand up to examination. Rizvi argues that discarding the principal components beyond the first two makes the method unreliable, but permutation testing of PCAs shows that the top components in these trials are significant and robust, and the results across many experiments show the combination of the first and second component to be effective in classification. Rizvi argues that PCA components must be treated separately, and much of his critique of the PCA method is based on this standpoint, but this is not the practice in the work presented in the publications he cites or in the wider literature. Rizvi is unable to replicate a chart in an article by Craig, but his replication, unlike the original, does not account for the widely varying sizes of samples in his data. The current article shows that Rizvi's claims are misguided and that using PCA in the Burrows tradition to find and formalize authorial discriminations in text samples from plays of the Shakespearean era is efficacious and robust. [ABSTRACT FROM AUTHOR]

This study examines two recently proposed stylometric indices, attributivity and subjectivity, in the context of Czech journalism and scientific literature. Index of attributivity is defined as the ratio of the frequency of attributes to the sum of frequencies of nouns, pronouns, and attributes. Index of subjectivity is defined as the ratio of the frequency of subjects to the sum of frequencies of predicates and subjects. The research is based on the corpus of contemporary written Czech language SYN2020. It appears that both indices of attributivity and subjectivity are sensitive to different genres, and could be used in stylometric analyses, especially those that focus on the interpretability of quantitative measures. In general, more formal texts tend to have higher values of both indices across different genres. [ABSTRACT FROM AUTHOR]

Machine translation metrics often fall short in capturing the challenges of literary translation in which translators play a creative role. Large Language Models (LLMs) like GPT4o and Mistral offer new approaches to assessing how well a translation mirrors the reading experience from one language to another. Our case study focuses on the first volume of Marcel Proust's “A la recherche du temps perdu,” a work known for its lively translation debates. We use stylometry and emotional arc leveraging the newest multilingual generative AI models to evaluate loss in translation according to different translation theories. AI analysis reveals previously undertheorized aspects of translation. Notably, we uncover changes in authorial style and the evolution of sentiment language over time. Our study demonstrates that AI-driven approaches leveraging advanced LLMs yield new perspectives on literary translation assessment. These methods offer insight into the creative choices made by translators and open up new avenues for understanding the complexities of translating literary works.

The digitization of texts and the advent of big data analyses have transformed our understanding of authorship and collaboration in early modern drama. However, these advancements ought to be carefully contextualized within the material realities of early modern playwriting. The scarcity of surviving dramatic manuscripts underscores the significant role of agents like compositors, printers and editors, and the loss of the majority of plays from English commercial theatres casts doubt on the reliability of comparisons based on unique or common verbal parallels. The article focuses on drama from the Elizabethan and Jacobean periods, particularly the recently proposed collaboration between Christopher Marlowe and William Shakespeare on the Henry VI plays. Applying the IT concept of GIGO (garbage in, garbage out), it highlights the impact of textual transmission intricacies on authorship attribution, emphasizing that even the most sophisticated attribution techniques are only as reliable as the (often unreliable) data they utilize.

Extensive use of AI-generated texts culminated recently after the advent of large language models. Although the use of AI text generators, such as ChatGPT, is beneficial, it also threatens the academic level as students may resort to it. In this work, we propose a technique leveraging the intrinsic stylometric features of documents to detect ChatGPT-based plagiarism. The stylometric features were normalized and fed to classical classifiers, such as k-Nearest Neighbors, Decision Tree, and Naïve Bayes, as well as ensemble classifiers, such as XGBoost and Stacking. A thorough examination of the classifier was conducted by using Cross-Fold validation, hyperparameters tuning, and multiple training iterations. The results show the efficacy of both classical and ensemble learning classifiers in distinguishing between human and ChatGPT writing styles with a noteworthy performance of XGBoost where 100 % was achieved for accuracy, recall, and precision metrics. Moreover, the proposed XGBoost classifier outperformed the state-of-the-art result on the same dataset and same classifier highlighting the superiority of the proposed feature style extraction method over TF-IDF techniques. The ensemble learning classifiers were also applied to the generated dataset with mixed texts, where paragraphs are written by ChatGPT and humans. The results show that 98 % of the documents were classified correctly as either mixed or human. The last contribution consists in the authorship attribution of the paragraphs of a single document where the accuracy reached 92.3 %.

The paper deals with the poetics of the novels of the Peruvian writer Mario Vargas Llosa and the evolution of his style. To solve research problems, it is used the Delta method which is the most authoritative and reliable stylometric tool that allows you to compare texts based on the distribution of most frequent words. The work poses three questions: 1) Do Vargas Llosa's early total novels The Time of the Hero, The Green House, and Conversation in the Cathedral differ at the level of style from the later novels, forming a separate group, as the literary hypothesis claims? Answer: the hypothesis is confirmed; 2) Is it possible to divide the fifteen texts of the second period into smaller groups based on stylistic similarities? Answer: novels of the second period are divided into three large groups. Moreover, an interesting correlation is revealed: the novels grouped into separate groups have similarities not only at the level of style, but also at the level of themes, composition and narrative features; 3) Whether the “boom” is a stylistically isolated period in the poetics of its key authors? Answer: the novels written by Latin American authors during the boom period (1960–1970s) are not stylistically isolated: the authorial signal is stronger, and the case of Vargas Llosa with a clear differentiation of groups of novels is unique.

The present study addresses the question of to what extent and how authors' gender is reflected in the textual properties of bestselling fiction in Swedish during the period 2015–2020. The empirical material was a corpus of 235 female-authored books and 214 male-authored works. The analysis of the texts departed from text property measures targeting grammatical and lexical aspects of language use. Differences between the genders were analysed using the probability of superiority measure in combination with a threshold criterion. The results suggest that authors of bestselling fiction in the Swedish book market to a high degree engage in forms of gender performance when they compose their texts. The differences could in most cases be interpreted as conforming to patterns that have previously been reported for other languages and categories of language use. The gender performance to a large extent agreed with traditional stereotypes about the interests of women and men. There were also differences in grammar-related stylistic preferences. Among the female themes, positive emotion and social interaction were prominent. The male examples include weapons and animosity, as well as numerical quantification. A more grammar-related tendency is that male authors tend to package a larger fraction of their text into noun phrases. [ABSTRACT FROM AUTHOR]

Although stylometry and related quantitative-linguistic approaches have long been in use for assessing text authorship or for addressing other textual issues in Western literature, I am not aware of any serious attempts in the field of study of pre-modern Chinese texts. It is quite surprising given the fact that the authorship and dating of particular ancient Chinese books or their components have belonged to the most fiercely discussed topics in pre-modern Chinese philology in the last few decades. This paper is therefore conceived as an experiment in this vein, centering on the problem of dating. On the basis of a set of detailed statistic data, involving e.g. occurrence frequencies of word classes, syntactic functions, special function words, constructions, or polysyllabic words, I tried to find out applicable parameters, which would be sensitive to date of composition. As the primary linguistic material is concerned, it consists of a series of stylistically variegated chapters of several texts a) clearly dated as Warring States texts, b) clearly dated as mediaeval texts, c) two chapters of the Yinwenzi, a text with disputed dating. In the article, the results of these experiments are reported, interpreted, and evaluated, and some recommendations for the future research are suggested. [ABSTRACT FROM AUTHOR]

Digital forensic investigations are becoming increasingly crucial in criminal investigations and civil litigations, especially in cases of corporate espionage and intellectual property theft as more communication occurs online via e-mail and social media. Deceptive opinion spam analysis is an emerging field of research that aims to detect and identify fraudulent reviews, comments, and other forms of deceptive online content. In this paper, we explore how the findings from this field may be relevant to forensic investigation, particularly the features that capture stylistic patterns and sentiments, which are psychologically relevant aspects of truthful and deceptive language. To assess these features' utility, we demonstrate the potential of our proposed approach using the real-world dataset from the Enron Email Corpus. Our findings suggest that deceptive opinion spam analysis may be a valuable tool for forensic investigators and legal professionals looking to identify and analyze deceptive behavior in online communication. By incorporating these techniques into their investigative and legal strategies, professionals can improve the accuracy and reliability of their findings, leading to more effective and just outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Matica Srpska Journal of Slavic Studies / Zbornik Matice Srpske za Slavistiku is the property of Matica srpska and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Interamericana de Investigación, Educación y Pedagogía - RIIEP is the property of Universidad Santo Tomas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to uncover the prototypical linguistic elements and patterns of Kundera's prose in his novel Le Livre du rire et de l'oubli (1979, Gallimard). The exploration employs statistical and machine learning techniques, including the application of Hyperbase in both its web and standard versions. Hyperbase provides deep learning features for text classification tasks (Savoy 2015; Tuzzi & Cortelazzo 2018), based on convolutional neural networks (Kalchbrenner et al. 2014; Kim 2014) which go beyond the process of convolution and incorporate an innovative deconvolution mechanism that extracts key linguistic markers essential for classification purposes (Vanni et al. 2018; Mayaffre & Vanni 2021). The training of the Hyperbase deep learning model involves an extensive corpus containing novels by 36 authors, including Kundera, thus encompassing the French literature landscape from 1960-2014. The study leads to the identification of linguistic markers related to vocabulary, morphosyntax, lexical and grammatical patterns, and lexico-grammatical structures. These markers are then examined to reveal the underlying aesthetic intentions of the author. The conclusion focuses on the contribution of deep learning and statistics in the context of this qualitative linguistic study of a literary text. [ABSTRACT FROM AUTHOR]

Copyright of Hipogrifo: revista de literatura y cultura del siglo de oro is the property of Hipogrifo: revista de literatura y cultura del siglo de oro and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Online peer support mental health forums provide an effective and accessible form of support, augmenting scarce clinical and face-to-face assistance. However, to enhance their effectiveness, it is essential to understand the unique characteristics of peer support user groups, and how they participate, contribute and communicate in these forums. This paper proposes and tests a novel approach that leverages stylometry analysis to uncover the unique characteristics of peer support user groups in such forums. Our approach identifies how each group empowers and supports other users, and what distinguishes them from others. The analysis shows that emotion-related words are crucial in identifying and distinguishing user groups based on their writing style. Comparative analysis of emotion expressions across user groups also uncovers the significance of emotional content in these forums in promoting mental well-being. Valued 'senior contributors' were more likely than all other groups including trained community guides to use a wide range of both positive and negative emotions in their posts. These findings have significant implications for improving the training of peer-mentors and moderators, scaling forum services, and improving guidelines for emotional expression among peer support users. Our approach presents an objective approach to differentiating the characteristics and communication patterns of valued senior contributors, mentors, and guides, enabling service providers to foster the kinds of communication that supports positive outcomes for distressed users. [ABSTRACT FROM AUTHOR]

This article investigates modern vector text models for solving the problem of genre classifying Russian-language texts. The models include ELMo embeddings, a pretrained BERT language model, and a set of numerical rhythmic characteristics based on lexico-grammatical tools. The experiments have been carried out on a corpus of 10 000 texts in five genres: novels, scientific articles, reviews, posts from the VKontakte social network, and news from OpenCorpora. Visualization and analysis of statistics for rhythmic characteristics have made it possible to distinguish both the most diverse genres in terms of rhythm (novels and reviews) and the least (scientific articles). It is these genres that are subsequently classified best using rhythm and the LSTM neural network classifier. Clustering and classifying texts by genre using the ELMo and BERT embeddings make it possible to separate one genre from another with a small number of errors. The multiclassification F-measure reaches 99%. This study confirms the effectiveness of modern embeddings in the tasks of computational linguistics and highlights the advantages and limitations of the set rhythmic characteristics on the genre classification material. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue on privacy-preserving techniques in cloud/fog and Internet of Things (IoT) networks. The issue explores various methods and strategies for protecting privacy in these networks, including cryptographic algorithms, federated learning models, and password-less authentication. The authors also discuss the challenges of securing IoT devices with limited computational power and the importance of safeguarding data in large networks. The special issue includes 10 selected papers out of a total of 21 submissions, covering topics such as true random number generation, shared key generation, watermarking, dependability assessment, authentication schemes, federated learning, batch processing, stylometry, and searchable encryption. The authors express their gratitude to the contributors for their valuable research in this field. [Extracted from the article]

Within the literary domain, genres function as fundamental organizing concepts that provide readers, publishers, and academics with a unified framework. Genres are discrete categories that are distinguished by common stylistic, thematic, and structural components. They facilitate the categorization process and improve our understanding of a wide range of literary expressions. In this paper, we introduce a new dataset for genre classification of Russian books, covering 11 literary genres. We also perform dataset evaluation for the tasks of binary and multi-class genre identification. Through extensive experimentation and analysis, we explore the effectiveness of different text representations, including stylometric features, in genre classification. Our findings clarify the challenges present in classifying Russian literature by genre, revealing insights into the performance of different models across various genres. Furthermore, we address several research questions regarding the difficulty of multi-class classification compared to binary classification, and the impact of stylometric features on classification accuracy.