Designing E-comic for English Reading Material for Grade Eight in Tanjungpinang

The research aimed to design an e-comic for English Reading Material for online learning at grade eight of SMPN 11 Tanjungpinang. The design of e-comic for English reading material can be considered to make the students interested and motivated in learning English during online environment. This research focused on designing English reading material about recount text for grade eight. The research design was R&D based on ADDE by Richey and Klein consisted of analyze, design, develop, and evaluation. The subject of this research was the 8.4 class students which consisted of 25 students. The instruments were interview and questionnaires. The researcher was analyzed the data by using qualitative and quantitative techniques. The results of the research showed that the practicality of e-comic was very high (91.5%). It can be concluded that the e-comic for English reading material was interested, motivated, and contextualized to learn by the students at SMPN 11 Tanjungpinang

Image Retrieval in Digital Libraries - A Large Scale Multicollection Experimentation of Machine Learning techniques

International audienceWhile historically digital heritage libraries were first powered in image mode, they quickly took advantage of OCR technology to index printed collections and consequently improve the scope and performance of the information retrieval services offered to users. But the access to iconographic resources has not progressed in the same way, and the latter remain in the shadows: manual incomplete and heterogeneous indexation, data silos by iconographic genre. Today, however, it would be possible to make better use of these resources, especially by exploiting the enormous volumes of OCR produced during the last two decades, and thus valorize these engravings, drawings, photographs, maps, etc. for their own value but also as an attractive entry point into the collections, supporting discovery and serenpidity from document to document and collection to collection. This article presents an ETL (extract-transform-load) approach to this need, that aims to: Identify andextract iconography wherever it may be found, in image collections but also in printed materials (dailies, magazines, monographies); Transform, harmonize and enrich the image descriptive metadata (in particular with machine learning classification tools); Load it all into a web app dedicated to image retrieval. The approach is pragmatically dual, since it involves leveraging existing digital resources and (virtually) on-the-shelf technologies.Si historiquement, les bibliothèques numériques patrimoniales furent d’abord alimentées par des images, elles profitèrent rapidement de la technologie OCR pour indexer les collections imprimées afin d’améliorer périmètre et performance du service de recherche d’information offert aux utilisateurs. Mais l’accès aux ressources iconographiques n’a pas connu les mêmes progrès et ces dernières demeurent dans l’ombre : indexation manuelle lacunaire, hétérogène et non viable à grande échelle ; silos documentaires par genre iconographique ; recherche par le contenu (CBIR, content-based image retrieval) encore peu opérationnelle sur les collections patrimoniales. Aujourd’hui, il serait pourtant possible de mieux valoriser ces ressources, en particulier en exploitant les énormes volumes d’OCR produits durant les deux dernières décennies (tant comme descripteur textuel que pour l’identification automatique des illustrations imprimées). Et ainsi mettre en valeur ces gravures, dessins, photographies, cartes, etc. pour leur valeur propre mais aussi comme point d’entrée dans les collections, en favorisant découverte et rebond de document en document, de collection à collection. Cet article décrit une approche ETL (extract-transform-load) appliquée aux images d’une bibliothèque numérique à vocation encyclopédique : identifier et extraire l’iconographie partout où elle se trouve (dans les collections image mais aussi dans les imprimés : presse, revue, monographie) ; transformer, harmoniser et enrichir ses métadonnées descriptives grâce à des techniques d’apprentissage machine – machine learning – pour la classification et l’indexation automatiques ; charger ces données dans une application web dédiée à la recherche iconographique (ou dans d’autres services de la bibliothèque). Approche qualifiée de pragmatique à double titre, puisqu’il s’agit de valoriser des ressources numériques existantes et de mettre à profit des technologies (quasiment) mâtures

Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project

From July 16-to November 8, 2019, the Aida digital libraries research team at the University of Nebraska-Lincoln collaborated with the Library of Congress on “Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project.“ This demonstration project sought to (1) develop and investigate the viability and feasibility of textual and image-based data analytics approaches to support and facilitate discovery; (2) understand technical tools and requirements for the Library of Congress to improve access and discovery of its digital collections; and (3) enable the Library of Congress to plan for future possibilities. In pursuit of these goals, we focused our work around two areas: extracting and foregrounding visual content from Chronicling America (chroniclingamerica.loc.gov) and applying a series of image processing and machine learning methods to minimally processed manuscript collections featured in By the People (crowd.loc.gov). We undertook a series of explorations and investigated a range of issues and challenges related to machine learning and the Library’s collections. This final report details the explorations, addresses social and technical challenges with regard to the explorations and that are critical context for the development of machine learning in the cultural heritage sector, and makes several recommendations to the Library of Congress as it plans for future possibilities. We propose two top-level recommendations. First, the Library should focus the weight of its machine learning efforts and energies on social and technical infrastructures for the development of machine learning in cultural heritage organizations, research libraries, and digital libraries. Second, we recommend that the Library invest in continued, ongoing, intentional explorations and investigations of particular machine learning applications to its collections. Both of these top-level recommendations map to the three goals of the Library’s 2019 digital strategy. Within each top-level recommendation, we offer three more concrete, short- and medium-term recommendations. They include, under social and technical infrastructures: (1) Develop a statement of values or principles that will guide how the Library of Congress pursues the use, application, and development of machine learning for cultural heritage. (2) Create and scope a machine learning roadmap for the Library that looks both internally to the Library of Congress and its needs and goals and externally to the larger cultural heritage and other research communities. (3) Focus efforts on developing ground truth sets and benchmarking data and making these easily available. Nested under the recommendation to support ongoing explorations and investigations, we recommend that the Library: (4) Join the Library of Congress’s emergent efforts in machine learning with its existing expertise and leadership in crowdsourcing. Combine these areas as “informed crowdsourcing” as appropriate. (5) Sponsor challenges for teams to create additional metadata for digital collections in the Library of Congress. As part of these challenges, require teams to engage across a range of social and technical questions and problem areas. (6) Continue to create and support opportunities for researchers to partner in substantive ways with the Library of Congress on machine learning explorations. Each of these recommendations speak to the investigation and challenge areas identified by Thomas Padilla in Responsible Operations: Data Science, Machine Learning, and AI in Libraries. This demonstration project—via its explorations, discussion, and recommendations—shows the potential of machine learning toward a variety of goals and use cases, and it argues that the technology itself will not be the hardest part of this work. The hardest part will be the myriad challenges to undertaking this work in ways that are socially and culturally responsible, while also upholding responsibility to make the Library of Congress’s materials available in timely and accessible ways. Fortunately, the Library of Congress is in a remarkable position to advance machine learning for cultural heritage organizations, through its size, the diversity of its collections, and its commitment to digital strategy

Embodied EFL reading activity: Let’s produce comics

While theories of embodied cognition have been investigated in lab experiments with proficient readers, currently no studies have applied these theories to improving reading comprehension for low-proficiency readers. Using an embodied cognition approach, this study investigated producing comics as an embodied reading activity. To compare effects on English as a foreign language (EFL) reading comprehension of narrative texts, 71 low-proficiency tertiary EFL readers were randomly assigned to one of two collaborative post-reading activity groups: comics production or translation. Before the activities, the participants were given background knowledge instruction for the narrative texts. Reading comprehension was assessed by a true-false test, followed by a semi-structured focus group interview. The results show that the comics production group outperformed the translation group in reading comprehension. Moreover, evidence from interviews shows the comics production activity assisted low-proficiency EFL readers in constructing multimodal representations of what they read, improving the depth of their reading comprehension

Publishing as Architectural Practice

Appropriating its title from the recent collection of essays Publishing as Artistic Practice (ed. Annette Gilbert, Sternberg Press, 2016) - in which publishing is explored as a recently appropriated form of artistic practice in itself - this article begins by asserting that, contrary to the art world, the act of publishing has always been an integral aspect of architectural practice, arguably dating back to Vitruvius, at which time the architectural publication was founded as a means of authenticating, distributing and subsequently developing architectural discourse and practice. This article focuses on architectural books – the book being architecture’s longest running, most abundant, and most celebrated form of publishing – and book design in particular as a publishing practice. Although there is a considerable existing body of research canonizing the history and development of the architectural book, particularly between the mid-sixteenth and mid-twentieth centuries, the majority of this research has focused on book content rather than book design. The book is still intrinsic to the proliferation of architectural discourse, culture and the profession, but the lack of debate and development around the theories of its form and its futures is limiting the architectural book to become increasingly self-referential as a designed entity. The market-led restrictions imposed by the publishing industry are limiting the architectural book - this is the principal time crime outlined here - the design of the architectural book is in stasis due to the publishing industry: it is stuck in time. This article looks to determine how architectural book designing can be considered as relational to architectural practice, through an exploration of how book design has been theorized as a formal and material interplay and manifestation of spatial ideas – chiefly through the theories of “book usage”, “intermedia”, “topological” and “ergodic” literatures, “visual language” semiotics, and “metamediality”, originating from studies in literature, design, media and comics. Examples from my own architectural book design research praxis are discussed in relation to these theories, with a more thorough examination of three recent book works that are used as an armature to speculate on the potential futures of architectural book design, making and publishing practice. One hopes that this article will arouse debate and encourage more enthusiasm for book works as designed manifestations of spatial ideas. One might even go so far as declaring architectural book designs to be architectural projects

Identity-Aware Semi-Supervised Learning for Comic Character Re-Identification

Character re-identification, recognizing characters consistently across different panels in comics, presents significant challenges due to limited annotated data and complex variations in character appearances. To tackle this issue, we introduce a robust semi-supervised framework that combines metric learning with a novel 'Identity-Aware' self-supervision method by contrastive learning of face and body pairs of characters. Our approach involves processing both facial and bodily features within a unified network architecture, facilitating the extraction of identity-aligned character embeddings that capture individual identities while preserving the effectiveness of face and body features. This integrated character representation enhances feature extraction and improves character re-identification compared to re-identification by face or body independently, offering a parameter-efficient solution. By extensively validating our method using in-series and inter-series evaluation metrics, we demonstrate its effectiveness in consistently re-identifying comic characters. Compared to existing methods, our approach not only addresses the challenge of character re-identification but also serves as a foundation for downstream tasks since it can produce character embeddings without restrictions of face and body availability, enriching the comprehension of comic books. In our experiments, we leverage two newly curated datasets: the 'Comic Character Instances Dataset', comprising over a million character instances and the 'Comic Sequence Identity Dataset', containing annotations of identities within more than 3000 sets of four consecutive comic panels that we collected.Comment: 18 pages, 9 Figure