180 research outputs found
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Predicate Matrix: an interoperable lexical knowledge base for predicates
183 p.La Matriz de Predicados (Predicate Matrix en inglés) es un nuevo recurso léxico-semántico resultado de la integración de múltiples fuentes de conocimiento, entre las cuales se encuentran FrameNet, VerbNet, PropBank y WordNet. La Matriz de Predicados proporciona un léxico extenso y robusto que permite mejorar la interoperabilidad entre los recursos semánticos mencionados anteriormente. La creación de la Matriz de Predicados se basa en la integración de Semlink y nuevos mappings obtenidos utilizando métodos automáticos que enlazan el conocimiento semántico a nivel léxico y de roles. Asimismo, hemos ampliado la Predicate Matrix para cubrir los predicados nominales (inglés, español) y predicados en otros idiomas (castellano, catalán y vasco). Como resultado, la Matriz de predicados proporciona un léxico multilingüe que permite el análisis semántico interoperable en múltiples idiomas
Comparing the production of a formula with the development of L2 competence
This pilot study investigates the production of a formula with the development of L2 competence over proficiency levels of a spoken learner corpus. The results show that the formula
in beginner production data is likely being recalled holistically from learners’ phonological
memory rather than generated online, identifiable by virtue of its fluent production in absence
of any other surface structure evidence of the formula’s syntactic properties. As learners’ L2
competence increases, the formula becomes sensitive to modifications which show structural
conformity at each proficiency level. The transparency between the formula’s modification
and learners’ corresponding L2 surface structure realisations suggest that it is the independent
development of L2 competence which integrates the formula into compositional language,
and ultimately drives the SLA process forward
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction
Static and dynamic metaphoricity in U.S.-China trade discourse:A transdisciplinary perspective
Metaphor scholars have widely explored metaphor use in political discourse. Nevertheless, the current research does not account for the ‘gradable metaphoricity’ in political discourse analysis. This dissertation fills this gap by addressing this specific issue in two frameworks: (1) viewing political metaphor from a static and gradient perspective (Source-Target mapping; Conventional vs. Novel vs. Dead), and (2) viewing political metaphor from a gradable and dynamic perspective (a matter of salience and awareness of metaphoricity). A systematic literature review in chapter 2 points out that the static and dynamic perspectives differ significantly in underlying assumptions and organizing principles, although both are indistinctly referred to by metaphor scholars as constituting a ‘gradable’ view. The former takes metaphor as a static conceptual unit or lexical unit, but the latter tends to accord a central role of activation of metaphoricity to metaphorical expressions. To launch a theoretical advancement about the dynamic view in political discourse, chapter 3 offers a usage-based model of gradable and dynamic metaphors—the YinYang Dynamics of Metaphoricity (YYDM). In addition, this thesis investigates political metaphors from an interdisciplinary angle, incorporating theory from the field of International Relations. An empirical evaluation of political (discourse) studies in chapter 4 shows the large absence of transdisciplinary perspectives. Addressing the abovementioned gaps, this dissertation reports on two empirical analyses of trade metaphors in a big corpus that represents the official trade positions of the United States and China during the presidencies of Bill Clinton and Jiang Zemin (1993-1997) as well as Donald Trump and Xi Jinping (2017-2021). Based on a codebook of a cross-linguistic metaphor identification procedure in chapter 5, the first empirical part contributes to the static and gradient perspective and includes two corpus-based studies of metaphorical framing about trade (chapters 6-7). The diachronic and cross-linguistic use of source domains from a socio-cognitive approach in chapter 6 reveals that source domains are semantic fields that vary with trade discourse contexts (interests, power, and power relations). Chapter 7 shows that the use of trade metaphors (source domains of Conventional and Novel metaphors) to construct and legitimize political ideologies correlates with differences between political genres. The second part contributes to the gradable and dynamic view by applying the transdisciplinary model of YinYang Dynamics of Metaphoricity in chapters 8-10. In chapter 8, an evaluation of the new model in the Clinton-Jiang trade discourse shows that the dynamic cognitive process (transformation of metaphoricity) and rhetorical process (argumentation and persuasion) mutually develop with the evolution of the socio-political process (trade perspectives and trade events). Chapter 9 investigates the transformation of metaphoricity in the Trump-Xi trade discourse and finds that cognitive processes (patterns of metaphoricity activation) and affective processes (emotions or sentiments) mutually develop with the evolution of socio-political processes (trade perspectives and trade events). Based on the findings in chapters 8-9, chapter 10 further shows several phenomena in the Clinton-Jiang and Trump-Xi trade discourses: the movement of metaphors on the metaphoricity spectrum, the bodily motivation of gradable and dynamic metaphoricity, and the interconnected political discourse systems. Drawing on all the theoretical and empirical insights revealed in the dissertation, the final section of the thesis outlines a future direction, i.e., moving towards a transdisciplinary and dynamic approach to metaphor in political discourse analysis
Machine Vision: How Algorithms are Changing the Way We See the World
Humans have used technology to expand our limited vision for millennia, from the invention of the stone mirror 8,000 years ago to the latest developments in facial recognition and augmented reality. We imagine that technologies will allow us to see more, to see differently and even to see everything. But each of these new ways of seeing carries its own blind spots. In this illuminating book, Jill Walker Rettberg examines the long history of machine vision. Providing an overview of the historical and contemporary uses of machine vision, she unpacks how technologies such as smart surveillance cameras and TikTok filters are changing the way we see the world and one another. By analysing fictional and real-world examples, including art, video games and science fiction, the book shows how machine vision can have very different cultural impacts, fostering both sympathy and community as well as anxiety and fear. Combining ethnographic and critical media studies approaches alongside personal reflections, Machine Vision is an engaging and eye-opening read. It is suitable for students and scholars of digital media studies, science and technology studies, visual studies, digital art and science fiction, as well as for general readers interested in the impact of new technologies on society.publishedVersio
Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences
Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin
A Survey on Semantic Processing Techniques
Semantic processing is a fundamental research domain in computational
linguistics. In the era of powerful pre-trained language models and large
language models, the advancement of research in this domain appears to be
decelerating. However, the study of semantics is multi-dimensional in
linguistics. The research depth and breadth of computational semantic
processing can be largely improved with new technologies. In this survey, we
analyzed five semantic processing tasks, e.g., word sense disambiguation,
anaphora resolution, named entity recognition, concept extraction, and
subjectivity detection. We study relevant theoretical research in these fields,
advanced methods, and downstream applications. We connect the surveyed tasks
with downstream applications because this may inspire future scholars to fuse
these low-level semantic processing tasks with high-level natural language
processing tasks. The review of theoretical research may also inspire new tasks
and technologies in the semantic processing domain. Finally, we compare the
different semantic processing techniques and summarize their technical trends,
application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN
1566-2535. The equal contribution mark is missed in the published version due
to the publication policies. Please contact Prof. Erik Cambria for detail
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