Hasil untuk "History of scholarship and learning. The humanities"

Anoop C. Choolayil, Panneer Devaraju, Muhammed Jabir et al.

Abstract From a spatial justice perspective, this article argues that the spatial plane is ‘constructed’ and that socio-spatial configurations can create and intensify health inequities for vulnerable sections. Employing a mixed methods design, the study inquired about the impact of socio- spatial elements on Vector-Borne Diseases (VBDs) in Puducherry, India. The quantitative phase involved geospatial mapping and household surveys of 650 random individuals from Puducherry who had recovered from dengue, chikungunya or scrub typhus over one year, followed by qualitative in-depth interviews of 30 individuals selected purposively from the first phase participants. The findings show how people are ‘placed’ in spatial planes that are constantly being ‘constructed’ by social forces, leading to an inevitable ‘entanglement’ with vectors. While certain sections possess the resources to ‘modify’ or ‘nullify’ these disadvantages, the disadvantaged continue to live in this entanglement. The complex interaction of geographical and social components in the case of VBDs necessitates debates on health equity from a spatial justice perspective. In fact, it can be argued that spatial justice serves as the foundation for health equity, on which other interventions can be built in communities endemic to VBDs.

Hamzeh Moradi

Abstract To successfully adopt and implement information and communication technology (ICT) in language teaching and learning processes, it is essential to understand how and why language teachers use ICT and identify the barriers they encounter when attempting to incorporate ICT into their lessons. This study examined English as a foreign language (EFL) teachers’ attitudes at the tertiary level towards integrating ICT in their classes based on technology acceptance model (TAM) factors using a questionnaire consisting of both closed- and open-ended questions. Data were collected from 68 EFL teachers at three institutions in Guangzhou, southern China. The findings validated the efficacy of the TAM components in assessing the integration of ICT in higher education EFL classes in China based on teachers’ perceptions and attitudes and revealed teachers’ positive attitudes and significant tendencies towards ICT integration in English classes. However, teachers’ digital literacy and pedagogical and technological skills were found to be insufficient to successfully integrate ICT into tertiary English education. Most participants expressed a desire for in-service ICT training. Moreover, various obstacles were found to affect ICT integration in higher education English classes in China, including personal, institutional, and technological barriers.

Ariel Horowitz

In his seminal book <i>Zakhor: Jewish History and Jewish Memory</i> (1982), renowned historian Yosef Hayim Yerushalmi argues that it is literature and culture, and not historiography, that shaped Jewish collective memory for generations. In Yerushalmi’s telling, the boundaries between historiography and literature, “truth” and “myth,” are set and strict. However, the reception of Yerushalmi’s work itself challenges this assumption and obscures the clear-cut distinctions between literature and historiography. This paper reads Yerushalmi’s book alongside its preface, written by Harold Bloom, in an attempt to understand <i>Zakhor</i> not only as a historiographic argument, but as a narrative of Jewish modernity, a literary meditation, embodying the very shift in collective memory that Yerushalmi himself lamented. The paper then explores the ways in which Yerushalmi’s work has inspired two prominent contemporary American Jewish writers: Joshua Cohen, in his novel <i>The Netanyahus</i> (2021), and Nicole Krauss, in her short story “Zusya on the Roof” (2013). In their literary work, one can hear echoes of Yerushalmi’s work, distinct and identifiable, yet incorporated in a fictional, imaginative world. <i>Zakhor</i> thus serves not only as an inspiration but as a catalyst for a deep, insightful rendering of Jewish history and one’s grappling with it.

Mohammad H. Zhoolideh Haghighi, Alireza Ghasrimanesh, Habib Khosroshahi

For monitoring the night sky conditions, wide-angle all-sky cameras are used in most astronomical observatories to monitor the sky cloudiness. In this manuscript, we apply a deep-learning approach for automating the identification of precipitation clouds in all-sky camera data as a cloud warning system. We construct our original training and test sets using the all-sky camera image archive of the Iranian National Observatory (INO). The training and test set images are labeled manually based on their potential rainfall and their distribution in the sky. We train our model on a set of roughly 2445 images taken by the INO all-sky camera through the deep learning method based on the EfficientNet network. Our model reaches an average accuracy of 99\% in determining the cloud rainfall's potential and an accuracy of 96\% for cloud coverage. To enable a comprehensive comparison and evaluate the performance of alternative architectures for the task, we additionally trained three models LeNet, DeiT, and AlexNet. This approach can be used for early warning of incoming dangerous clouds toward telescopes and harnesses the power of deep learning to automatically analyze vast amounts of all-sky camera data and accurately identify precipitation clouds formations. Our trained model can be deployed for real-time analysis, enabling the rapid identification of potential threats, and offering a scaleable solution that can improve our ability to safeguard telescopes and instruments in observatories. This is important now that numerous small and medium-sized telescopes are increasingly integrated with smart control systems to reduce manual operation.

Maria Levchenko

Digital humanities scholars increasingly use Large Language Models for historical document digitization, yet lack appropriate evaluation frameworks for LLM-based OCR. Traditional metrics fail to capture temporal biases and period-specific errors crucial for historical corpus creation. We present an evaluation methodology for LLM-based historical OCR, addressing contamination risks and systematic biases in diplomatic transcription. Using 18th-century Russian Civil font texts, we introduce novel metrics including Historical Character Preservation Rate (HCPR) and Archaic Insertion Rate (AIR), alongside protocols for contamination control and stability testing. We evaluate 12 multimodal LLMs, finding that Gemini and Qwen models outperform traditional OCR while exhibiting over-historicization: inserting archaic characters from incorrect historical periods. Post-OCR correction degrades rather than improves performance. Our methodology provides digital humanities practitioners with guidelines for model selection and quality assessment in historical corpus digitization.

Geonwoo Cho, Jaegyun Im, Doyoon Kim et al.

Designing effective task sequences is crucial for curriculum reinforcement learning (CRL), where agents must gradually acquire skills by training on intermediate tasks. A key challenge in CRL is to identify tasks that promote exploration, yet are similar enough to support effective transfer. While recent approach suggests comparing tasks via their Structural Causal Models (SCMs), the method requires access to ground-truth causal structures, an unrealistic assumption in most RL settings. In this work, we propose Causal-Paced Deep Reinforcement Learning (CP-DRL), a curriculum learning framework aware of SCM differences between tasks based on interaction data approximation. This signal captures task novelty, which we combine with the agent's learnability, measured by reward gain, to form a unified objective. Empirically, CP-DRL outperforms existing curriculum methods on the Point Mass benchmark, achieving faster convergence and higher returns. CP-DRL demonstrates reduced variance with comparable final returns in the Bipedal Walker-Trivial setting, and achieves the highest average performance in the Infeasible variant. These results indicate that leveraging causal relationships between tasks can improve the structure-awareness and sample efficiency of curriculum reinforcement learning. We provide the full implementation of CP-DRL to facilitate the reproduction of our main results at https://github.com/Cho-Geonwoo/CP-DRL.

Enos Paselle, Etika Khairina, Mohammad Taufik et al.

This study evaluates sustainable transportation development in Batam City, focusing on the accessibility of public transportation for persons with disabilities as a sustainability indicator. Utilizing qualitative research methods, primary data were collected through interviews, complemented by secondary data from various sources such as Department of Transportation reports, Batam City Government documents, and online media. Data analysis employed triangulation, supported by Vosviewer Tools for processing secondary data. Findings reveal inadequate transportation accessibility services for people with disabilities in Batam City. Effectiveness, efficiency, adequacy, and equality for this demographic remain suboptimal, indicating a lack of sustainable and inclusive approaches. Regional regulations concerning disabilities are not fully implemented, and there's a lack of management strategies for transportation or Transport Demand Management tailored to assist people with disabilities. The Bus Rapid Transit (BRT) system's ineffectiveness hampers transportation connectivity, exacerbating access challenges. Essential disability facilities like ramps and bell signals are unevenly provided, while the limited number of special seats creates difficulties in prioritizing pregnant women and the elderly. Consequently, the current serviceability access falls short of meeting the standard needs of people with disabilities, as envisioned in Sustainable Transportation frameworks.

Amaral Rodrigues Gomes, Erlando da Silva Rêses

O presente artigo analisa como Karl Marx e Friedrich Engels compreendiam a literatura como meio de (re)produção capitalista, indicando suas contribuições para (re)construção de uma nova sociedade, a partir da consciência revolucionária. Discorremos sobre a trajetória desses autores, entendendo as manifestações literárias como potenciais elementos para estratégias de ensino e aprendizagem capazes de construir uma consciência proletária, articulada a literatura revolucionária. Embasamo-nos em Cotrim (2016), Lukács (2010), Silva (2012) e Vedda (2003), a partir de uma pesquisa de cunho qualitativo, pautada na revisão bibliográfica. Articulamos, aos preceitos de Marx e Engels, dois poemas: O Bicho de Manuel Bandeira (1986) e Lixo de Augusto de Campos (1986), indicando caminhos para a utilização da literatura voltada à conscientização revolucionária. Nossos resultados apontam a importância da aproximação desses teóricos à realidade dos indivíduos desprovidos da lógica capitalista e neoliberal como uma forma de libertação das desigualdades, condicionamentos sociais e exploração humana.

Olga Cherednichenko, Dmytro Chernyshov, Dmytro Sytnikov et al.

This research paper delves into the innovative integration of Shannon entropy and rough set theory, presenting a novel approach to generalize the evaluation approach in machine learning. The conventional application of entropy, primarily focused on information uncertainty, is extended through its combination with rough set theory to offer a deeper insight into data's intrinsic structure and the interpretability of machine learning models. We introduce a comprehensive framework that synergizes the granularity of rough set theory with the uncertainty quantification of Shannon entropy, applied across a spectrum of machine learning algorithms. Our methodology is rigorously tested on various datasets, showcasing its capability to not only assess predictive performance but also to illuminate the underlying data complexity and model robustness. The results underscore the utility of this integrated approach in enhancing the evaluation landscape of machine learning, offering a multi-faceted perspective that balances accuracy with a profound understanding of data attributes and model dynamics. This paper contributes a groundbreaking perspective to machine learning evaluation, proposing a method that encapsulates a holistic view of model performance, thereby facilitating more informed decision-making in model selection and application.

Carmen Martin-Turrero, Maxence Bouvier, Manuel Breitenstein et al.

We seek to enable classic processing of continuous ultra-sparse spatiotemporal data generated by event-based sensors with dense machine learning models. We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding based on PointNet models -- the ALERT module -- that can continuously integrate new and dismiss old events thanks to a leakage mechanism, (2) a flexible readout of the embedded data that allows to feed any downstream model with always up-to-date features at any sampling rate, (3) exploiting the input sparsity in a patch-based approach inspired by Vision Transformer to optimize the efficiency of the method. These embeddings are then processed by a transformer model trained for object and gesture recognition. Using this approach, we achieve performances at the state-of-the-art with a lower latency than competitors. We also demonstrate that our asynchronous model can operate at any desired sampling rate.

Oleksandr Melnyk

This article elucidates the reconstruction of the Soviet security apparatus during World War II in what today is western Ukraine. In late 1943 to early 1944, six operational groups of the People’s Commissariat of State Security of the Ukrainian Soviet Socialist Republic headed to the Axis occupied territories with orders to re-establish contacts with Soviet secret agents and create a support infrastructure for the deployment of other operational groups, special purposes units, and individual agents, as well as to infiltrate organizations of Polish and Ukrainian nationalists. The essay examines Soviet special operations within the context of state efforts to project power into the Axis occupied territories. It sheds light on the objectives of Soviet security agencies and on the activities of individual units in the field.

Badih Ghazi, Pritish Kamath, Ravi Kumar et al.

We consider the learning--unlearning paradigm defined as follows. First given a dataset, the goal is to learn a good predictor, such as one minimizing a certain loss. Subsequently, given any subset of examples that wish to be unlearnt, the goal is to learn, without the knowledge of the original training dataset, a good predictor that is identical to the predictor that would have been produced when learning from scratch on the surviving examples. We propose a new ticketed model for learning--unlearning wherein the learning algorithm can send back additional information in the form of a small-sized (encrypted) ``ticket'' to each participating training example, in addition to retaining a small amount of ``central'' information for later. Subsequently, the examples that wish to be unlearnt present their tickets to the unlearning algorithm, which additionally uses the central information to return a new predictor. We provide space-efficient ticketed learning--unlearning schemes for a broad family of concept classes, including thresholds, parities, intersection-closed classes, among others. En route, we introduce the count-to-zero problem, where during unlearning, the goal is to simply know if there are any examples that survived. We give a ticketed learning--unlearning scheme for this problem that relies on the construction of Sperner families with certain properties, which might be of independent interest.

Ajay Mandlekar, Soroush Nasiriany, Bowen Wen et al.

Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents. However, the demonstrations can be extremely costly and time-consuming to collect. We introduce MimicGen, a system for automatically synthesizing large-scale, rich datasets from only a small number of human demonstrations by adapting them to new contexts. We use MimicGen to generate over 50K demonstrations across 18 tasks with diverse scene configurations, object instances, and robot arms from just ~200 human demonstrations. We show that robot agents can be effectively trained on this generated dataset by imitation learning to achieve strong performance in long-horizon and high-precision tasks, such as multi-part assembly and coffee preparation, across broad initial state distributions. We further demonstrate that the effectiveness and utility of MimicGen data compare favorably to collecting additional human demonstrations, making it a powerful and economical approach towards scaling up robot learning. Datasets, simulation environments, videos, and more at https://mimicgen.github.io .

Morris Alper, Hadar Averbuch-Elor

Interactions between humans are diverse and context-dependent, but previous works have treated them as categorical, disregarding the heavy tail of possible interactions. We propose a new paradigm of learning human-human interactions as free text from a single still image, allowing for flexibility in modeling the unlimited space of situations and relationships between people. To overcome the absence of data labelled specifically for this task, we use knowledge distillation applied to synthetic caption data produced by a large language model without explicit supervision. We show that the pseudo-labels produced by this procedure can be used to train a captioning model to effectively understand human-human interactions in images, as measured by a variety of metrics that measure textual and semantic faithfulness and factual groundedness of our predictions. We further show that our approach outperforms SOTA image captioning and situation recognition models on this task. We will release our code and pseudo-labels along with Waldo and Wenda, a manually-curated test set for still image human-human interaction understanding.

Maohao Shen, Bowen Jiang, Jacky Yibo Zhang et al.

Active learning enables efficient model training by leveraging interactions between machine learning agents and human annotators. We study and propose a novel framework that formulates batch active learning from the sparse approximation's perspective. Our active learning method aims to find an informative subset from the unlabeled data pool such that the corresponding training loss function approximates its full data pool counterpart. We realize the framework as sparsity-constrained discontinuous optimization problems, which explicitly balance uncertainty and representation for large-scale applications and could be solved by greedy or proximal iterative hard thresholding algorithms. The proposed method can adapt to various settings, including both Bayesian and non-Bayesian neural networks. Numerical experiments show that our work achieves competitive performance across different settings with lower computational complexity.

عبدالله بن عبده بن جردي الحمدي

يهدف البحث إلى إظهار طبيعة الإعلام الإسلامي، وتوضيح معايير مخاطبة الجمهور، والخدمة الإعلامية التي ينبغي أن توجه لهذا الجمهور، وتبيين قواعد صياغة المضــمون الإعلامي للجمهور، والوسائل التي يمكن استعمالها لنقل المضامين الإعلامية، وقد انقسم البحث إلى مقدمة وتمهيد ومبحثين وخاتمة؛ تطرق المبحث الأول إلى طبيعة الإعلام الإسلامي وجمهوره، ودرس المبحث الثاني معايير مخاطبة الجمهور، وتوصل إلى جملة من النتائج منها: أن هذا المنهج هو الذي يجب أن يتبعه القائمون على وسائل الإعلام عند مخاطبتهم لمختلف فئات الجمهور المستقبل وطبقاتهم، وأنه لا وجود له في التطبيق العملي في الدول الإسلامية، إلا في جوانب بسيطة وبرامج محدودة، ينقصها كثير من أساليب الصياغة والمعرفة بحال المخاطبين، وهذه الجوانب لا تمثل شيئًا أمام الكم الهائل من الممارسات الإعلامية الوضعية التي تسود مجتمعاتنا الإسلامية.

Ravi Kumar Thakur, MD-Nazmus Samin Sunbeam, Vinicius G. Goecks et al.

Approaches for teaching learning agents via human demonstrations have been widely studied and successfully applied to multiple domains. However, the majority of imitation learning work utilizes only behavioral information from the demonstrator, i.e. which actions were taken, and ignores other useful information. In particular, eye gaze information can give valuable insight towards where the demonstrator is allocating visual attention, and holds the potential to improve agent performance and generalization. In this work, we propose Gaze Regularized Imitation Learning (GRIL), a novel context-aware, imitation learning architecture that learns concurrently from both human demonstrations and eye gaze to solve tasks where visual attention provides important context. We apply GRIL to a visual navigation task, in which an unmanned quadrotor is trained to search for and navigate to a target vehicle in a photorealistic simulated environment. We show that GRIL outperforms several state-of-the-art gaze-based imitation learning algorithms, simultaneously learns to predict human visual attention, and generalizes to scenarios not present in the training data. Supplemental videos and code can be found at https://sites.google.com/view/gaze-regularized-il/.

Martin Wistuba, Tejaswini Pedapati

Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to existing methods, we consider this task as a ranking and transfer learning problem. We qualitatively show that by optimizing a pairwise ranking loss and leveraging learning curves from other datasets, our model is able to effectively rank learning curves without having to observe many or very long learning curves. We further demonstrate that our method can be used to accelerate a neural architecture search by a factor of up to 100 without a significant performance degradation of the discovered architecture. In further experiments we analyze the quality of ranking, the influence of different model components as well as the predictive behavior of the model.

Evgeny Kim, Roman Klinger

Emotions are a crucial part of compelling narratives: literature tells us about people with goals, desires, passions, and intentions. Emotion analysis is part of the broader and larger field of sentiment analysis, and receives increasing attention in literary studies. In the past, the affective dimension of literature was mainly studied in the context of literary hermeneutics. However, with the emergence of the research field known as Digital Humanities (DH), some studies of emotions in a literary context have taken a computational turn. Given the fact that DH is still being formed as a field, this direction of research can be rendered relatively new. In this survey, we offer an overview of the existing body of research on emotion analysis as applied to literature. The research under review deals with a variety of topics including tracking dramatic changes of a plot development, network analysis of a literary text, and understanding the emotionality of texts, among other topics. Based on this review, we point to a set of remaining challenges that constitute promising future research directions.

Weder FERREIRA DA SILVA, Felipe RICCIO SCHIEFLER

<p>En la primera década de la monarquía brasileña, el gobierno de Pedro I experimentó la oposición de sectores que estaban en contra de la opción monárquica como régimen político para Brasil. Esta oposición utilizaba la prensa como medio de propagación del ideario liberal y republicano. Ese fue el caso de los textos publicados en periódicos como <em>Sentinela da Liberdade</em>, editado por Cipriano Barata (1772- 1838) y <em>Sentinela do Serro</em>, editado por Teófilo Ottoni (1807- 1869). Al analizar estos textos a partir de las referencias de la llamada Historia de los Conceptos, procuraremos identificar en el material publicado entre la independência y los primeros años de regencia, los conceptos y narrativas políticas que derivaron del léxico republicano con el fin de comprender mejor el complejo escenario político que caracterizó al país en los años iniciales de la formación del Estado nacional.</p>