Understanding how texts are produced is crucial not only for the development of theoretical models and writing strategies, but also for practical applications. However, the writing process itself--- including intermediate versions, copy-paste actions, input from co-authors or LLMs---remains invisible in the final text. This study addresses this gap by visualizing fine-grained keystroke logging data to capture both the product (final text) and the process (writer's actions) at sentence and text level. We design and implement custom JavaScript visualizations of linguistically processed keystroke logging data. Our pilot study examines data from nine students writing under identical conditions; we analyze temporal, spatial, and structural aspects of writing. The results reveal diverse, nonlinear writing strategies and suggest that individualized process visualizations can inform both document engineering and writing analytics. The novel visualization types we present demonstrate how process and product can be meaningfully integrated.
Non-robustness of image classifiers to subtle, adversarial perturbations is a well-known failure mode. Defenses against such attacks are typically evaluated by measuring the error rate on perturbed versions of the natural test set, quantifying the worst-case performance within a specified perturbation budget. However, these evaluations often isolate specific perturbation types, underestimating the adaptability of real-world adversaries who can modify or compose attacks in unforeseen ways. In this work, we show that models considered robust to strong attacks, such as AutoAttack, can be compromised by a simple modification of the weaker FGSM attack, where the adversarial perturbation is slightly transformed prior to being added to the input. Despite the attack's simplicity, robust models that perform well against standard FGSM become vulnerable to this variant. These findings suggest that current defenses may generalize poorly beyond their assumed threat models and can achieve inflated robustness scores under narrowly defined evaluation settings.
The perception of the readability of biomedical texts varies depending on the reader's profile, a disparity further amplified by the intrinsic complexity of these documents and the unequal distribution of health literacy within the population. Although 72% of Internet users consult medical information online, a significant proportion have difficulty understanding it. To ensure that texts are accessible to a diverse audience, it is essential to assess readability. However, conventional readability formulas, designed for general texts, do not take this diversity into account, underlining the need to adapt evaluation tools to the specific needs of biomedical texts and the heterogeneity of readers. To address this gap, we propose a novel readability assessment method tailored to three distinct audiences: expert adults, non-expert adults, and children. Our approach is built upon a structured, bilingual biomedical corpus of 20,008 documents (8,854 in French, 11,154 in English), compiled from multiple sources to ensure diversity in both content and audience. Specifically, the French corpus combines texts from Cochrane and Wikipedia/Vikidia, both of which are subsets of the CLEAR corpus, while the English corpus merges documents from the Cochrane Library, Plaba, and Science Journal for Kids. For each original expert-level text, domain specialists produced simplified variants calibrated specifically to the comprehension abilities of non-expert adults or children. Every document is therefore explicitly labeled by its target audience. Leveraging this resource, we trained a diverse suite of classifiers, from classical approaches (e.g., XGBoost, SVM) to classifiers built upon language models (e.g., BERT, CamemBERT, BioBERT, DrBERT). We then designed a hybrid architecture "BioReadNet" that integrates transformer embeddings with expert-driven linguistic features, achieving a macro-averaged F1 score of 0.987.
Image binarization is fundamental for document image processing. The performance of binarization algorithms depends on several factors that range from the quality of the digitalization devices to the intrinsic features of the document itself and the kind and intensity of the noises present in the image. This assessment on binarizing photographed documents evaluated the quality, time, space, and performance of five new algorithms and ninety-eight "classical" algorithms. The test data set is composed of laser and deskjet printed documents, photographed using six widely used mobile devices with the strobe flash on, off, and in auto modes under three different angles and places of capture.
This demo introduces Doenba Edit, a user-friendly, AI-powered platform developed by Librum Technologies, Inc., designed for seamless editing and typesetting of academic writing within a word processor-like interface. It supports the entire academic writing workflow, from outlining and idea development to drafting, revising, typesetting, and cross-referencing, assisted by integrated AI tools at every stage. This offers a comprehensive solution for enhancing both the quality and efficiency of producing scholarly work.
