The aim of the study is to ascertain what characterizes a book classified as easy reading, and how the selected books relate to factors that are regarded as facilitating reading. The study was conducted with the help of a text analysis considering both linguistic and narratological aspects. The material used is the four parts in the series Jakten på Jack, written by Martin Olczak and illustrated by Anna Sandler. The theories on which the study is based are LIX value, the classification levels of the publisher Nypon Förlag, and MTM’s different aspects of what makes books easily read. The result of the study shows that it is not possible to use a calculation of the LIX value to find out how well a book suits a reader at a particular level. Other aspects need to be considered, for example, those found in Nypon Förlag’s levels and via MTM’s website. The results show that tempo and cliffhangers are factors that make reading easier. The illustrations in the books prove to be significant too, since they can help to explain difficult words or events.

Automatic text summarization has achieved remarkable success with the development of deep neural networks and the availability of standardized benchmark datasets. It can generate fluent, human-like summaries. However, the unreliability of the existing evaluation metrics hinders its practical usage and slows down its progress. To address this issue, we propose an automatic reference-less text summarization evaluation system with dynamically generated synthetic facts. We hypothesize that if a system guarantees a summary that has all the facts that are 100% known in the synthetic document, it can provide natural interpretability and high feasibility in measuring factual consistency and comprehensiveness. To our knowledge, our system is the first system that measures the overarching quality of the text summarization models with factual consistency, comprehensiveness, and compression rate. We validate our system by comparing its correlation with human judgment with existing N-gram overlap-based metrics such as ROUGE and BLEU and a BERT-based evaluation metric, BERTScore. Our system's experimental evaluation of PEGASUS, BART, and T5 outperforms the current evaluation metrics in measuring factual consistency with a noticeable margin and demonstrates its statistical significance in measuring comprehensiveness and overall summary quality.

Syftet med studien var att analysera bilderböcker av två författare, Elsa Beskow och Pija Lindebaum, från olika tidsperioder med bred spridning och stor produktion. Syftet var också att analysera hur bilderböcker har förändrats över tid. Genom den utvalda barnlitteraturen undersöktes genusframställningen utifrån respektive tidsperiod och författare genom både bild- och textanalys. Studien utgick från Hirdmans genusteori och analyserades via ett analysschema inspirerat av Nikolajeva. Analysens resultat diskuteras utifrån ett didaktiskt perspektiv.                      Studiens resultat visar att barnens vuxna förebilder ofta framställs stereotypiskt men att det mellan författarnas tidsperioder skett en omfattande utveckling i porträtteringen av könsöverskridande barnkaraktärer i barnlitteraturen. Ett överraskande resultat var att illustrationernas färger tydligt kopplades till karaktärernas kön. Ljusa färger som till exempelvis röd, rosa, orange kopplades till de feminina karaktärerna medan mörkare färger som till exempelvis blå, grön och grå kopplades till de maskulina karaktärerna. Ytterligare resultat var att det framkom mönster kring de övergripande handlingarna beroende av vilket kön den könsöverskridande karaktären hade. I de bilderböckerna där flickorna framställs som maskulina hade handlingen ett annat fokusområde. I bilderböckerna där pojkarna går över mot de feminina dragen fokuserar handlingen specifikt på det könsöverskridande som sker hos den feminina pojken. Detta synliggör vikten av att som pedagog fundera över hur val av barnlitteratur påverkar barn och deras identitetsutveckling utifrån ett genusperspektiv.

Yes / Ambassador Tibor Toth, Chairman of the Ad Hoc Group, gives an address relating to the submission to the Ad Hoc Group of the Chairman's Composite Text of the Protocol to the Biological and Toxin Weapons Convention.

Based on Kim Witte's proven Extended Parallel Process Model, a mobile communication system was developed in which men were sent reminders about their health. This study focused on reminding men about testicular self-examination (TSE), a proactive behavior used to detect testicular cancer, through the use of text messaging. A cohort of 75 men were recruited for this study and placed into one of four groups. All participants were provided with information concerning TSE and told to perform the exam monthly; two of the four groups were sent reminders via text message while the other two groups were told once about the behavior. An original 30-item survey was used to measure intention. Proper data analysis could not be performed due to an attrition rate of 71%. Nonetheless, a significant relationship was observed between pre- and post-test adherence as reported by the participants. In addition, the measurement tool was assessed and determined to be useful in measuring intention to perform TSE. Internal consistency measures were reported as 0.672 and 0.626, both of which would have been higher with a larger sample size. While further research and analysis is recommended, this study has laid a foundation for a way to communicate with young men about their health.

Document clustering is a widely used strategy for information retrieval and text data mining. In traditional document clustering systems, documents are represented as a bag of independent words. In this project, we propose to enrich the representation of a document by incorporating semantic information and syntactic information. Semantic analysis and syntactic analysis are performed on the raw text to identify this information. A detailed survey of current research in natural language processing, syntactic analysis, and semantic analysis is provided. Our experimental results demonstrate that incorporating semantic information and syntactic information can improve the performance of our document clustering system for most of our data sets. A statistically significant improvement can be achieved when we combine both syntactic and semantic information. Our experimental results using compound words show that using only compound words does not improve the clustering performance for our data sets. When the compound words are combined with original single words, the combined feature set gets slightly better performance for most data sets. But this improvement is not statistically significant. In order to select the best clustering algorithm for our document clustering system, a comparison of several widely used clustering algorithms is performed. Although the bisecting K-means method has advantages when working with large datasets, a traditional hierarchical clustering algorithm still achieves the best performance for our small datasets.

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