This article examines Alice’s Adventures in Wonderland from the perspective of childhood epistemology, arguing that childhood knowledge should not be understood as deficient in relation to adult-centered rationality, but rather as an autonomous epistemic domain constituted through experience, uncertainty, and contextual interaction. The study conceptualizes childhood not merely as a pedagogical category, but as a distinct epistemic position that exists in structural tension with dominant adult knowledge regimes. Its point of departure lies in the limited number of studies within childhood research that directly problematize the epistemic status of childhood. The theoretical framework integrates Miranda Fricker’s concept of epistemic injustice, John Dewey’s theory of experiential learning, and Michel Foucault’s analyses of knowledge–power relations. Metho- dologically, the study employs qualitative close reading and thematic analysis, focusing on the scenes of the Queen’s Croquet Ground, Advice from a Caterpillar, and the Mad Tea Party. The findings demonstrate that adult epistemology in the text operates through arbitrary authority, normalizing judgments, and exclusionary discourses, thereby exposing its own internal inconsistencies. In contrast, Alice’s modes of knowing are shaped through trial and error, embodied experience, and the suspension of fixed meaning. The article positions Alice’s Adventures in Wonderland as a critical epistemological space in which alternative forms of knowledge associated with childhood are literarily constructed, contributing to interdisciplinary debates in childhood studies, literary theory, and philosophy of knowledge.
The article explores the impact of informational fragmentation within the contemporary media environment on the formation of citizens’ political identity and its implications for democratic processes. Particular attention is given to the philosophical dimensions of this issue: the relationship between freedom of access to information, the search for truth, and the possibilities of authentic communication in the public sphere. The aim of the study is to identify the mechanisms of interaction between information flows and political socialization, to analyze cognitive polarization, and to assess the risks for public discourse understood as a space of meaning-making and democratic coexistence. To achieve this goal, the study employs methods of analyzing contemporary theoretical research in media ecology, political science, sociology, and the philosophy of communication, as well as a comparative analysis of national and international sources.
The findings demonstrate that informational fragmentation generates isolated informational segments that restrict citizens’ access to diverse perspectives and reinforce the effect of “echo chambers.” This leads to cognitive and political polarization, instability of political identity, and the deepening of socio-political division. At the same time, fragmentation creates new opportunities for individualized political participation, personalized access to information, and the expansion of the space of freedom of choice.
The practical significance of the study lies in the development of approaches to integrating diverse information flows into a unified public sphere, enhancing media literacy, and fostering mechanisms of intergroup dialogue. Special emphasis is placed on the necessity of combining technological and social strategies with a philosophical reflection on the values of democracy, truth, and communication. The results of the study may be applied to the optimization of state media policy, the design of educational programs, and the development of strategies to support democratic public discourse.
The conclusions highlight the dual character of the influence of informational fragmentation: it simultaneously stimulates democratic engagement while deepening citizens’ isolation, thus necessitating the elaboration of comprehensive strategies for integrating information flows and strengthening democratic unity on the basis of the philosophical principles of freedom, truth, and responsibility.
Purpose: Development of conceptual foundations of ethics by design and rethinking of subject-object relations in the context of digital ethics for the formation of more effective approaches to the design and management of ethically responsible technologies.
Design / Method / Approach: The research is based on an interdisciplinary approach combining methods of philosophical analysis, ethics, sociology of technology and research in the field of human-computer interaction. The methods of conceptual modeling, ethical analysis of technologies and scenario forecasting are used.
Findings: Considered approaches to the formation of a conceptual model of distributed ethical responsibility in complex sociotechnical systems. Proposed methodological approaches to the integration of ethical considerations at various stages of the life cycle of digital products. The potential of the ethics of care to solve the problems of vulnerability and dependence in the digital environment is investigated.
Theoretical implications: The work contributes to the development of the theory of technology ethics, offering a new perspective on the interaction between ethical principles and the processes of designing digital systems. The research expands the understanding of subject-object relations in the context of modern technologies.
Practical implications: The proposed approaches can be used to create methodologies for the ethical design of digital products, the formation of policies for the responsible use of technologies in organizations, and the development of educational programs on the ethics of technologies for engineers and designers.
Originality / Value: The novelty of the research lies in the development of an integrative approach to ethics by design that takes into account changes in subject-object relations in the digital age. The proposed approach is aimed at expanding the capabilities of existing models of ethical design and explores new perspectives for creating ethically responsible digital technologies.
Research limitations / Future research: Prospects include empirical testing of proposed models, development of specific tools for ethical audit of digital systems, and research into cultural aspects of the perception of ethics by design . The limitations are related to the rapid development of technologies, which may require constant adaptation of the proposed approaches.
Paper type: Theoretical.
Large vision-language models (LVLMs) are ignorant of the up-to-date knowledge, such as LLaVA series, because they cannot be updated frequently due to the large amount of resources required, and therefore fail in many cases. For example, if a LVLM was released on January 2024, and it wouldn't know the singer of the theme song for the new Detective Conan movie, which wasn't released until April 2024. To solve the problem, a promising solution motivated by retrieval-augmented generation (RAG) is to provide LVLMs with up-to-date knowledge via internet search during inference, i.e., internet-augmented generation (IAG), which is already integrated in some closed-source commercial LVLMs such as GPT-4V. However, the specific mechanics underpinning them remain a mystery. In this paper, we propose a plug-and-play framework, for augmenting existing LVLMs in handling visual question answering (VQA) about up-to-date knowledge, dubbed SearchLVLMs. A hierarchical filtering model is trained to effectively and efficiently find the most helpful content from the websites returned by a search engine to prompt LVLMs with up-to-date knowledge. To train the model and evaluate our framework's performance, we propose a pipeline to automatically generate news-related VQA samples to construct a dataset, dubbed UDK-VQA. A multi-model voting mechanism is introduced to label the usefulness of website/content for VQA samples to construct the training set. Experimental results demonstrate the effectiveness of our framework, outperforming GPT-4V by about 25% in accuracy.
Creation and curation of knowledge graphs can accelerate disease discovery and analysis in real-world data. While disease ontologies aid in biological data annotation, codified categories (SNOMED-CT, ICD10, CPT) may not capture patient condition nuances or rare diseases. Multiple disease definitions across data sources complicate ontology mapping and disease clustering. We propose creating patient knowledge graphs using large language model extraction techniques, allowing data extraction via natural language rather than rigid ontological hierarchies. Our method maps to existing ontologies (MeSH, SNOMED-CT, RxNORM, HPO) to ground extracted entities. Using a large ambulatory care EHR database with 33.6M patients, we demonstrate our method through the patient search for Dravet syndrome, which received ICD10 recognition in October 2020. We describe our construction of patient-specific knowledge graphs and symptom-based patient searches. Using confirmed Dravet syndrome ICD10 codes as ground truth, we employ LLM-based entity extraction to characterize patients in grounded ontologies. We then apply this method to identify Beta-propeller protein-associated neurodegeneration (BPAN) patients, demonstrating real-world discovery where no ground truth exists.
Since 1883, Picard-Vessiot theory had been developed as the Galois theory of differential field extensions associated to linear differential equations. Inspired by categorical Galois theory of Janelidze, and by using novel methods of precategorical descent applied to algebraic-geometric situations, we develop a Galois theory that applies to morphisms of differential schemes, and vastly generalises the linear Picard-Vessiot theory, as well as the strongly normal theory of Kolchin.
Artificial intelligence-enabled histopathological data analysis has become a valuable assistant to the pathologist. However, existing models lack representation and inference abilities compared with those of pathologists, especially in cancer subtype diagnosis, which is unconvincing in clinical practice. For instance, pathologists typically observe the lesions of a slide from global to local, and then can give a diagnosis based on their knowledge and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K) model to replicate the process of cancer subtype classification on a histopathological slide like a pathologist. Specifically, in the data-driven module, the bagging mechanism in ensemble learning is leveraged to integrate the histological features from various bags extracted by the embedding representation unit. Furthermore, a knowledge-driven module is established based on the Gestalt principle in psychology to build the three-dimensional (3D) expert knowledge space and map histological features into this space for metric. Then, the diagnosis can be made according to the Euclidean distance between them. Extensive experimental results on both public and in-house datasets demonstrate that the D&K model has a high performance and credible results compared with the state-of-the-art methods for diagnosing histopathological subtypes. Code: https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classification
Entity Resolution (ER) is the problem of determining when two entities refer to the same underlying entity. The problem has been studied for over 50 years, and most recently, has taken on new importance in an era of large, heterogeneous 'knowledge graphs' published on the Web and used widely in domains as wide ranging as social media, e-commerce and search. This chapter will discuss the specific problem of named ER in the context of personal knowledge graphs (PKGs). We begin with a formal definition of the problem, and the components necessary for doing high-quality and efficient ER. We also discuss some challenges that are expected to arise for Web-scale data. Next, we provide a brief literature review, with a special focus on how existing techniques can potentially apply to PKGs. We conclude the chapter by covering some applications, as well as promising directions for future research.
A. Fronzetti Colladon, F. Grippa, C. Broccatelli
et al.
Purpose: This study investigates the dynamics of knowledge sharing in healthcare, exploring some of the factors that are more likely to influence the evolution of idea sharing and advice seeking in healthcare. Design/methodology/approach: We engaged 50 pediatricians representing many subspecialties at a mid-size US children's hospital using a social network survey to map and measure advice seeking and idea sharing networks. Through the application of Stochastic Actor-Oriented Models, we compared the structure of the two networks prior to a leadership program and eight weeks post conclusion. Findings: Our models indicate that healthcare professionals carefully and intentionally choose with whom they share ideas and from whom to seek advice. The process is fluid, non-hierarchical and open to changing partners. Significant transitivity effects indicate that the processes of knowledge sharing can be supported by mediation and brokerage. Originality: Hospital administrators can use this method to assess knowledge-sharing dynamics, design and evaluate professional development initiatives, and promote new organizational structures that break down communication silos. Our work contributes to the literature on knowledge sharing in healthcare by adopting a social network approach, going beyond the dyadic level, and assessing the indirect influence of peers' relationships on individual networks.
Model architectures such as wav2vec 2.0 and HuBERT have been proposed to learn speech representations from audio waveforms in a self-supervised manner. When they are combined with downstream tasks such as keyword spotting and speaker verification, they provide state-of-the-art performance. However, these models use a large number of parameters, the smallest version of which has 95 million parameters. This constitutes a challenge for edge AI device deployments. In this paper, we investigate the application of knowledge distillation to speech representation learning (SRL) models followed by joint fine-tuning with multiple downstream voice-activated tasks. In our experiments on two such tasks, our approach results in nearly 75% reduction in model size while suffering only 0.1% accuracy and 0.9% equal error rate degradation compared to the full-size model. In addition, we show that fine-tuning the SRL models results in a significant performance boost compared to using frozen SRL models.
The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of contexts makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team DAMO-NLP proposes a knowledge-based system, where we build a multilingual knowledge base based on Wikipedia to provide related context information to the named entity recognition (NER) model. Given an input sentence, our system effectively retrieves related contexts from the knowledge base. The original input sentences are then augmented with such context information, allowing significantly better contextualized token representations to be captured. Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
This study examined the discourse of resistance in Fem Fani-Kayode’s (FFK) Facebook posts. FFK’s use of language of resistance has not attracted the attention of scholars, especially in Critical Discourse Analysis (CDA). The thrust of this work is to investigate and examine different resistance strategies in some of his political posts. Data for this work were collected from the Facebook page of Fani-Kayode. The posts were downloaded and saved on the laptop device for further analysis. The work adopts a purposive sampling method in data collection, which was preferred because it allows manual assessment of FFK posts to extract relevant data for this work. Five political posts were selected and downloaded from his Facebook page. The five posts were selected because they discussed critical political and security issues in the country. From the five posts, 12 extracts were culled because of their resourcefulness in the use of resistance strategies. This study adopted qualitative analysis using CDA because CDA helps to unravel inherent ideologies in the posts. The study identified and discussed different resistance strategies in the FFK posts on Facebook and their implications. The identified resistance strategies include: proposition, presupposition, negation, propaganda, and emotive lexis. The study submits that the strategies have political, social and academic implications for society. It concludes that social media users should filter information on the media before they react, to avoid the dissemination of wrong information and prevent conflict in the society.
Relevance. Meningococcal vaccine refers to any of the vaccines used to prevent infection by Neisseria meningitidis. Therefore, there is a great scientific and practical interest in the existing and developed menicococcal vaccines.Aims the review is to provide an analysis: literature data on the effectiveness of meningococcal vaccines of new generations - conjugated polysaccharide serogroups A, C, W and Y and protein serogroup B.Conclusions. With regard to conjugated vaccines, there are a large number of reliable observations confirming the high immunological and epidemiological effectiveness of these vaccine preparations, including the prevention of bacterial carriage and the development of herd immunity. These vaccines are weakly reactogenic, and in many countries, they are introduced into national immunization programs and in some countries are used as mandatory (UK) or in connection with the existing epidemic indications. The protein «vesicle» vaccine based on serogroup B meningococcal outer membrane proteins, showed high efficacy only in those cases when the protein composition of the strain that caused the morbidity corresponded to the composition (mainly in terms of the PorA subtype antigen) of the vaccine. Genetic-engineered vaccines containing only a few serogroup B meningococcal protein antigens with or without the addition of «vesicle» proteins are difficult to evaluate due to the small number of observations associated with low serogroup В prevalence, but in Great Britain, such vaccine was also introduced as mandatory in the national immunization schedule for babies. At the same time, new vaccines of serogroup B induce immune protection against some strains of meningococcus of other serogroups C, W, and Y, and even against other species of Neisseria, in particular - gonococcus. This circumstance gives rise to hope for the development of protein meningococcal vaccines with a wider spectrum of specificity than the group, and even than the species.
Through the filter of his specific two steps method in approaching theories of knowledge, Bagdasar seems to invite his interpreter to consider solely the systematic level. In the following study, I interpret Bagdasar’s conception of knowledge from a perspective that he did not explicitly assume. This approach gives an opportunity to discover not only the methodology of Bagdasar’s epistemological research, or a possible methodology for any “epistemological research”, but even Bagdasar’s own (non-explicit) position in relation to what and how knowledge might be. Of these three main points of my research, a special attention will be paid to Bagdasar’s so called “systematic approach”, in order to reveal the non-explicit commitments on which he built his own conception of knowledge. The pragmatic analysis of the results obtained by Bagdasar in his Theory of knowledge allows the uncovering of some tacit assumptions that seem to contradict the position of neutrality he explicitly assumed.
Alishiba Dsouza, Nicolas Tempelmeier, Ran Yu
et al.
OpenStreetMap is a rich source of openly available geographic information. However, the representation of geographic entities, e.g., buildings, mountains, and cities, within OpenStreetMap is highly heterogeneous, diverse, and incomplete. As a result, this rich data source is hardly usable for real-world applications. This paper presents WorldKG -- a new geographic knowledge graph aiming to provide a comprehensive semantic representation of geographic entities in OpenStreetMap. We describe the WorldKG knowledge graph, including its ontology that builds the semantic dataset backbone, the extraction procedure of the ontology and geographic entities from OpenStreetMap, and the methods to enhance entity annotation. We perform statistical and qualitative dataset assessment, demonstrating the large scale and high precision of the semantic geographic information in WorldKG.
Tarcísio Souza Costa, Simon Gottschalk, Elena Demidova
Semantic Question Answering (QA) is a crucial technology to facilitate intuitive user access to semantic information stored in knowledge graphs. Whereas most of the existing QA systems and datasets focus on entity-centric questions, very little is known about these systems' performance in the context of events. As new event-centric knowledge graphs emerge, datasets for such questions gain importance. In this paper, we present the Event-QA dataset for answering event-centric questions over knowledge graphs. Event-QA contains 1000 semantic queries and the corresponding English, German and Portuguese verbalizations for EventKG - an event-centric knowledge graph with more than 970 thousand events.
In this article we classify additive operations in connective K-theory with various torsion-free coefficients. We discover that the answer for the integral case requires understanding of the $\hat{\mathbb{Z}}$ one. Moreover, although integral additive operations are topologically generated by Adams operations, these are not reduced to infinite linear combinations of the latter ones. We describe a topological basis for stable operations and relate it to a basis of stable operations in graded K-theory. We classify multiplicative operations in both theories and show that homogeneous additive stable operations with $\hat{\mathbb{Z}}$-coefficients are topologically generated by stable multiplicative operations. This is not true for integral operations.
Abu Quwsar Ohi, M. F. Mridha, Farisa Benta Safir
et al.
Clustering is widely used in unsupervised learning method that deals with unlabeled data. Deep clustering has become a popular study area that relates clustering with Deep Neural Network (DNN) architecture. Deep clustering method downsamples high dimensional data, which may also relate clustering loss. Deep clustering is also introduced in semi-supervised learning (SSL). Most SSL methods depend on pairwise constraint information, which is a matrix containing knowledge if data pairs can be in the same cluster or not. This paper introduces a novel embedding system named AutoEmbedder, that downsamples higher dimensional data to clusterable embedding points. To the best of our knowledge, this is the first research endeavor that relates to traditional classifier DNN architecture with a pairwise loss reduction technique. The training process is semi-supervised and uses Siamese network architecture to compute pairwise constraint loss in the feature learning phase. The AutoEmbedder outperforms most of the existing DNN based semi-supervised methods tested on famous datasets.