Doctors, Drugs, and Danger

Scholarship on young adult (YA) literature has long attended to the interrelationship of power, ideology, and narrative. Drawing on this scholarship, we examined a nonfiction text about the opiate epidemic. Using critical comparative content analysis (CCCA), our study examined differences in Dreamland (the original version) and Dreamland (the young adult adaptation) to better understand the changing nature of textual representation when youth become the imagined audience. We found that in the youth adaptation of Dreamland, the implied youth reader is (a) provided less information about the opiate epidemic, which is also delivered in a simpler structure; (b) kept at a greater rhetorical distance from people who might be deemed unsavory, and (c) given a more optimistic view of the opiate epidemic in terms of progress achieved rather than action needed. The youth adaptation of Dreamland, therefore, positions youth as needing simplicity, protection, and a sense of optimism. Our analysis demonstrates how the implied youth reader is a textual byproduct of discourses of adolescence/ts. As youth adaptations continue their prominence in the YA marketplace, scholars and teachers should critically engage how youth are positioned as readers and thinkers by the YA publishing industry. Next steps involve additional studies that focus on the implied (youth) reader through CCCA and studies that involve middle and secondary education students, the real readers of these texts. This study is supplemented by an interview with Sam Quinones, the author of the original version of Dreamland.

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.

Strokovna leksika v Kremplovih Dogodivšinah štajerske zemle

The article focuses on the vocabulary derived from the first extensive historical work in Slovene, i.e. Dogodivšine štajerske zemle (The History of the Land of Styria, 1845) by Anton Krempl. The analysis focuses on the development of a terminology in the field of history, which has not been thoroughly researched so far. The analysis highlights East Styrian linguistic features at various linguistic levels. The article covers word-building and textual representation processes, as well as stylistic characteristics, in particular expressive and phraseological expressions. Terminology is classified according to those thematic areas that are still relevant in studying and researching history, considering the connection of historical terminology with the terminology of related disciplines and with general language.

UNBERT: User-News Matching BERT for News Recommendation

Nowadays, news recommendation has become a popular channel for users to access news of their interests. How to represent rich textual contents of news and precisely match users' interests and candidate news lies in the core of news recommendation. However, existing recommendation methods merely learn textual representations from in-domain news data, which limits their generalization ability to new news that are common in cold-start scenarios. Meanwhile, many of these methods represent each user by aggregating the historically browsed news into a single vector and then compute the matching score with the candidate news vector, which may lose the low-level matching signals. In this paper, we explore the use of the successful BERT pre-training technique in NLP for news recommendation and propose a BERT-based user-news matching model, called UNBERT. In contrast to existing research, our UNBERT model not only leverages the pre-trained model with rich language knowledge to enhance textual representation, but also captures multi-grained user-news matching signals at both word-level and news-level. Extensive experiments on the Microsoft News Dataset (MIND) demonstrate that our approach constantly outperforms the state-of-the-art methods.

TopFuzz4SA: An Integrated Fuzzy Neural Network with Topic-aware Auto-encoding for Sentiment Analysis

Recent advanced deep learning architectures, such as neural seq2seq, transformer, etc. have demonstrated remarkable improvements in multi-typed sentiment classification tasks. Even though recent transformer-based and seq2seq-based models have successfully enabled to capture rich-contextual information of texts, they are still lacking of attention on incorporating the global semantic information, such as topic, in order to sufficiently leverage the performance of downstream SA task. Moreover, emotional expressions of users are normally in forms of natural human-written textual data which might consist a lot of noise and ambiguity which impose great challenges on the processes of textual representation learning as well as sentiment polarity prediction. To meet these challenges, we propose a novel integrated fuzzy-neural architecture with a topic-driven textual representation learning approach for handling SA task, called as: TopFuzz4SA. Specifically, in the proposed TopFuzz4SA model, we first apply a topic-driven neural encoder-decoder architecture with the incorporation of latent topic embedding and attention mechanism to sufficiently learn both rich contextual and global semantic information of the given textual data. Then, the achieved rich semantic representations of texts are fed into a fused deep fuzzy neural network to effectively reduce the feature ambiguity and noise, forming the final textual representations for sentiment classification task. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed TopFuzz4SA model in comparing with contemporary state-of-the-art baselines.

VISUAL-GRAPHIC PROGRAMMING SYSTEM BASED ON DEVELOPMENT OF BLOCK DIAGRAMS OF ALGORITHMS. CONVERTING PROGRAMS TEXTS IN DIFFERENT PROGRAMMING LANGUAGES INTO BLOCK DIAGRAMS AND BACK

В работе представлены результаты исследований и разработки средств визуально-графического проектирования сложных алгоритмов в виде блок-схем в целом и, в частности, интерфейсной компоненты таких средств – конвертора текстов программ на различных языках программирования во внутреннее структурно-вложенное представление и обратно в тексты. Визуально-графическое представление алгоритмов лучше воспринимается человеком-разработчиком, чем традиционное текстовое представление, поэтому развитию подобных систем программирования в последнее время уделяется определенное внимание. Однако реализация максимально удобных для человека технологий создания и редактирования блок-схем сопряжена с необходимостью решения ряда сложных задач формирования и адекватного отображения управляющих структур, используемых в популярных языках программирования. Проведен анализ таких структур для наиболее популярных по разным метрикам языков программирования, представлены его результаты и предложен способ выявления и преобразования управляющих структур во внутреннее представление визуально-графического редактора. Описаны основные алгоритмы работы конвертора текстов программ как в прямом, так и в обратном направлениях. The paper presents the results of research and development of tools for visual and graphic design of complex algorithms in the form of block diagrams in general and, in particular, the interface component of such tools - a converter of programs texts in various programming languages ​​into an internal structurally nested representation and back into texts. The visual-graphical representation of algorithms is better perceived by a human developer than the traditional textual representation, therefore, some attention has been paid to the development of such programming systems in recent years. However, the implementation of the most human-friendly technologies for creating and editing block diagrams is associated with the need to solve a number of complex problems of forming and adequately displaying control structures used in popular programming languages. The analysis of such structures for the most popular programming languages ​​in terms of various metrics is carried out, its results are presented, and a method for identifying and transforming control structures into an internal representation of a visual-graphic editor is proposed. The main algorithms for the operation of the program text converter both in forward and backward directions are described.

Deconstructing the Textual Representation of the Fathers in the Fantasy Picturebook One Dad, Two Dads, Brown Dad, Blue Dads (1994)

This paper aims to explore the linguistic and visual choices used by the writer and the illustrator in order to create meaning in the fantasy picturebook One Dad, Two Dads, Brown Dad, Blue Dads (1994), written by Johnny Valentine and illustrated by Melody Sarecky, which features a gay family. The analytical tools employed in this study to deconstruct meanings in the said picturebook are Kress and van Leeuwen’s (2006) Visual Social Semiotics and Painter et al.’s (2013) model to read visual narratives in children’s picturebooks. The analysis concentrates on the textual and compositional metafunctions in order to observe the intersemiotic relationship between verbal and visual meanings and their realizations through various linguistic and visual modes. The methodology is qualitative-descriptive. One Dad, Two Dads, Brown Dad, Blue Dads reveals that both visuals and written text narrate the story, although it is the visual that is given a predominant role on the page due to its size, the location of the characters and the frames. The analysis shows that this is a picturebook in which having two fathers is represented as nonnormalized, although they perform their family duties as they are expected to because they do the same things that other fathers do.

Seeing Blackness through Black Expressive Culture: A Reading of Zanele Muholi’s Somnyama Ngonyama – Hail the Dark Lioness

This article examines visual and textual representation of blackness in contemporary black expressive culture. Its primary objective is to discern what blackness means and looks like when seen from the point of view of contemporary black expressive culture. To assess this, I first, briefly, analyze and interpret blackness. Second, I interrogate how contemporary black practitioners critique European ideas of blackness and mirror the complex multidimensionality of black subjecthood by conducting a formal analysis of two pieces of South African artist Zanele Muholi’s Somnyama Ngonyama – Hail the Dark Lioness series. Third, I explore the relationship between visual and textual imagery and their involvement in discourses on race. My intention is to reveal the role text and images play and have played in shaping the concept, perception, and representation of blackness; the visual effect they have had on the black imagination; and the heavy responsibility placed on black writers and artists not only to correct these images but to create images for the collective more often than for themselves.