4,918 research outputs found
Patterns and Variation in English Language Discourse
The publication is reviewed post-conference proceedings from the international 9th Brno Conference on Linguistics Studies in English, held on 16–17 September 2021 and organised by the Faculty of Education, Masaryk University in Brno. The papers revolve around the themes of patterns and variation in specialised discourses (namely the media, academic, business, tourism, educational and learner discourses), effective interaction between the addressor and addressees and the current trends and development in specialised discourses. The principal methodological perspectives are the comparative approach involving discourses in English and another language, critical and corpus analysis, as well as identification of pragmatic strategies and appropriate rhetorical means. The authors of papers are researchers from the Czech Republic, Italy, Luxembourg, Serbia and Georgia
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Semantics, Ontology and Explanation
The terms 'semantics' and 'ontology' are increasingly appearing together with
'explanation', not only in the scientific literature, but also in
organizational communication. However, all of these terms are also being
significantly overloaded. In this paper, we discuss their strong relation under
particular interpretations. Specifically, we discuss a notion of explanation
termed ontological unpacking, which aims at explaining symbolic domain
descriptions (conceptual models, knowledge graphs, logical specifications) by
revealing their ontological commitment in terms of their assumed truthmakers,
i.e., the entities in one's ontology that make the propositions in those
descriptions true. To illustrate this idea, we employ an ontological theory of
relations to explain (by revealing the hidden semantics of) a very simple
symbolic model encoded in the standard modeling language UML. We also discuss
the essential role played by ontology-driven conceptual models (resulting from
this form of explanation processes) in properly supporting semantic
interoperability tasks. Finally, we discuss the relation between ontological
unpacking and other forms of explanation in philosophy and science, as well as
in the area of Artificial Intelligence
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Mining Butterflies in Streaming Graphs
This thesis introduces two main-memory systems sGrapp and sGradd for performing the fundamental analytic tasks of biclique counting and concept drift detection over a streaming graph. A data-driven heuristic is used to architect the systems. To this end, initially, the growth patterns of bipartite streaming graphs are mined and the emergence principles of streaming motifs are discovered. Next, the discovered principles are (a) explained by a graph generator called sGrow; and (b) utilized to establish the requirements for efficient, effective, explainable, and interpretable management and processing of streams. sGrow is used to benchmark stream analytics, particularly in the case of concept drift detection.
sGrow displays robust realization of streaming growth patterns independent of initial conditions, scale and temporal characteristics, and model configurations. Extensive evaluations confirm the simultaneous effectiveness and efficiency of sGrapp and sGradd. sGrapp achieves mean absolute percentage error up to 0.05/0.14 for the cumulative butterfly count in streaming graphs with uniform/non-uniform temporal distribution and a processing throughput of 1.5 million data records per second. The throughput and estimation error of sGrapp are 160x higher and 0.02x lower than baselines. sGradd demonstrates an improving performance over time, achieves zero false detection rates when there is not any drift and when drift is already detected, and detects sequential drifts in zero to a few seconds after their occurrence regardless of drift intervals
"Le present est plein de l’avenir, et chargé du passé" : Vorträge des XI. Internationalen Leibniz-Kongresses, 31. Juli – 4. August 2023, Leibniz Universität Hannover, Deutschland. Band 2
[No abstract available]Deutschen Forschungsgemeinschaft (DFG)/Projektnr. 517991912VGH VersicherungNiedersächsisches Ministerium für Wissenschaft und Kultur (MWK
Referring to discourse participants in Ibero-Romance languages
Synopsis:
This volume brings together contributions by researchers focusing on personal pronouns in Ibero-Romance languages, going beyond the well-established variable of expressed vs. non-expressed subjects. While factors such as agreement morphology, topic shift and contrast or emphasis have been argued to account for variable subject expression, several corpus studies on Ibero-Romance languages have shown that the expression of subject pronouns goes beyond these traditionally established factors and is also subject to considerable dialectal variation. One of the factors affecting choice and expression of personal pronouns or other referential devices is whether the construction is used personally or impersonally. The use and emergence of new impersonal constructions, eventually also new (im)personal pronouns, as well as the variation found in the expression of human impersonality in different Ibero-Romance language varieties is another interesting research area that has gained ground in the recent years. In addition to variable subject expression, similar methods and theoretical approaches have been applied to study the expression of objects. Finally, the reference to the addressee(s) using different address pronouns and other address forms is an important field of study that is closely connected to the variable expression of pronouns. The present book sheds light on all these aspects of reference to discourse participants. The volume contains contributions with a strong empirical background and various methods and both written and spoken corpus data from Ibero-Romance languages. The focus on discourse participants highlights the special properties of first and second person referents and the factors affecting them that are often different from the anaphoric third person. The chapters are organized into three thematic sections: (i) Variable expression of subjects and objects, (ii) Between personal and impersonal, and (iii) Reference to the addressee
A Transitivity Analysis of International Ecological Discourse – Taking Remarks at the APEC Informal Economic Leaders’ Retreat as an Example
This study is guided by the International Ecosophy of “diversity and harmony, interaction and co-existence” proposed by He Wei and Wei Rong, and draw on the analytical model of transitivity for international ecological discourse to analyze China’s President Xi Jinping’s Remarks at the APEC Informal Economic Leaders’ Retreat. The study finds that: first, in this speech, the action process plays a dominant role in the transitivity process, which explains that China not only plays more roles in the international community, but also attaches importance to and advocates starting from all aspects of action to establish an image of actively participating in international affairs and being responsible in the international community; second, in this speech, most of the speech discourse is beneficial discourse, few neutral discourse and no destructive discourse, which shows that China is committed to establishing a good international social ecological discourse system, so as to promote the benign development of the international social ecosystem
Industry 5.0 implications for inclusive sustainable manufacturing: an evidence-knowledge-based strategic roadmap
Despite the hype surrounding Industry 5.0 and its importance for sustainability, the micro-mechanisms through which this agenda can lead to socio-environmental values are largely understudied. The present study strived to address this knowledge gap by developing a strategic roadmap that outlines how Industry 5.0 can boost sustainable manufacturing. The study first conducted a content-centric literature review and identified 12 functions through which Industry 5.0 can inclusively boost sustainable manufacturing. The study further developed a strategic roadmap that identified the complex contextual relationships among the functions and explained how they should be synergistically leveraged to maximize their contribution to sustainability. Results reveal that value network integration, sustainable technology governance, sustainable business model innovation, and sustainable skill development are the most driver and tangible implications of Industry 5.0 for sustainable manufacturing. Alternatively, renewable integration and manufacturing resilience are among the most dependent and hard-to-reach sustainable functions of Industry 5.0, and their materialization requires major strategic collaboration among stakeholders. The strategic roadmap outlines how Industry 5.0 stakeholders can leverage the technological and functional constituents of this agenda to promote sustainable manufacturing inclusively
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GRAPH REPRESENTATION LEARNING WITH BOX EMBEDDINGS
Graphs are ubiquitous data structures, present in many machine-learning tasks, such as link prediction of products and node classification of scientific papers. As gradient descent drives the training of most modern machine learning architectures, the ability to encode graph-structured data using a differentiable representation is essential to make use of this data. Most approaches encode graph structure in Euclidean space, however, it is non-trivial to model directed edges. The naive solution is to represent each node using a separate source and target vector, however, this can decouple the representation, making it harder for the model to capture information within longer paths in the graph.
In this dissertation, we propose to model graphs by representing each node as a \textit{box} (a Cartesian product of intervals) where directed edges are captured by the relative containment of one box in another. Theoretical proof shows that our proposed box embeddings have the expressiveness to represent any \emph{directed acyclic graph}. We also perform rigorous empirical evaluations of vector, hyperbolic, and region-based geometric representations on several families of synthetic and real-world directed graphs. Extensive experimental results suggest that the box containment can allow for transitive relationships to be modeled easily. We further propose t-Box, a variant of box embeddings that learns the temperature together during training. t-Box uses a learned smoothing parameter to achieve better representational capacity than vector models in low dimensions, while also avoiding performance saturation common to other geometric models in high dimensions.
Though promising, modeling directed graphs that both contain cycles and some element of transitivity, two properties common in real-world settings, is challenging. Box embeddings, which can be thought of as representing the graph as an intersection over some learned super-graphs, have a natural inductive bias toward modeling transitivity, but (as we prove) cannot model cycles. To address this issue, we propose binary code box embeddings, where a learned binary code selects a subset of graphs for intersection. We explore several variants, including global binary codes (amounting to a union over intersections) and per-vertex binary codes (allowing greater flexibility) as well as methods of regularization. Theoretical and empirical results show that the proposed models not only preserve a useful inductive bias of transitivity but also have sufficient representational capacity to model arbitrary graphs, including graphs with cycles.
Lastly, we discuss the use case where box embeddings are not free parameters but are produced by functions. In particular, we explore whether neural networks can map node features into the box space. This is critical in many real-world scenarios. On the one hand, graphs are sparse and the majority of vertices only have few connections or are completely isolated. On the other hand, there may exist rich node features such as attributes and descriptions, that could be useful for prediction tasks. The experimental analysis points out both the effectiveness and insufficiency of multi-layer perceptron-based encoders under different circumstances
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