Relevant bibliographies by topics / Semantic map model

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  2. Dissertations / Theses
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Academic literature on the topic 'Semantic map model'

Author: Grafiati
Published: 21 February 2023

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Journal articles on the topic "Semantic map model"

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Zhang, Ze Shun. "Semantic Map Model and Construction of Synset." Advanced Materials Research 756-759 (September 2013): 2484–88. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2484.

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Language Information Processing has to achieve the goal of automated label of word meaning. To realizing this aim one of the chief tasks is construction of synonym synset. Functional synonym is faced with two big problems: scientific description and construction of synset. This article, takingmingming,xianranin Mandarin andobviously,clearlyin English as examples, introduces the methods of translation frequency and semantic map model to construct and describe functional synonym synset which shared by human and computer.
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도재학. "An Overview of the Semantic Map Model." CONCEPT AND COMMUNICATION ll, no. 24 (December 2019): 83–112. http://dx.doi.org/10.15797/concom.2019..24.003.

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Vitalis, Stelios, Ken Ohori, and Jantien Stoter. "Incorporating Topological Representation in 3D City Models." ISPRS International Journal of Geo-Information 8, no. 8 (August 1, 2019): 347. http://dx.doi.org/10.3390/ijgi8080347.

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3D city models are being extensively used in applications such as evacuation scenarios and energy consumption estimation. The main standard for 3D city models is the CityGML data model which can be encoded through the CityJSON data format. CityGML and CityJSON use polygonal modelling in order to represent geometries. True topological data structures have proven to be more computationally efficient for geometric analysis compared to polygonal modelling. In a previous study, we have introduced a method to topologically reconstruct CityGML models while maintaining the semantic information of the dataset, based solely on the combinatorial map (C-Map) data structure. As a result of the limitations of C-Map’s semantic representation mechanism, the resulting datasets could suffer either from semantic information loss or the redundant repetition of them. In this article, we propose a solution for a more efficient representation of geometry, topology and semantics by incorporating the C-Map data structure into the CityGML data model and implementing a CityJSON extension to encode the C-Map data. In addition, we provide an algorithm for the topological reconstruction of CityJSON datasets to append them according to this extension. Finally, we apply our methodology to three open datasets in order to validate our approach when applied to real-world data. Our results show that the proposed CityJSON extension can represent all geometric information of a city model in a lossless way, providing additional topological information for the objects of the model.
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Hua, Yan, Yingyun Yang, and Jianhe Du. "Deep Multi-Modal Metric Learning with Multi-Scale Correlation for Image-Text Retrieval." Electronics 9, no. 3 (March 10, 2020): 466. http://dx.doi.org/10.3390/electronics9030466.

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Multi-modal retrieval is a challenge due to heterogeneous gap and a complex semantic relationship between different modal data. Typical research map different modalities into a common subspace with a one-to-one correspondence or similarity/dissimilarity relationship of inter-modal data, in which the distances of heterogeneous data can be compared directly; thus, inter-modal retrieval can be achieved by the nearest neighboring search. However, most of them ignore intra-modal relations and complicated semantics between multi-modal data. In this paper, we propose a deep multi-modal metric learning method with multi-scale semantic correlation to deal with the retrieval tasks between image and text modalities. A deep model with two branches is designed to nonlinearly map raw heterogeneous data into comparable representations. In contrast to binary similarity, we formulate semantic relationship with multi-scale similarity to learn fine-grained multi-modal distances. Inter-modal and intra-modal correlations constructed on multi-scale semantic similarity are incorporated to train the deep model in an end-to-end way. Experiments validate the effectiveness of our proposed method on multi-modal retrieval tasks, and our method outperforms state-of-the-art methods on NUS-WIDE, MIR Flickr, and Wikipedia datasets.
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Park Jin-ho. "Semantic Description of Lexical and Grammatical Elements in Korean Using Semantic Map Model." Journal of Korean Linguistics ll, no. 63 (April 2012): 459–519. http://dx.doi.org/10.15811/jkl.2012..63.016.

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Yang, Guan, Ayou Han, Xiaoming Liu, Yang Liu, Tao Wei, and Zhiyuan Zhang. "Enhancing Semantic-Consistent Features and Transforming Discriminative Features for Generalized Zero-Shot Classifications." Applied Sciences 12, no. 24 (December 9, 2022): 12642. http://dx.doi.org/10.3390/app122412642.

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Generalized zero-shot learning (GZSL) aims to classify classes that do not appear during training. Recent state-of-the-art approaches rely on generative models, which use correlating semantic embeddings to synthesize unseen classes visual features; however, these approaches ignore the semantic and visual relevance, and visual features synthesized by generative models do not represent their semantics well. Although existing GZSL methods based on generative model disentanglement consider consistency between visual and semantic models, these methods consider semantic consistency only in the training phase and ignore semantic consistency in the feature synthesis and classification phases. The absence of such constraints may lead to an unrepresentative synthesized visual model with respect to semantics, and the visual and semantic features are not modally well aligned, thus causing the bias between visual and semantic features. Therefore, an approach for GZSL is proposed to enhance semantic-consistent features and discriminative features transformation (ESTD-GZSL). The proposed method can enhance semantic-consistent features at all stages of GZSL. A semantic decoder module is first added to the VAE to map synthetic and real features to the corresponding semantic embeddings. This regularization method allows synthesizing unseen classes for a more representative visual representation, and synthetic features can better represent their semantics. Then, the semantic-consistent features decomposed by the disentanglement module and the features output by the semantic decoder are transformed into enhanced semantic-consistent discriminative features and used in classification to reduce the ambiguity between categories. The experimental results show that our proposed method achieves more competitive results on four benchmark datasets (AWA2, CUB, FLO, and APY) of GZSL.
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Georgakopoulos, Thanasis, and Stéphane Polis. "Teaching & Learning Guide for: The semantic map model." Language and Linguistics Compass 12, no. 8 (August 2018): e12286. http://dx.doi.org/10.1111/lnc3.12286.

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Hayes, Taylor R., and John M. Henderson. "Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes." Psychological Science 32, no. 8 (July 12, 2021): 1262–70. http://dx.doi.org/10.1177/0956797621994768.

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The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real-world scenes but is poorly understood. Here, we examined the relationship between object semantics and attention by combining a vector-space model of semantics with eye movements in scenes. In this approach, the vector-space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers’ focus of attention; specifically, greater attention was given to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.
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Divers, John. "Philosophical Issues from Kripke’s ‘Semantical Considerations on Modal Logic’." Principia: an international journal of epistemology 20, no. 1 (September 22, 2016): 01. http://dx.doi.org/10.5007/1808-1711.2016v20n1p01.

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http://dx.doi.org/10.5007/1808-1711.2016v20n1p1In ‘Semantical Considerations on Modal Logic’, Kripke articulates his project in the discourse of “possible worlds”. There has been much philosophical discussion of whether endorsement of the Kripke semantics brings ontological commitment to possible worlds. However, that discussion is less than satisfactory because it has been conducted without the necessary investigation of the surrounding philosophical issues that are raised by the Kripke semantics. My aim in this paper is to map out the surrounding territory and to commence that investigation. Among the surrounding issues, and my attitudes to them, are these: (1) the potential of the standard distinction between pure and impure versions of the semantic theory has been under-exploited; (2) there has been under-estimation of what is achieved by the pure semantic theory alone; (3) there is a methodological imperative to co-ordinate a clear conception of the purposes of the impure theory with an equally clear conception of the content the theory; (4) there is a need to support by argument claims about how such a semantic theory, even in an impure state, can fund explanations in the theory of meaning and metaphysics; (5) greater attention needs to be paid to the crucial advance that Kripke makes on the precursors of possible-worlds semantics proper (e.g. Carnap 1947) in clearly distinguishing variation across the worlds within a model of modal space from variation across such models and, finally, (6) the normative nature of the concept of applicability, of the pure semantic theory, is both of crucial importance and largely ignored.
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Zhang, Guigang, Chao Li, Yong Zhang, and Chunxiao Xing. "A Semantic++ MapReduce Parallel Programming Model." International Journal of Semantic Computing 08, no. 03 (September 2014): 279–99. http://dx.doi.org/10.1142/s1793351x14400091.

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Big data is playing a more and more important role in every area such as medical health, internet finance, culture and education etc. How to process these big data efficiently is a huge challenge. MapReduce is a good parallel programming language to process big data. However, it has lots of shortcomings. For example, it cannot process complex computing. It cannot suit real-time computing. In order to overcome these shortcomings of MapReduce and its variants, in this paper, we propose a Semantic++ MapReduce parallel programming model. This study includes the following parts. (1) Semantic++ MapReduce parallel programming model. It includes physical framework of semantic++ MapReduce parallel programming model and logic framework of semantic++ MapReduce parallel programming model; (2) Semantic++ extraction and management method for big data; (3) Semantic++ MapReduce parallel programming computing framework. It includes semantic++ map, semantic++ reduce and semantic++ shuffle; (4) Semantic++ MapReduce for multi-data centers. It includes basic framework of semantic++ MapReduce for multi-data centers and semantic++ MapReduce application framework for multi-data centers; (5) A Case Study of semantic++ MapReduce across multi-data centers.
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Dissertations / Theses on the topic "Semantic map model"

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Kim, Yongtaek 1968. "Event construal and its linguistic encoding: Towards an Extended Semantic Map model." Thesis, University of Oregon, 2009. http://hdl.handle.net/1794/10329.

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xvi, 185 p. : ill. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number.
This dissertation investigates constructional alternation among the English verb- at , verb- away-at , and verb- away constructions. The primary purpose is to lay a fundamental conceptual framework on the interrelation between how we perceive a situation in an external world and how we construe it as an event structure in a conceptualized world to encode it linguistically. This study suggests an Extended Semantic Map (hereafter ESM) model. It presents an in-depth analysis of the three constructions, derived from the BNC (British National Corpus), and resultative constructions in Korean and Japanese. I argue that language has conceptual bases rooted in perception and cognitive construal. Construal allows one to view the same situation in a number of alternative ways. Construal is closely related to distribution of attention, which has two main patterns: focus of attention and windowing of attention. Focus of attention is mainly based on perceptual prominence. It is placed on participants and is typically encoded in the selection and arrangement of nominals. Windowing of attention operates on cognitive prominence. It is a cognitive process to segment some relation(s) out of an event structure. It is typically encoded in predicate or adverbial expressions. I further argue that any mismatch between perceptual and cognitive prominence requires overt marking. For example, the English passive construction requires the overt marking of ' be/get + past participle,' which directs an addressee's primary focus of attention to a perceptually secondary but cognitively primary patient. It also places windowing of attention on the perceptually secondary but cognitively primary Change. Windowing and focus of attention will be used to define the X- and Y-axes of the ESM. The X-axis consists of five causal relations -- Volition, Activity, Force Transfer, Change, and State, on which attention is windowed. The Y-axis is composed of four types of configuration for the semantic roles of the participants -- Agent, Agent-Location, Agent-Theme, and Theme. The ESM visually maps relations among constructions within and across languages. It illustrates how event structures can be categorized typically as either [Activity]-windowing or [Change]-windowing. Finally, it also allows us to represent cross-linguistic differences in the available constructions for construing event structures.
Committee in charge: Eric Pederson, Chairperson, Linguistics; Scott DeLancey, Member, Linguistics; Doris Payne, Member, Linguistics; Kaori Idemaru, Outside Member, East Asian Languages & Literatures
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MATTIOLA, Simone. "Typology of pluractional constructions in the languages of the World." Doctoral thesis, Università degli studi di Bergamo, 2017. http://hdl.handle.net/10446/77241.

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This thesis provides a first large-scale typological study on the phenomenon known as pluractionality in cross-linguistic perspective (sample of 241 languages). The aim of this work is to investigate how the languages of the World express pluractionality (i.e., plurality of situations marked on the verb) giving a complete account of the functional domain and morpho-syntax of this phenomenon adopting a functional-typological approach.
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Brenninkmeijer, Christian Y. A. "Querying sensor networks : requirements, semantics, algorithms and cost models." Thesis, University of Manchester, 2010. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:163395.

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Brunn, Ansgar. "Semantik-basierte Gebäudeerfassung mit verkoppelten Markoff-Zufallsfeldern /." Bonn : Rheinischen Friedrich-Wilhelms-Universität, Institut für Photogrammetrie, 2000. http://hss.ulb.uni-bonn.de:90/ulb_bonn/diss_online/landw_fak/2000/brunn_ansgar/0139.pdf.

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Wang, Jia-Wei, and 王嘉偉. "A Semantics Based Knowledge Map Extraction Model through Web Corpora-A Construction Industry Perspective." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/47611255196858430614.

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碩士
國立臺灣大學
土木工程學研究所
93
As the growing advancement of the construction technology, the management of the construction knowledge becomes the important issue to fellow involved in this industry. Traditionally, in the knowledge management theoretical view, the construction of the knowledge map is one quintessential technique to integrate and share knowledge existing in the organization. However, owing to the complexity nature of construction knowledge system, the fabrication of construction knowledge map is much easier said than done. Furthermore, a dynamic knowledge map rather than a static one displaying ontology fits the characteristic of knowledge essence that varies from minute to minute. Besides, the lack of methodology to build the map forms the major drawback of the application of knowledge map. Thanks to the astonishing development of internet technology, the internet is filled with all kinds of abundant information at moments. Hence, in order to establish dynamic knowledge map varying with time or events, we make use of the diversified information from web corpus and combine several information techniques such as information retrieval, Chinese keyword extraction and terms hierarchical clustering figured out in this research to find the semantics logic relations between lexicons and develop knowledge map extraction model to mine the ontology hide behind the internet corpus. We also build an internet knowledge map auto-extraction system according to the model proposed in this paper and develop two indexes, keyword-extraction validity and map validity, to measure the system performance with real case at last. In this paper, we develop a knowledge map extraction model that could be applied to integrate the enterprise knowledge management system platform, and provide a foundation of methodology to construct knowledge map to assist the development of knowledge map related research.
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Books on the topic "Semantic map model"

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Getting computers to talk like you and me: Discourse context, focus, and semantics : (an ATN model). Cambridge, Mass: MIT Press, 1985.

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Reichman, Rachel. Getting computers to talk like you and me: Discourse context, focus, and semantics (an ATN model). Cambridge, Mass: MIT Press, 1985.

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Goro, Takuya. Logical Connectives. Edited by Jeffrey L. Lidz, William Snyder, and Joe Pater. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199601264.013.23.

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This chapter reviews results from recent experimental studies on the acquisition of logical connectives. Developmental psychologists have long been interested in the development of logic in children, and recent research in this field has made great advancement by incorporating insights from theoretical linguistics. There are two important theoretical grounds that were crucial to such advancement. One is dissociation between pragmatic implicature and lexical semantics. The other is a model of semantic interaction between a logical connective and another logical word in the same sentence. Experimental results from recent studies that incorporated these insights strongly suggest that preschool children have sophisticated semantic knowledge of logical connectives, even though their behavior may sometimes deviate from adults’ behavior.
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Nuyts, Jan. Analyses of the Modal Meanings. Edited by Jan Nuyts and Johan Van Der Auwera. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199591435.013.1.

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This article deals with the semantic analysis of the notion of modality, surveying the most important traditional views in linguistics. After pointing out the problems encountered in the literature in trying to define the category, it first discusses the in the literature most common basic types of modality, namely, dynamic modality, deontic modality, and epistemic modality, as well as the less common basic category of boulomaic modality. It then goes on to survey a variety of alternative views on how the semantic domain of modality may be organized. The article also considers the types of criteria that have been proposed to motivate the “cover category” of modality. Finally, it outlines a few features and properties frequently referenced in the literature on modality as characteristic of (some of) the modal categories, including subjectivity vs objectivity or intersubjectivity, performativity vs descriptivity, informational status, and the semantic scope of qualificational dimensions.
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Yanovich, Igor. May under verbs of hoping: Evolution of the modal system in the complements of hoping verbs in Early Modern English. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198718208.003.0008.

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The chapter traces two stages of the rise of the may-under-hope construction of Late Modern English, present in examples like (i) Dearest, I hope we may be on such terms twenty years hence. Despite the archaic feel to it, this construction is in fact a very recent innovation that arose not earlier than the sixteenth century. I conjecture that its elevated flavor does not stem from its old age, but rather was inherited from another construction, with the inflectional subjunctive under hope. Along the way, I also present evidence that the textual absence of may under verbs of hoping before the rise of this construction was not due to narrow compositional semantics.
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Piccinini, Gualtiero. Computationalism. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0010.

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The introduction of the concept of computation in cognitive science is discussed in this article. Computationalism is usually introduced as an empirical hypothesis that can be disconfirmed. Processing information is surely an important aspect of cognition so if computation is information processing, then cognition involves computation. Computationalism becomes more significant when it has explanatory power. The most relevant and explanatory notion of computation is that associated with digital computers. Turing analyzed computation in terms of what are now called Turing machines that are the kind of simple processor operating on an unbounded tape. Turing stated that any function that can be computed by an algorithm could be computed by a Turing machine. McCulloch and Pitts's account of cognition contains three important aspects that include an analogy between neural processes and digital computations, the use of mathematically defined neural networks as models, and an appeal to neurophysiological evidence to support their neural network models. Computationalism involves three accounts of computation such as causal, semantic, and mechanistic. There are mappings between any physical system and at least some computational descriptions under the causal account. The semantic account may be formulated as a restricted causal account.
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Silk, Alex. Normative Language in Context. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198805076.003.0009.

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This chapter develops a contextualist account of normative language, focusing on broadly normative readings of modal verbs. The account draws on a more general framework for implementing a contextualist semantics and pragmatics, Discourse Contextualism. The aim of Discourse Contextualism is to derive the discourse properties of normative language from a contextualist interpretation of an independently motivated formal semantics, along with principles of interpretation and conversation. In using normative language, interlocutors can exploit their grammatical and world knowledge, and general pragmatic reasoning skills, to manage an evolving system of norms. Discourse Contextualism provides a perspicuous framework for further philosophical theorizing about the nature of normativity, normative language, and normative judgment. Delineating these issues can help refine our understanding of the space of overall theories and motivate more fruitful ways the dialectics may proceed. Discourse Contextualism provides a linguistic basis for a more comprehensive theory of normativity and normative discourse and practice.
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Malchukov, Andrej L., and Viktor S. Xrakovskij. The Linguistic Interaction of Mood with Modality and Other Categories. Edited by Jan Nuyts and Johan Van Der Auwera. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199591435.013.7.

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This chapter surveys some of the most important findings in the literature regarding the syntagmatic interactions between linguistic expressions of mood and of modality and some other categories, and regarding how these interactions may be explained in terms of the semantic properties of the categories involved. After a preliminary exemplification of the syntagmatic interaction of mood with other categories, showing how infelicitous combinations are either blocked or reinterpreted, the chapter deals, in subsequent sections, with the interaction with modal and modality related markers, with tense, with aspect, with negation, and with person. It concludes with a further discussion of the factors regulating the interaction of mood with other categories, viz., most importantly, functional (in)compatibility, markedness and economy.
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Gergel, Remus. Dimensions of variation in Old English modals. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198718208.003.0010.

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The chapter investigates aspects of variation in Old English modals with respect to functional status, modal base, modal force, and event realization under the modals in the actual world. The main goal is to view—in tandem, rather than in isolation—aspects of variation that are of interest at the syntax–semantics interface. The chapter focuses on three modals, the cognates of ‘can,’ ‘may,’ and ‘must,’ which show the full range of variation available in the categories investigated. The role of circumstantial modality in the diachronic development is underlined. It is furthermore argued that the modals of the period occupy a structural position in the aspectual domain. Evidence for functional status includes ellipsis, topicalization, and the complementary distribution with the aspectual prefix ge-.
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Mills, Caitlin, Arianne Herrera-Bennett, Myrthe Faber, and Kalina Christoff. Why the Mind Wanders. Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.42.

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This chapter offers a functional account of why the mind—when free from the demands of a task or the constraints of heightened emotions—tends to wander from one topic to another, in a ceaseless and seemingly random fashion. We propose the default variability hypothesis, which builds on William James’s phenomenological account of thought as a form of mental locomotion, as well as on recent advances in cognitive neuroscience and computational modeling. Specifically, the default variability hypothesis proposes that the default mode of mental content production yields the frequent arising of new mental states that have heightened variability of content over time. This heightened variability in the default mode of mental content production may be an adaptive mechanism that (1) enhances episodic memory efficiency through de-correlating individual episodic memories from one another via temporally spaced reactivations, and (2) facilitates semantic knowledge optimization by providing optimal conditions for interleaved learning.
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Book chapters on the topic "Semantic map model"

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Zhang, Ding. "Development of the Semantic Map Model in China." In The Palgrave Handbook of Chinese Language Studies, 133–54. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-0924-4_13.

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Zhang, Ding. "Development of the Semantic Map Model in China." In The Palgrave Handbook of Chinese Language Studies, 1–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-13-6844-8_13-1.

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Kumar, Cheruku Sandesh, Ratnadeep Roy, Sanyog Rawat, and Archek Praveen Kumar. "Activation Map Networks with Deep Graphical Model for Semantic Segmentation." In Intelligent Computing Techniques for Smart Energy Systems, 845–52. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0214-9_89.

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Schneider, Stefan, and Andreas Nürnberger. "Evaluating Semantic Co-creation by Using a Marker as a Linguistic Constraint Tool in Shared Cognitive Representation Models." In Language, Cognition, and Mind, 121–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69823-2_6.

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AbstractSemantic co-creation occurs in the process of communication between two or more people, where human cognitive representation models of the topic of discussion converge. The use of linguistic constraint tools (for example a shared marker) enable participants to focus on communication, improving communicative success. Recent results state that the best communicative success can be achieved if two users can interact in a restricted way, so called team focused interaction hypothesis. Even though the advantage of team focused interaction sounds plausible, it needs to be noted that previous studies enforce the constraint usage. Our study aims at investigating the advantage of using shared markers as a linguistic constraint tool in semantic co-creation, while moving them becomes optional. In our experimental task, based on a shared geographic map as a cognitive representation model, the two participants have to identify a target location, which is only known to a third participant. We assess two main factors, the teams’ use of a shared marker and the two complexity levels of the cognitive representation model. We had hypothesized that sharing a marker should improve communicative success, as communication is more focused. However, our results indicated no general benefit by using a marker as well as team interaction, itself. Our results suggest that the use of a shared marker is an efficient linguistic constraint at higher levels of complexity of the cognitive representation than those tested in our study. Based on this consideration, the team focused interaction hypothesis should be further developed to include a control parameter for the perceived decision complexity of the cognitive representation model.
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Johann, Patricia, Enrico Ghiorzi, and Daniel Jeffries. "Parametricity for Primitive Nested Types." In Lecture Notes in Computer Science, 324–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71995-1_17.

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AbstractThis paper considers parametricity and its resulting free theorems for nested data types. Rather than representing nested types via their Church encodings in a higher-kinded or dependently typed extension of System F, we adopt a functional programming perspective and design a Hindley-Milner-style calculus with primitives for constructing nested types directly as fixpoints. Our calculus can express all nested types appearing in the literature, including truly nested types. At the term level, it supports primitive pattern matching, map functions, and fold combinators for nested types. Our main contribution is the construction of a parametric model for our calculus. This is both delicate and challenging: to ensure the existence of semantic fixpoints interpreting nested types, and thus to establish a suitable Identity Extension Lemma for our calculus, our type system must explicitly track functoriality of types, and cocontinuity conditions on the functors interpreting them must be appropriately threaded throughout the model construction. We prove that our model satisfies an appropriate Abstraction Theorem and verifies all standard consequences of parametricity for primitive nested types.
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Morzycki, Marcin. "Structure and Ontology in Nonlocal Readings of Adjectives." In Language, Cognition, and Mind, 65–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-50200-3_4.

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AbstractIn certain uses, adjectives appear to make the semantic contribution normally associated with adverbs. These readings are often thought to be a peripheral phenomenon, restricted to one corner of the grammar and just a handful of lexical items. I’ll argue that it’s actually considerably more general than is often recognized, and that it admits two fundamentally different modes of explanation: in terms of the syntactic machinery that undergirds these structures and in terms of the ontology of the objects manipulated by its semantics. Both modes of explanation have been suggested for some of the puzzles in this domain, and I’ll argue both are necessary. With respect to adjectives including average and occasional, the key insight is that their lexical semantics is fundamentally about kinds. But to arrive at a more general theory of adverbial readings, it is also necessary to further articulate the compositional semantics. In this spirit, I’ll argue that these adjectives actually have the semantic type of quantificational determiners like every. If this way of thinking about adverbial readings is on the right track, it instantiates a means by which these two distinct modes of explanation—and the distinct aspects of cognition they may ultimately be associated with—both play a crucial role in bringing about the apparently aberrant behavior of this class of adjectives.
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Sanderson, Edward, and Bogdan J. Matuszewski. "FCN-Transformer Feature Fusion for Polyp Segmentation." In Medical Image Understanding and Analysis, 892–907. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12053-4_65.

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AbstractColonoscopy is widely recognised as the gold standard procedure for the early detection of colorectal cancer (CRC). Segmentation is valuable for two significant clinical applications, namely lesion detection and classification, providing means to improve accuracy and robustness. The manual segmentation of polyps in colonoscopy images is time-consuming. As a result, the use of deep learning (DL) for automation of polyp segmentation has become important. However, DL-based solutions can be vulnerable to overfitting and the resulting inability to generalise to images captured by different colonoscopes. Recent transformer-based architectures for semantic segmentation both achieve higher performance and generalise better than alternatives, however typically predict a segmentation map of $$\frac{h}{4}\times \frac{w}{4}$$ h 4 × w 4 spatial dimensions for a $$h\times w$$ h × w input image. To this end, we propose a new architecture for full-size segmentation which leverages the strengths of a transformer in extracting the most important features for segmentation in a primary branch, while compensating for its limitations in full-size prediction with a secondary fully convolutional branch. The resulting features from both branches are then fused for final prediction of a $$h\times w$$ h × w segmentation map. We demonstrate our method’s state-of-the-art performance with respect to the mDice, mIoU, mPrecision, and mRecall metrics, on both the Kvasir-SEG and CVC-ClinicDB dataset benchmarks. Additionally, we train the model on each of these datasets and evaluate on the other to demonstrate its superior generalisation performance.Code available: https://github.com/CVML-UCLan/FCBFormer.
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Péchoux, Romain, Simon Perdrix, Mathys Rennela, and Vladimir Zamdzhiev. "Quantum Programming with Inductive Datatypes: Causality and Affine Type Theory." In Lecture Notes in Computer Science, 562–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_29.

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AbstractInductive datatypes in programming languages allow users to define useful data structures such as natural numbers, lists, trees, and others. In this paper we show how inductive datatypes may be added to the quantum programming language QPL. We construct a sound categorical model for the language and by doing so we provide the first detailed semantic treatment of user-defined inductive datatypes in quantum programming. We also show our denotational interpretation is invariant with respect to big-step reduction, thereby establishing another novel result for quantum programming. Compared to classical programming, this property is considerably more difficult to prove and we demonstrate its usefulness by showing how it immediately implies computational adequacy at all types. To further cement our results, our semantics is entirely based on a physically natural model of von Neumann algebras, which are mathematical structures used by physicists to study quantum mechanics.
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Löbner, Sebastian. "Cascades. Goldman’s Level-Generation, Multilevel Categorization of Action, and Multilevel Verb Semantics." In Language, Cognition, and Mind, 263–307. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-50200-3_13.

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AbstractThe paper proposes a novel theory of the categorization of acts and applies it to the semantics of action verbs, with fundamental consequences for semantic theory and beyond. The theory is based on Goldman’s (Theory of human action. Princeton University Press, Princeton, NJ, 1970) multilevel theory of action which is taken here as a theory of categorization. Goldman’s central notion is level-generation: acts of a type may under circumstances generate acts of other, more abstract types. The acts form a hierarchical structure which Goldman calls an act-tree. Level-generation results in a conceptual relation called c-constitution here, i.e. constitution under the given circumstances; I also introduce the more general term cascade for act-trees. In the second part, multilevel cascade-structure categorization is combined with a cognitive semantics that models meanings with Barsalou frames. A multilevel analysis of the concept of writing is discussed in depth and detail in order to illustrate the potential and the consequences of a cascade approach to verb semantics. It is shown that the concept of c-constitution can be generalized as to cover the roles of persons and objects across levels in a cascade. The generalization suggests that multilevel categorization may be a very general and fundamental phenomenon in the psychology of categorization.
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Kakaletsis, Efstratios, Maria Tzelepi, Pantelis I. Kaplanoglou, Charalampos Symeonidis, Nikos Nikolaidis, Anastasios Tefas, and Ioannis Pitas. "Semantic Map Annotation Through UAV Video Analysis Using Deep Learning Models in ROS." In MultiMedia Modeling, 328–40. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05716-9_27.

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Conference papers on the topic "Semantic map model"

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Nguyen, Tam V., and Luoqi Liu. "Salient Object Detection with Semantic Priors." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/628.

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Salient object detection has increasingly become a popular topic in cognitive and computational sciences, including computer vision and artificial intelligence research. In this paper, we propose integrating semantic priors into the salient object detection process. Our algorithm consists of three basic steps. Firstly, the explicit saliency map is obtained based on the semantic segmentation refined by the explicit saliency priors learned from the data. Next, the implicit saliency map is computed based on a trained model which maps the implicit saliency priors embedded into regional features with the saliency values. Finally, the explicit semantic map and the implicit map are adaptively fused to form a pixel-accurate saliency map which uniformly covers the objects of interest. We further evaluate the proposed framework on two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental results demonstrate that our method outperforms other state-of-the-art methods.
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Zhang, Qingxiang, Meiling Wang, and Yufeng Yue. "Robust Semantic Map Matching Algorithm Based on Probabilistic Registration Model." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561176.

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Hu, Yuan, Yunpeng Chen, Xiang Li, and Jiashi Feng. "Dynamic Feature Fusion for Semantic Edge Detection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/110.

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Features from multiple scales can greatly benefit the semantic edge detection task if they are well fused. However, the prevalent semantic edge detection methods apply a fixed weight fusion strategy where images with different semantics are forced to share the same weights, resulting in universal fusion weights for all images and locations regardless of their different semantics or local context. In this work, we propose a novel dynamic feature fusion strategy that assigns different fusion weights for different input images and locations adaptively. This is achieved by a proposed weight learner to infer proper fusion weights over multi-level features for each location of the feature map, conditioned on the specific input. In this way, the heterogeneity in contributions made by different locations of feature maps and input images can be better considered and thus help produce more accurate and sharper edge predictions. We show that our model with the novel dynamic feature fusion is superior to fixed weight fusion and also the na\"ive location-invariant weight fusion methods, via comprehensive experiments on benchmarks Cityscapes and SBD. In particular, our method outperforms all existing well established methods and achieves new state-of-the-art.
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Chunxiu, Qin, Zhao Pengwei, and Dou Yongxiang. "Model of Semantic Community Based on Knowledge Map on Peer-to-Peer Networks." In 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE). IEEE, 2012. http://dx.doi.org/10.1109/icicee.2012.495.

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Akatkin, Yu, M. Bich, E. Yasinovskaya, and A. Shilin. "THE TECHNOLOGY AND TOOLS FOR THE BUILDING OF INFORMATION EXCHANGE PACKAGE BASED ON SEMANTIC DOMAIN MODEL." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.62.26.001.

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This paper presents the technology developed by the authors with the aim to improve the semantic interoperability in heterogeneous environment, where the systems use web-services orchestrated by an object-oriented exchange bus for cross-agency interaction and information sharing. The suggested solution, including the set of tools, allows to map the models of interacting information systems with aunified data model (domain otology) on the semantic level during the development of an information exchange package.
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Liu, Dongnan, Donghao Zhang, Yang Song, Chaoyi Zhang, Fan Zhang, Lauren O'Donnell, and Weidong Cai. "Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/121.

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Automated detection and segmentation of individual nuclei in histopathology images is important for cancer diagnosis and prognosis. Due to the high variability of nuclei appearances and numerous overlapping objects, this task still remains challenging. Deep learning based semantic and instance segmentation models have been proposed to address the challenges, but these methods tend to concentrate on either the global or local features and hence still suffer from information loss. In this work, we propose a panoptic segmentation model which incorporates an auxiliary semantic segmentation branch with the instance branch to integrate global and local features. Furthermore, we design a feature map fusion mechanism in the instance branch and a new mask generator to prevent information loss. Experimental results on three different histopathology datasets demonstrate that our method outperforms the state-of-the-art nuclei segmentation methods and popular semantic and instance segmentation models by a large margin.
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Rong-Hua Luo, Hua-Qing Min, Yong-Hui Xu, and Jun-Bo Li. "Coupled hidden semi-Markov conditional random fields based context model for semantic map building." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359025.

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Lehrer, Nancy Bonar, George Reynolds, and Joey Griffith. "A Method for Initial Hypothesis Formation in Image Understanding." In Machine Vision. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/mv.1987.fa3.

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One task in image understanding is to develop a mapping from some lowlevel abstraction to a higher level abstraction. One approach taken is to map from regions, lines, and/or surfaces to semantic labels. Achieving this task commonly involves developing a world model which defines semantic labels in terms of their lower level primitives. A matching scheme is then employed to calculate a match score between low level primitives and semantic labels, and some inference technique used to compare, contrast and combine match scores to create a consistent interpretation.
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Xu, Huapeng, Guilin Qi, Jingjing Li, Meng Wang, Kang Xu, and Huan Gao. "Fine-grained Image Classification by Visual-Semantic Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/145.

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This paper investigates a challenging problem,which is known as fine-grained image classification(FGIC). Different from conventional computer visionproblems, FGIC suffers from the large intraclassdiversities and subtle inter-class differences.Existing FGIC approaches are limited to exploreonly the visual information embedded in the images.In this paper, we present a novel approachwhich can use handy prior knowledge from eitherstructured knowledge bases or unstructured text tofacilitate FGIC. Specifically, we propose a visual-semanticembedding model which explores semanticembedding from knowledge bases and text, andfurther trains a novel end-to-end CNN frameworkto linearly map image features to a rich semanticembedding space. Experimental results on a challenginglarge-scale UCSD Bird-200-2011 datasetverify that our approach outperforms several state-of-the-art methods with significant advances.
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Chen, Zehui, Zhenyu Li, Shiquan Zhang, Liangji Fang, Qinhong Jiang, Feng Zhao, Bolei Zhou, and Hang Zhao. "AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/116.

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Object detection through either RGB images or the LiDAR point clouds has been extensively explored in autonomous driving. However, it remains challenging to make these two data sources complementary and beneficial to each other. In this paper, we propose AutoAlign, an automatic feature fusion strategy for 3D object detection. Instead of establishing deterministic correspondence with camera projection matrix, we model the mapping relationship between the image and point clouds with a learnable alignment map. This map enables our model to automate the alignment of non-homogenous features in a dynamic and data-driven manner. Specifically, a cross-attention feature alignment module is devised to adaptively aggregate pixel-level image features for each voxel. To enhance the semantic consistency during feature alignment, we also design a self-supervised cross-modal feature interaction module, through which the model can learn feature aggregation with instance-level feature guidance. Extensive experimental results show that our approach can lead to 2.3 mAP and 7.0 mAP improvements on the KITTI and nuScenes datasets respectively. Notably, our best model reaches 70.9 NDS on the nuScenes testing leaderboard, achieving competitive performance among various state-of-the-arts.
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Reports on the topic "Semantic map model"

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BIZIKOEVA, L. S., and G. S. KOKOEV. МЕТАФОРЫ ШЕКСПИРА КАК ПЕРЕВОДЧЕСКАЯ ПРОБЛЕМА (НА МАТЕРИАЛЕ ПЕРЕВОДА ТРАГЕДИИ "РОМЕО И ДЖУЛЬЕТТА" НА РУССКИЙ И ОСЕТИНСКИЙ ЯЗЫКИ). Science and Innovation Center Publishing House, 2020. http://dx.doi.org/10.12731/2077-1770-2020-3-3-95-106.

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Purpose. The goal of the present article is to analyze the original text of the tragedy “Romeo and Juliette” and its translations into the Russian and Ossetian languages to reveal Shakespeare’s metaphors for further analysis of the ways they are translated and possible problems translators might come across while translating. The main methods employed in the research are: the method of contextual analysis, the descriptive-analytical and the contrastive method. Results. The research was based on the theory of Shakespeare’s metaphor introduced by S.M. Mezenin. According to S.M. Mezenin the revealed metaphors were divided into several semantic groups the most numerous of which comprises metaphors with the semantic model “man - nature” that once again proved the idea of Caroline Spurgeon. The analysis of the translations into the Russian and Ossetian languages showed that translators do not always manage to preserve in the translated text unique Shakespeare’s metaphors. Practical implications. The received results can be used in teaching theory and practice of translation, cultural science, comparative lexicology of the Ossetian and Russian languages and the Ossetian and English languages.
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Whitecloud, Simone, Holly VerMeulen, Franz Lichtner, Nadia Podpora, Timothy Cooke, Christopher Williams, Michael Musty, Irene MacAllister, and Jason Dorvee. Understanding plant volatiles for environmental awareness : chemical composition in response to natural light cycles and wounding. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45961.

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Plants emit a bouquet of volatile organic compounds (VOCs) in response to both biotic and abiotic stresses and, simultaneously, eavesdrop on emit-ted signals to activate direct and indirect defenses. By gaining even a slight insight into the semantics of interplant communications, a unique aware-ness of the operational environment may be obtainable (e.g., knowledge of a disturbance within). In this effort, we used five species of plants, Arabidopsis thaliana, Panicum virgatum, Festuca rubra, Tradescantia zebrina, and Achillea millefolium, to produce and query VOCs emitted in response to mechanical wounding and light cycles. These plants provide a basis for further investigation in this communication system as they span model organisms, common house plants, and Arctic plants. The VOC com-position was complex; our parameter filtering often enabled us to reduce the noise to fewer than 50 compounds emitted over minutes to hours in a day. We were able to detect and measure the plant response through two analytical methods. This report documents the methods used, the data collected, and the analyses performed on the VOCs to determine if they can be used to increase environmental awareness of the battlespace.
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