Reviewerly/exHarmony huggingface dataset:
Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection

In this work, we propose a method for incorporating question-answering (QA) signals into a summarization model. Our method identifies salient noun phrases (NPs) in the input document by automatically generating wh-questions that are answered by the NPs and automatically determining whether those questions are answered in the gold summaries. This QA-based signal is incorporated into a two-stage summarization model which first marks salient NPs in the input document using a classification model, then conditionally generates a summary. Our experiments demonstrate that the models trained using QA-based supervision generate higher-quality summaries than baseline methods of identifying salient spans on benchmark summarization datasets. Further, we show that the content of the generated summaries can be controlled based on which NPs are marked in the input document. Finally, we propose a method of augmenting the training data so the gold summaries are more consistent with the marked input spans used during training and show how this results in models which learn to better exclude unmarked document content.
WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition

Named Entity Recognition (NER) is a fundamental NLP tasks with a wide range of practical applications. The performance of state-of-the-art NER methods depends on high quality manually anotated datasets which still do not exist for some languages. In this work we aim to remedy this situation in Slovak by introducing WikiGoldSK, the first sizable human labelled Slovak NER dataset. We benchmark it by evaluating state-of-the-art multilingual Pretrained Language Models and comparing it to the existing silver-standard Slovak NER dataset. We also conduct few-shot experiments and show that training on a sliver-standard dataset yields better results. To enable future work that can be based on Slovak NER, we release the dataset, code, as well as the trained models publicly under permissible licensing terms at https://github.com/NaiveNeuron/WikiGoldSK
Target Two Birds With One SToNe: Entity-Level Sentiment and Tone Analysis in Croatian News Headlines

Sentiment analysis is often used to examine how different actors are portrayed in the media, and analysis of news headlines is of particular interest due to their attention-grabbing role. We address the task of entity-level sentiment analysis from Croatian news headlines. We frame the task as targeted sentiment analysis (TSA), explicitly differentiating between sentiment toward a named entity and the overall tone of the headline. We describe SToNe, a new dataset for this task with sentiment and tone labels. We implement several neural benchmark models, utilizing single- and multi-task training, and show that TSA can benefit from tone information. Finally, we gauge the difficulty of this task by leveraging dataset cartography.
Machine-translated texts from English to Polish show a potential for typological explanations in Source Language Identification

This work examines a case study that investigates (1) the achievability of extracting typological features from Polish texts, and (2) their contrastive power to discriminate between machine-translated texts from English. The findings indicate potential for a proposed method that deals with the explainable prediction of the source language of translated texts.
Varepsilon kú mask: Integrating Yorùbá cultural greetings into machine translation

This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yorùbá greetings (kú mask), which are a big part of Yorùbá language and culture, into English. To evaluate these models, we present IkiniYorùbá, a Yorùbá-English translation dataset containing some Yorùbá greetings, and sample use cases. We analysed the performance of different multilingual NMT systems including Google and NLLB and show that these models struggle to accurately translate Yorùbá greetings into English. In addition, we trained a Yorùbá-English model by fine-tuning an existing NMT model on the training split of IkiniYorùbá and this achieved better performance when compared to the pre-trained multilingual NMT models, although they were trained on a large volume of data.
Information Extraction from Polish Radiology Reports Using Language Models

Radiology reports are vital elements of directing patient care. They are usually delivered in free text form, which makes them prone to errors, such as omission in reporting radiological findings and using difficult-to-comprehend mental shortcuts. Although structured reporting is the recommended method, its adoption continues to be limited. Radiologists find structured reports too limiting and burdensome. In this paper, we propose the model, which is meant to preserve the benefits of free text, while moving towards a structured report. The model automatically parametrizes Polish radiology reports based on language models. The models were trained on a large dataset of 1200 chest computed tomography (CT) reports annotated by multiple medical experts reports with 44 observation tags. Experimental analysis shows that models based on language models are able to achieve satisfactory results despite being pre-trained on general domain corpora. Overall, the model achieves an F1 score of 81% and is able to successfully parametrize the most common radiological observations, allowing for potential adaptation in clinical practice. Our model is publically available.
A Cross-Lingual Study of Homotransphobia on Twitter

We present a cross-lingual study of homotransphobia on Twitter, examining the prevalence and forms of homotransphobic content in tweets related to LGBT issues in seven languages. Our findings reveal that homotransphobia is a global problem that takes on distinct cultural expressions, influenced by factors such as misinformation, cultural prejudices, and religious beliefs. To aid the detection of hate speech, we also devise a taxonomy that classifies public discourse around LGBT issues. By contributing to the growing body of research on online hate speech, our study provides valuable insights for creating effective strategies to combat homotransphobia on social media.
Automatic text simplification of Russian texts using control tokens

This paper describes the research on the possibilities to control automatic text simplification with special tokens that allow modifying the length, paraphrasing degree, syntactic complexity, and the CEFR (Common European Framework of Reference) grade level of the output texts, i.e. the level of language proficiency a non-native speaker would need to understand them. The project is focused on Russian texts and aims to continue and broaden the existing research on controlled Russian text simplification. It is done by exploring available datasets for monolingual Russian machine translation (paraphrasing and simplification), experimenting with various model architectures, and adding control tokens that have not been used on Russian texts previously.
STUDY ON CHARACTERISTICS OF MEGASTORY CATEGORY ON NEWS OF VIETNAM NEWS AGENCY

The baotintuc.vn website is a newspaper belonging to the Vietnam News Agency (VNA). Following the new trend of the press, this newspaper officially launched the website www.baotintuc.vn on December 17, 2010 to support information for newspapers and expand the audience. When researching this topic, the researchers selected Megastory articles on the baotintuc.vn online newspaper for the survey. The study would examine 74 articles in the Megastory category in a 12-month period in 2020. From this respect, it aims to generalize the characteristics of content and form of this genre with various fields. The findings revealed that the content and form characteristics in Megastory articles have been asserting themselves in the world's multimedia journalism trends, meeting public tastes nationwide and worldwide as well.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0046/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Shepard’s Validation of Antitrust Relief Framework on Patent Infringement of Novartis’ Cancer Drug Using Genomic Architectures of Legal Literature based on UK Intellectual Property Law

Comparative law is designed for alignment of constitutional law with other countries advocating public welfare and safety. The United States has an Intellectual Property provision under U.S. Fair Clause using their constitution as pre-emptive doctrine. The aim of this paper is to evaluate the applicability of UK Intellectual Property Law based on their complexed policies on Artificial Intelligence. Hence, it leads to problem statements questioning: (1) the eligibility of matters of facts did not meet UK IP Law; (2) the standard for evidence towards invention using Artificial Intelligence does not conform with UK IP Law; (3) Liability in AI patent infringement is not subsistent in UK IP Law; and (4) AI’s compliance is not subject for responsibility under creativity and non-obviousness criteria. The Trade-Related Aspects of Intellectual Property Rights (TRIPS) Agreement promotes public welfare and safety under constitutional laws. India, as a member, is obliged to comply with the standard of evidence in patentability under World Trade Organization (WTO). The WTO is a committee responsible for advocating business law. Invention for marketability of patent product has its own complexed policy to comply for acceptance of an Intellectual Property creation. Constitutional law is designed to be made comparable with other countries, promoting the monetary success of their nation exhibiting economic progress in industrial and technological advancements. Hence, authorless works marking artificial intelligence towards public health and safety must be done in lack of any dedication to human connections, resulting in immersion of their “new” product as a work of art, making non-obviousness skills to people as part of common logic and interests, hence, a product of convenience. This intergovernmental task force is vital to implement constitutional laws comparable to other countries. Hence, the advocacy of business ethics is a highly acknowledged means of making the lives of people to be technologically advanced with convenience. Thus, inventions should be made affordable for public access.
Spring Transaction Management

Transactions are one of the most critical parts of building a reliable enterprise application. The most common type of transaction is a database operation. In a typical database update operation, a database transaction begins, data is updated, and then the transaction is committed or rolled back, depending on the result of the database operation. However, in many cases, depending on the application requirements and the back-end resources that the application needs to interact with (such as an RDBMS, message-oriented middleware, an ERP system, and so on), transaction management can be much more complicated.
Spring REST Support

Chapter 13 introduced communication over the HTTP protocol between two Spring Web applications that exposed REST API. This chapter expands the subject by introducing you to RESTful web services, also referred to as RESTful-WS.
Task Scheduling

Task scheduling is a common feature in enterprise applications. Task scheduling is composed mainly of three parts:
Securing Spring Web Applications

Chapter explained how to build a Spring web application using classic, “manual”-style configuration and using Spring Boot with Thymeleaf. This chapter will take the application built in Chapter 14 and add a security layer that will declare which users are allowed to access various parts of the application. For example, only those users who logged into the application with a valid user ID can add a new singer or update existing singers. Other users, known as anonymous users, can only view singer information.
The Effect of the Use of Civics Teaching Aids Media on Students' Understanding

This study aims to investigate the impact of using visual aids on students' understanding of Civics in Class V at UPTD SD Negeri Patenteng 4, Modung District. The research adopts a quantitative approach and includes all fifth-grade students at UPTD SD Negeri Patenteng 4 as the population, totaling 16 students, consisting of 7 female and 9 male students. The study is conducted at UPTD SD Negeri Patenteng 4, Modung District. Data collection techniques involve observation, tests, questionnaires, and documentation. The data is analyzed through validity testing, reliability testing, normality testing, and paired simple T-Test. The results of the hypothesis analysis indicate that the student's learning outcomes improved after utilizing visual aids in the learning process. The average pre-test score was 68.13, while the average post-test score was 85.88, suggesting a significant increase in learning outcomes. The statistical analysis in IMB SPSS v21.0 revealed a significance value of 0.000, indicating that using visual aids significantly impacts student understanding, facilitating their mastery of the subject matter and enhancing the meaningfulness of the learning process.
Cognitive Driven Development helps software teams to keep code units under the limit!

Software design techniques are key elements in the process of designing good software. Over the years, a large number of design techniques have been proposed by both researchers and practitioners. Unfortunately, despite their uniqueness, it is not uncommon to find software products that make subpar design decisions, leading to design degradation challenges. One potential reason for this behavior is that developers do not have a clear vision of how much a code unit could grow; without this vision, a code unit can grow endlessly, even when developers are equipped with an arsenal of design practices. Different than other design techniques, Cognitive Driven Development (CDD for short) focuses on (1) defining and (2) limiting the number of coding elements that developers could use at a given code unit. In this paper, we report on the experiences of a software development team using CDD for building from scratch a learning management tool at Zup Innovation, a Brazilian tech company. By curating commit traces left in the repositories, combined with the developers’ perception, we organized a set of findings and lessons that could be useful for those interested in adopting CDD. For instance, we noticed that by using CDD, despite the evolution of the product, developers were able to keep the code units under a small amount of size (in terms of lines of code). Furthermore, although limiting the complexity is at the heart of CDD, we also discovered that developers tend to relax this notion of limit so that they can cope with the different complexities of the software. Still, we noticed that CDD could also influence testing practices; limiting the code units’ size makes testing easier to perform.
An efficient image encryption algorithm based on S-box and DNA code

In order to ensure the security of image information transmission, a new image encryption algorithm is proposed based on S-box and DNA coding theory. Through the construction of the dynamic S-box, the key space of the encryption algorithm is enlarged, and the encryption process is more flexible. The flexibility of DNA encoding allows for a larger key volume and higher security in image encryption. Experimental results show that the encrypted image gray level distribution is uniform, the correlation between adjacent pixels is lower, the encryption effect is good, and it has good security.
Developing English Listening Skills for Comprehension Through Repetition Technique Using Podcast

This research is a pre-experimental study conducted with the following objectives: 1) to investigate the efficiency of using podcast in developing English listening skills for; 2) to compare the achievement of English listening comprehension before and after the repetition technique using podcast; 3) to examine the satisfaction with the intervention of using the repetition technique with podcast to enhance English listening skills for comprehension. The sample group consisted of 41 second-year students. The selection of the sample group was done through purposive sampling, whereby the sample group had to meet the language proficiency standards of level A2 or B1 according to the Common European Framework of Reference for Languages (CEFR). The experimental period spanned 11 weeks, with a total of 22 hours. The research instruments were podcast clips, practice exercises, pre and post-tests, and satisfaction questionnaire. The research findings revealed that the efficiency of using podcast in developing English listening skills for comprehension exceeded the predefined criterion of 80/80, with a score of 88.51/86.59. Moreover, when comparing the scores for the post-test English listening skills for comprehension of students using the intervention of using the repetition technique with podcast to enhance English listening skills for comprehension, it was found to be significantly higher than the average pre-test scores at a statistical significance level of .05. Overall, the students expressed a high level of satisfaction with the intervention of using the repetition technique with podcast to enhance English listening skills for comprehension. The average satisfaction score was 4.50.
Human Librarian Interviews ChatGPT

In an effort to understand how ChatGPT might be used as a scholarly research tool, I conducted an informal, unstructured interview with the AI chatbot. I learned that it generates its responses based on patterns it identifies in its large reservoir of text data; ChatGPT cannot vouch for the accuracy of the information it presents, nor cite its sources. For this reason, instead of being used as an information source in itself, ChatGPT may be better suited as an information assistant, helping researchers design searches for information in other sources in tandem with “human librarians” (as it designates academic librarians like myself). This reflective piece describes and evaluates the AI chatbot using its own generated content and provides a foundation from which librarians can explore the topic of AI chatbots and scholarly research within information literacy classes.
Integrating of Digital Literacy and Achievement Motives for Learning Activities in Universities After the Covid-19 Pandemic

The research aims to analyze and describe the integration of digital literacy and achievement motives in post-covid-19 post-pandemic tertiary learning activities using a quantitative method with a correlational approach. The research location was at the Sharia Faculty of UIN Raden Fattah Palembang, from a population of 256 students. Data was collected using observation, documentation, and questionnaires. The results of the study found that the integration of digital literacy and achievement motives partially and simultaneously had a low effect on post-covid-19 learning activities. It was concluded that learning activities at the Sharia Faculty of UIN Raden Fatah Palembang had combined digital literacy with achievement motives in learning activities that had not been fully successful. It is recommended that total changes be made to learning activities by maximizing digital literacy integration and increasing achievement motives for lecturers and students. Learning activities will not run effectively if digital literacy interactions with achievement motives from lecturers and students are not carried out optimally.
Handling Temporal Coupling

Cohesion and coupling are two important concepts in software engineering. Wikipedia ( (computer_science)) states the following:
Composing Functions Using Pipelining

Understanding the building blocks of functional programming (FP) is essential. But using them effectively is equally important. Composing functions is an integral part of FP. This chapter discusses this topic.
Functional Patterns

Patterns are common in programming. You may use them either knowingly or unknowingly. So, what is a pattern? In simple terms, it is a description or template for solving problems that can be applied in different situations. Let me give you an example to help you understand the importance of identifying patterns. Suppose you have a requirement to write a function that can do some meaningful operation on a list. Let’s further assume that you were able to solve the problem. Then you got a similar requirement for other data structures, such as an array or a dictionary. You can write a function that applies to a list as well as other data structures, such as an array or a dictionary. Now you are solving the pattern of the problem, rather than the problem itself. Solving the “pattern of the problems” can give you long-term benefits. Here are some important points before look at a detailed discussion of patterns:
Fine-Tuning BERT with Character-Level Noise for Zero-Shot Transfer to Dialects and Closely-Related Languages

In this work, we induce character-level noise in various forms when fine-tuning BERT to enable zero-shot cross-lingual transfer to unseen dialects and languages. We fine-tune BERT on three sentence-level classification tasks and evaluate our approach on an assortment of unseen dialects and languages. We find that character-level noise can be an extremely effective agent of cross-lingual transfer under certain conditions, while it is not as helpful in others. Specifically, we explore these differences in terms of the nature of the task and the relationships between source and target languages, finding that introduction of character-level noise during fine-tuning is particularly helpful when a task draws on surface level cues and the source-target cross-lingual pair has a relatively high lexical overlap with shorter (i.e., less meaningful) unseen tokens on average.
Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with Hromadske.ua News Dataset

Despite recent NLP developments, abstractive summarization remains a challenging task, especially in the case of low-resource languages like Ukrainian. The paper aims at improving the quality of summaries produced by mT5 for news in Ukrainian by fine-tuning the model with a mixture of summarization and text similarity tasks using summary-article and title-article training pairs, respectively. The proposed training set-up with small, base, and large mT5 models produce higher quality résumé. Besides, we present a new Ukrainian dataset for the abstractive summarization task that consists of circa 36.5K articles collected from Hromadske.ua until June 2021.
Automatic Spell Checker and Correction for Under-represented Spoken Languages: Case Study on Wolof

This paper presents a spell checker and correction tool specifically designed for Wolof, an under-represented spoken language in Africa. The proposed spell checker leverages a combination of a trie data structure, dynamic programming, and the weighted Levenshtein distance to generate suggestions for misspelled words. We created novel linguistic resources for Wolof, such as a lexicon and a corpus of misspelled words, using a semi-automatic approach that combines manual and automatic annotation methods. Despite the limited data available for the Wolof language, the spell checker’s performance showed a predictive accuracy of 98.31% and a suggestion accuracy of 93.33%.Our primary focus remains the revitalization and preservation of Wolof as an Indigenous and spoken language in Africa, providing our efforts to develop novel linguistic resources. This work represents a valuable contribution to the growth of computational tools and resources for the Wolof language and provides a strong foundation for future studies in the automatic spell checking and correction field.