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1
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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Ploux, Sabine, and Hyungsuk Ji. "A Model for Matching Semantic Maps between Languages (French/English, English/French)." Computational Linguistics 29, no. 2 (June 2003): 155–78. http://dx.doi.org/10.1162/089120103322145298.
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This article describes a spatial model for matching semantic values between two languages, French and English. Based on semantic similarity links, the model constructs a map that represents a word in the source language. Then the algorithm projects the map values onto a space in the target language. The new space abides by the semantic similarity links specific to the second language. Then the two maps are projected onto the same plane in order to detect overlapping values. For instructional purposes, the different steps are presented here using a few examples. The entire set of results is available at the following address: http://dico.isc.cnrs.fr .
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Veiga, Tiago S., Miguel Silva, Rodrigo Ventura, and Pedro U. Lima. "A Hierarchical Approach to Active Semantic Mapping Using Probabilistic Logic and Information Reward POMDPs." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 773–81. http://dx.doi.org/10.1609/icaps.v29i1.3546.
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Maintaining a semantic map of a complex and dynamic environment, where the uncertainty originates in both noisy perception and unexpected changes, is a challenging problem. In particular, we focus on the problem of maintaining a semantic map of an environment by a mobile agent. In this paper we address this problem in an hierarchical fashion. Firstly, we employ a probabilistic logic model representing the semantic map, as well as the associated uncertainty. Secondly, we model the interaction of the robot with the environment with a set of information-reward POMDP models, one for each partition of the environment (e.g., a room). The partition is performed in order to address the scalability limitations of POMDP models over very large state spaces. We then use probabilistic inference to determine which POMDP and policy to execute next. Experimental results show the efficiency of this architecture in real domestic service robotic scenarios.
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Fernández-Martínez, Nicolás José, and Pamela Faber. "Who stole what from whom?" Languages in Contrast 20, no. 1 (June 5, 2019): 107–40. http://dx.doi.org/10.1075/lic.19002.fer.
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Abstract Drawing on the Lexical Grammar Model, Frame Semantics and Corpus Pattern Analysis, we analyze and contrast verbs of stealing in English and Spanish from a lexico-semantic perspective. This involves looking at the lexical collocates and their corresponding semantic categories that fill the argument slots of verbs of stealing. Our corpus search is performed with the Word Sketch tool on Sketch Engine. To the best of our knowledge, no study has yet taken advantage of the Word Sketch tool in the study of the selection preferences of verbs of stealing, let alone a semantic, cross-linguistic study of those verbs. Our findings reveal that English and Spanish verbs of stealing map out the same underlying semantic space. This shared conceptual layer can thus be incorporated into an ontology based on deep semantics, which could in turn enhance NLP tasks such as word sense disambiguation, machine translation, semantic tagging, and semantic parsing.
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Kang, Xujie, Jing Li, Xiangtao Fan, Hongdeng Jian, and Chen Xu. "Object-Level Semantic Map Construction for Dynamic Scenes." Applied Sciences 11, no. 2 (January 11, 2021): 645. http://dx.doi.org/10.3390/app11020645.
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Visual simultaneous localization and mapping (SLAM) is challenging in dynamic environments as moving objects can impair camera pose tracking and mapping. This paper introduces a method for robust dense bject-level SLAM in dynamic environments that takes a live stream of RGB-D frame data as input, detects moving objects, and segments the scene into different objects while simultaneously tracking and reconstructing their 3D structures. This approach provides a new method of dynamic object detection, which integrates prior knowledge of the object model database constructed, object-oriented 3D tracking against the camera pose, and the association between the instance segmentation results on the current frame data and an object database to find dynamic objects in the current frame. By leveraging the 3D static model for frame-to-model alignment, as well as dynamic object culling, the camera motion estimation reduced the overall drift. According to the camera pose accuracy and instance segmentation results, an object-level semantic map representation was constructed for the world map. The experimental results obtained using the TUM RGB-D dataset, which compares the proposed method to the related state-of-the-art approaches, demonstrating that our method achieves similar performance in static scenes and improved accuracy and robustness in dynamic scenes.
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Kang, Xujie, Jing Li, Xiangtao Fan, Hongdeng Jian, and Chen Xu. "Object-Level Semantic Map Construction for Dynamic Scenes." Applied Sciences 11, no. 2 (January 11, 2021): 645. http://dx.doi.org/10.3390/app11020645.
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Visual simultaneous localization and mapping (SLAM) is challenging in dynamic environments as moving objects can impair camera pose tracking and mapping. This paper introduces a method for robust dense bject-level SLAM in dynamic environments that takes a live stream of RGB-D frame data as input, detects moving objects, and segments the scene into different objects while simultaneously tracking and reconstructing their 3D structures. This approach provides a new method of dynamic object detection, which integrates prior knowledge of the object model database constructed, object-oriented 3D tracking against the camera pose, and the association between the instance segmentation results on the current frame data and an object database to find dynamic objects in the current frame. By leveraging the 3D static model for frame-to-model alignment, as well as dynamic object culling, the camera motion estimation reduced the overall drift. According to the camera pose accuracy and instance segmentation results, an object-level semantic map representation was constructed for the world map. The experimental results obtained using the TUM RGB-D dataset, which compares the proposed method to the related state-of-the-art approaches, demonstrating that our method achieves similar performance in static scenes and improved accuracy and robustness in dynamic scenes.
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Xu, Jiakang, Wolfgang Mayer, Hongyu Zhang, Keqing He, and Zaiwen Feng. "Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base." Mathematics 10, no. 24 (December 15, 2022): 4778. http://dx.doi.org/10.3390/math10244778.
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A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known.
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Barðdal, Jóhanna, Thomas Smitherman, Valgerður Bjarnadóttir, Serena Danesi, Gard B. Jenset, and Barbara McGillivray. "Reconstructing constructional semantics." Theory and data in cognitive linguistics 36, no. 3 (November 30, 2012): 511–47. http://dx.doi.org/10.1075/sl.36.3.03bar.
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As the historical linguistic community is well aware, reconstructing semantics is a notoriously difficult undertaking. Such reconstruction has so far mostly been carried out on lexical items, like words and morphemes, and has not been conducted for larger and more complex linguistic units, which intuitively seems to be a more intricate task, especially given the lack of methodological criteria and guidelines within the field. This follows directly from the fact that most current theoretical frameworks are not construction-based, that is, they do not assume that constructions are form-meaning correspondences. In order to meet this challenge, we present an attempt at reconstructing constructional semantics, and more precisely the semantics of the Dative Subject Construction for an earlier stage of Indo-European. For this purpose we employ lexical semantic verb classes in combination with the semantic map model (Barðdal 2007, Barðdal, Kristoffersen & Sveen 2011), showing how incredibly stable semantic fields may remain across long time spans, and how reconstructing such semantic fields may be accomplished.
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Zhou, Lu. "Event Scene Method of Legal Domain Knowledge Map Based on Neural Network Hybrid Model." Applied Bionics and Biomechanics 2022 (June 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/5880595.
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Event extraction technology is one of the important researches in the field of information extraction, which helps people accurately retrieve, find, classify, and summarize effective information from a large amount of information streams. This paper uses the neural network hybrid model to identify the trigger words and event categories of the legal domain knowledge graph events, extracts the events of interest from a large amount of free text, and displays them in a structured format. First, the original text is preprocessed, and then, the distributed semantic word vector is combined with the dependent syntactic structure and location attributes to create a semantic representation in the form of a vector. The combined deep learning model is used to extract activated words, the long-term memory loop neural network uses temporal semantics to extract deep features, and the convergent neural network completes the extraction of activated words and event categories. Finally, the experimental results show that the accuracy of event extraction of the neural network hybrid model designed in this paper has reached 77.1%, and the recall rate has reached 76.8%, which is greatly improved compared with the traditional model.
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Lu, Tianyi, Yafei Liu, Yuan Yang, Huiqing Wang, and Xiaoguo Zhang. "A Monocular Visual Localization Algorithm for Large-Scale Indoor Environments through Matching a Prior Semantic Map." Electronics 11, no. 20 (October 20, 2022): 3396. http://dx.doi.org/10.3390/electronics11203396.
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It is challenging for a visual SLAM system to keep long-term precise and robust localization ability in a large-scale indoor environment since there is a low probability of the occurrence of loop closure. Aiming to solve this problem, we propose a monocular visual localization algorithm for large-scale indoor environments through matching a prior semantic map. In the approach, the line features of certain semantic objects observed by the monocular camera are extracted in real time. A cost function is proposed to represent the difference between the observed objects and the matched semantic objects in the preexisting semantic map. After that, a bundle adjustment model integrating the semantic object matching difference is given to optimize the pose of the camera and the real-time environment map. Finally, test cases are designed to evaluate the performance of our approach, in which the line features with semantic information are extracted in advance to build the semantic map for matching in real time. The test results show that the positioning accuracy of our method is improved in large-scale indoor navigation.
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Gao, Zhu, and Xiao Min Ji. "Study on the Semantic Model of Product Form." Key Engineering Materials 458 (December 2010): 8–13. http://dx.doi.org/10.4028/www.scientific.net/kem.458.8.
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In view of the product shape prototype's information model, based on the fuzzy D-S inference establishment product shape meaning matrix's design style description method, determinates the product conventional shape meaning. Then, the shape description object's meaning word is proposed based on the most superior fondness's meaning connection. Finally, the analysis is carried on based on the design style space and the shape whole appraisal distribution map to the product, established the shape meaning matrix to express as the product design style description model computer formalization.
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Vinasco-Alvarez, D., J. Samuel, S. Servigne, and G. Gesquière. "TOWARDS LIMITING SEMANTIC DATA LOSS IN 4D URBAN DATA SEMANTIC GRAPH GENERATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences VIII-4/W2-2021 (October 7, 2021): 37–44. http://dx.doi.org/10.5194/isprs-annals-viii-4-w2-2021-37-2021.
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Abstract. To enrich urban digital twins and better understand city evolution, the integration of heterogeneous, spatio-temporal data has become a large area of research in the enrichment of 3D and 4D (3D + Time) semantic city models. These models, which can represent the 3D geospatial data of a city and their evolving semantic relations, may require data-driven integration approaches to provide temporal and concurrent views of the urban landscape. However, data integration often requires the transformation or conversion of data into a single shared data format, which can be prone to semantic data loss. To combat this, this paper proposes a model-centric ontology-based data integration approach towards limiting semantic data loss in 4D semantic urban data transformations to semantic graph formats. By integrating the underlying conceptual models of urban data standards, a unified spatio-temporal data model can be created as a network of ontologies. Transformation tools can use this model to map datasets to interoperable semantic graph formats of 4D city models. This paper will firstly illustrate how this approach facilitates the integration of rich 3D geospatial, spatio-temporal urban data and semantic web standards with a focus on limiting semantic data loss. Secondly, this paper will demonstrate how semantic graphs based on these models can be implemented for spatial and temporal queries toward 4D semantic city model enrichment.
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Chen, Defang, Jian-Ping Mei, Yuan Zhang, Can Wang, Zhe Wang, Yan Feng, and Chun Chen. "Cross-Layer Distillation with Semantic Calibration." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7028–36. http://dx.doi.org/10.1609/aaai.v35i8.16865.
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Recently proposed knowledge distillation approaches based on feature-map transfer validate that intermediate layers of a teacher model can serve as effective targets for training a student model to obtain better generalization ability. Existing studies mainly focus on particular representation forms for knowledge transfer between manually specified pairs of teacher-student intermediate layers. However, semantics of intermediate layers may vary in different networks and manual association of layers might lead to negative regularization caused by semantic mismatch between certain teacher-student layer pairs. To address this problem, we propose Semantic Calibration for Cross-layer Knowledge Distillation (SemCKD), which automatically assigns proper target layers of the teacher model for each student layer with an attention mechanism. With a learned attention distribution, each student layer distills knowledge contained in multiple layers rather than a single fixed intermediate layer from the teacher model for appropriate cross-layer supervision in training. Consistent improvements over state-of-the-art approaches are observed in extensive experiments with various network architectures for teacher and student models, demonstrating the effectiveness and flexibility of the proposed attention based soft layer association mechanism for cross-layer distillation.
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Becker, Laura, and Andrej Malchukov. "Semantic maps and typological hierarchies: Evidence for the Actionality Hierarchy." Zeitschrift für Sprachwissenschaft 41, no. 1 (June 1, 2022): 31–66. http://dx.doi.org/10.1515/zfs-2021-2044.
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Abstract Although semantic maps and typological hierarchies are different analytical tools and make different predictions, there is, arguably, a particular kind of a semantic map that shares certain features with typological hierarchies, in particular, the property of directionality. First, we briefly illustrate that such maps are based on the notion of local markedness and are designed to model the interaction between grammatical categories. We then explore the Actionality Hierarchy, formulated in terms of Vendlerian classes, which models the interaction between actionality and grammatical aspect. On the one hand, it will be shown that the Actionality Hierarchy can be reconstructed as a semantic map, based on common semantic features shared selectively between individual Vendlerian classes. Notably, it is directional and can be used to capture relations of local markedness between actionality and aspect. On the other hand, we will provide first systematic, quantitative evidence for the Actionality Map, using cross-linguistic parallel corpus data of four languages.
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Wang, Xin Yu, and Qing Song Zhang. "The Design and Research of Digital Service Platform Based on Semantic Web." Advanced Materials Research 760-762 (September 2013): 1808–11. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1808.
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With the application of semantic web technique and concept map theory, this paper constructs a personalized platform model based on semantic web, which is called digital library. The model consists of personalized module, information resource integration and process module, semantic analysis and process module, and query module. The function of each module is analyzed concretely.
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Han, Xu, Deyun Chen, and Hailu Yang. "A Semantic Community Detection Algorithm Based on Quantizing Progress." Complexity 2019 (January 9, 2019): 1–13. http://dx.doi.org/10.1155/2019/3475458.
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The semantic social network is a kind of network that contains enormous nodes and complex semantic information, and the traditional community detection algorithms could not give the ideal cogent communities instead. To solve the issue of detecting semantic social network, we present a clustering community detection algorithm based on the PSO-LDA model. As the semantic model is LDA model, we use the Gibbs sampling method that can make quantitative parameters map from semantic information to semantic space. Then, we present a PSO strategy with the semantic relation to solve the overlapping community detection. Finally, we establish semantic modularity (SimQ) for evaluating the detected semantic communities. The validity and feasibility of the PSO-LDA model and the semantic modularity are verified by experimental analysis.
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Merkx, Danny, and Stefan L. Frank. "Learning semantic sentence representations from visually grounded language without lexical knowledge." Natural Language Engineering 25, no. 4 (July 2019): 451–66. http://dx.doi.org/10.1017/s1351324919000196.
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AbstractCurrent approaches to learning semantic representations of sentences often use prior word-level knowledge. The current study aims to leverage visual information in order to capture sentence level semantics without the need for word embeddings. We use a multimodal sentence encoder trained on a corpus of images with matching text captions to produce visually grounded sentence embeddings. Deep Neural Networks are trained to map the two modalities to a common embedding space such that for an image the corresponding caption can be retrieved and vice versa. We show that our model achieves results comparable to the current state of the art on two popular image-caption retrieval benchmark datasets: Microsoft Common Objects in Context (MSCOCO) and Flickr8k. We evaluate the semantic content of the resulting sentence embeddings using the data from the Semantic Textual Similarity (STS) benchmark task and show that the multimodal embeddings correlate well with human semantic similarity judgements. The system achieves state-of-the-art results on several of these benchmarks, which shows that a system trained solely on multimodal data, without assuming any word representations, is able to capture sentence level semantics. Importantly, this result shows that we do not need prior knowledge of lexical level semantics in order to model sentence level semantics. These findings demonstrate the importance of visual information in semantics.
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Zhang, Nan, Wenqiang Zhang, and Yingnan Shang. "Research on Dynamic Knowledge Map Service System Using Computer Big Data." Journal of Physics: Conference Series 2083, no. 4 (November 1, 2021): 042001. http://dx.doi.org/10.1088/1742-6596/2083/4/042001.
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Abstract The emergence of computer big data related data provides a new method for the construction of knowledge links in the knowledge map. This realizes an objective knowledge network with practical significance that is easier to be understood by machines. The article combines the four principles of linked data publishing content objects and their semantic characteristics, and uses the RDF data model to convert unstructured data on the Internet and structured data that adopts different standards into unified standard structured data for association. The system forms a huge knowledge map with semantics, intelligence, and dynamics.
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Joo, Sunghyeon, Sanghyeon Bae, Junhyeon Choi, Hyunjin Park, Sangwook Lee, Sujeong You, Taeyoung Uhm, Jiyoun Moon, and Taeyong Kuc. "A Flexible Semantic Ontological Model Framework and Its Application to Robotic Navigation in Large Dynamic Environments." Electronics 11, no. 15 (August 3, 2022): 2420. http://dx.doi.org/10.3390/electronics11152420.
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Advanced research in robotics has allowed robots to navigate diverse environments autonomously. However, conducting complex tasks while handling unpredictable circumstances is still challenging for robots. The robots should plan the task by understanding the working environments beyond metric information and need countermeasures against various situations. In this paper, we propose a semantic navigation framework based on a Triplet Ontological Semantic Model (TOSM) to manage various conditions affecting the execution of tasks. The framework allows robots with different kinematics to perform tasks in indoor and outdoor environments. We define the TOSM-based semantic knowledge and generate a semantic map for the domains. The robots execute tasks according to their characteristics by converting inferred knowledge to Planning Domain Definition Language (PDDL). Additionally, to make the framework sustainable, we determine a policy of maintaining the map and re-planning when in unexpected situations. The various experiments on four different kinds of robots and four scenarios validate the scalability and reliability of the proposed framework.
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Nan, Chengyu. "Semantic Map and HBV in English, Chinese and Korean—A Case Study of hand,手and손." Journal of Language Teaching and Research 7, no. 6 (November 1, 2016): 1216. http://dx.doi.org/10.17507/jltr.0706.21.
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Semantic map is often used for semantic analysis in the research of grammatical forms and structures than lexical forms and meanings in linguistic typology. This paper, by means of Semantic Map Model, conducts the typological analysis of the lexical meanings of [+HAND] in English, Chinese and Korean, which typologically belong to three different types of languages, that is, English is inflectional, Chinese is isolating and Korean is agglutinative. From the conceptual space and the semantic map of hand, 手 and 손, we can find that their meanings are extended on the basis of their basic meanings of [+part of body], [+holding things] and [+doing things] from holding something with hands to controlling something or somebody with power, from a person who does something with hands to a person in general, from actions which are done with hands to actions in general, from skills done with hands to methods in general. The semantic map of [+HAND] also conveys the relationship and distance among the lexical meanings, and concludes and predicts the dynamic evolution of the lexical meanings.
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Широкова, Елена, Игорь Вострокнутов, Александр Луканкин, and Ирина Слободская. "НЕЧЕТКАЯ КОГНИТИВНАЯ МОДЕЛЬ СИСТЕМЫ ПРОЕКТНОЙ ДЕЯТЕЛЬНОСТИ ВУЗА." Education and Technologies Journal 13, no. 1 (August 1, 2022): 177–83. http://dx.doi.org/10.26883/2010.221.4206.
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The article summarizes the preliminary results of modeling a complex system of student project activity using a fuzzy cognitive map. For effective management of any process, to achieve a certain effect in the future, mathematical models of various processes and phenomena are needed. But where accurate quantitative measurements are impossible, where only qualitative expert assessments are applicable, management proceeds under conditions of uncertainty. The fuzzy cognitive model (map) allows us to consider heterogeneous semantic structures in the process of mutual influence: the value-semantic sphere of the personality of the future teacher; project activity through subjective experience and social and material results; the infrastructure of the educational space of the university formed in the process of project activity. As a result of computer research, the conditions of applicability of the proposed model are clarified, the dynamics of changes in a complex system is analyzed; steps to refine numerical data and modeling prospects are determined.
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Wysocki, O., B. Schwab, L. Hoegner, T. H. Kolbe, and U. Stilla. "PLASTIC SURGERY FOR 3D CITY MODELS: A PIPELINE FOR AUTOMATIC GEOMETRY REFINEMENT AND SEMANTIC ENRICHMENT." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2021 (June 17, 2021): 17–24. http://dx.doi.org/10.5194/isprs-annals-v-4-2021-17-2021.
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Abstract. Nowadays, the number of connected devices providing unstructured data is rapidly rising. These devices acquire data with a temporal and spatial resolution at an unprecedented level creating an influx of geoinformation which, however, lacks semantic information. Simultaneously, structured datasets like semantic 3D city models are widely available and assure rich semantics and high global accuracy but are represented by rather coarse geometries. While the mentioned downsides curb the usability of these data types for nowadays’ applications, the fusion of both shall maximize their potential. Since testing and developing automated driving functions stands at the forefront of the challenges, we propose a pipeline fusing structured (CityGML and HD Map datasets) and unstructured datasets (MLS point clouds) to maximize their advantages in the automatic 3D road space models reconstruction domain. The pipeline is a parameterized end-to-end solution that integrates segmentation, reconstruction, and modeling tasks while ensuring geometric and semantic validity of models. Firstly, the segmentation of point clouds is supported by the transfer of semantics from a structured to an unstructured dataset. The distinction between horizontal- and vertical-like point cloud subsets enforces a further segmentation or an immediate refinement while only adequately depicted models by point clouds are allowed. Then, based on the classified and filtered point clouds the input 3D model geometries are refined. Building upon the refinement, the semantic enrichment of the 3D models is presented. The deployment of a simulation engine for automated driving research and a city model database tool underlines the versatility of possible application areas.
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Tenser, Anton. "Semantic Map Borrowing – Case Representation in Northeastern Romani Dialects." Journal of Language Contact 9, no. 2 (April 29, 2016): 211–45. http://dx.doi.org/10.1163/19552629-00902001.
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Recent studies in contact linguistics have emphasized the aspect of language-internal grammaticalization that is triggered by accommodation to an external (contact-language) model (e.g. Heine and Kuteva, 2005). This is based on the notion that speakers make use of the available resources in order to match them to those of the target language. A problematic issue is contact-induced change in the domain of case representation. Synthetic case markers are usually thought of as fully grammaticalized morphemes. If contact-induced grammaticalization is, as Heine and Kuteva suggest, much like monolingual grammaticalization, unidirectional, how do we treat instances of rearrangement of the semantic meaning and scope of case markers? I will discuss this problem by examining a sample of Romani dialects, belonging to the so-called Northeastern dialect group (see Matras, 2002). Relying on specific constructions, like Subject of Negative Existence, External Possession, Privative, Partitive etc., I will compare and contrast the Northeastern dialects with their respective contact languages (Russian and Polish). Using semantic maps, I will demonstrate how the Romani dialects in question restructure their case representation system to accommodate to the systems of the model languages, and will discuss what it is exactly that gets equated when two languages come into contact.
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Vanhove, Martine. "A diachronic semantic map of the Optative negative in Beja (North-Cushitic)." Zeitschrift für Sprachwissenschaft 41, no. 1 (June 1, 2022): 263–77. http://dx.doi.org/10.1515/zfs-2021-2047.
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Abstract The Optative negative of Beja is a multifunctional paradigm which encodes optative, hortative and jussive grammatical meanings, depending on the person, as well as participant-internal and participant-external modalities of impossibility and unnecessity. It is also the sole paradigm licensed in balanced embedded clauses. Based on a large corpus of naturalistic first-hand data, this study analyses the various uses of the paradigm, provides an account of its evolution from the pre-modal stage to the post-modal stage on the basis of language internal morpho-syntactic cues, and proposes a diachronic semantic map, based on van der Auwera and Plungian (1998) model. It shows that semantic maps are not only useful for typological purposes, but also for language internal studies, helping understand the semantic shifts that occurred in the grammar of unwritten languages with no recorded history.
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Zhang, Chengyuan, Jiayu Song, Xiaofeng Zhu, Lei Zhu, and Shichao Zhang. "HCMSL: Hybrid Cross-modal Similarity Learning for Cross-modal Retrieval." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (April 20, 2021): 1–22. http://dx.doi.org/10.1145/3412847.
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The purpose of cross-modal retrieval is to find the relationship between different modal samples and to retrieve other modal samples with similar semantics by using a certain modal sample. As the data of different modalities presents heterogeneous low-level feature and semantic-related high-level features, the main problem of cross-modal retrieval is how to measure the similarity between different modalities. In this article, we present a novel cross-modal retrieval method, named Hybrid Cross-Modal Similarity Learning model (HCMSL for short). It aims to capture sufficient semantic information from both labeled and unlabeled cross-modal pairs and intra-modal pairs with same classification label. Specifically, a coupled deep fully connected networks are used to map cross-modal feature representations into a common subspace. Weight-sharing strategy is utilized between two branches of networks to diminish cross-modal heterogeneity. Furthermore, two Siamese CNN models are employed to learn intra-modal similarity from samples of same modality. Comprehensive experiments on real datasets clearly demonstrate that our proposed technique achieves substantial improvements over the state-of-the-art cross-modal retrieval techniques.
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Miao, Lizhi, Chengliang Liu, Li Fan, and Mei-Po Kwan. "An OGC web service geospatial data semantic similarity model for improving geospatial service discovery." Open Geosciences 13, no. 1 (January 1, 2021): 245–61. http://dx.doi.org/10.1515/geo-2020-0232.
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Abstract Open Geospatial Consortium (OGC) Web Services (OWS) are highly significant for geospatial data sharing and widely used in many scientific fields. However, those services are hard to find and utilize effectively. Focusing on addressing the big challenge of OWS resource discovery, we propose a measurement model that integrates spatiotemporal similarity and thematic similarity based on ontology semantics to generate a more efficient search method: OWS Geospatial Data Semantic Similarity Model (OGDSSM)-based search engine for semantically enabled geospatial data service discovery that takes into account the hierarchy difference of geospatial service documents and the number of map layers. We implemented the proposed OGDSSM-based semantic search algorithm on United States Geological Survey mineral resources geospatial service discovery. The results show that the proposed search method has better performance than the existing search engines that are based on keyword-based matching, such as Lucene, when recall, precision, and F-measure are taken into consideration. Furthermore, the returned results are ranked based on semantic similarity, which makes it easier for users to find the most similar geospatial data services. Our proposed method can thus enhance the performance of geospatial data service discovery for a wide range of geoscience applications.
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Wu, Dongyan, Bingbo Xie, and Chongben Tao. "3D Semantic VSLAM of Dynamic Environment Based on YOLACT." Mathematical Problems in Engineering 2022 (September 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/7307783.
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A Visual Simultaneous Localization and Mapping (VSLAM) method is proposed to construct a 3D environment map in the creation of dynamic noise information which leads to significant errors in camera pose estimation and a substantial number of noise points. For this problem, this paper proposed a method based on YOLACT, which combined optical flow and ViBe+ semantic map construction algorithm. First, our approach uses LK optical flow method to estimate the overall motion trajectory of adjacent frames. Then the trajectory data are employed to intercept the relative position of the current frame to the previous frame. Afterward, we combine ViBe+ algorithm to accurately detect and eliminate dynamic noise. Secondly, image semantic segmentation is performed based on YOLACT model. Image feature points are extracted from MAPLAB algorithm, pose estimation of the camera is performed and movement trajectory is recorded to complete a semantic map. Finally, through ablations study with common algorithms, the experimental results show that the proposed algorithm effectively avoids interference of dynamic noise information on VSLAM. Additionally, the constructed semantic map provides higher precision, fewer noise points, and pretty robustness.
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Uygur, Irem, Renato Miyagusuku, Sarthak Pathak, Alessandro Moro, Atsushi Yamashita, and Hajime Asama. "Robust and Efficient Indoor Localization Using Sparse Semantic Information from a Spherical Camera." Sensors 20, no. 15 (July 24, 2020): 4128. http://dx.doi.org/10.3390/s20154128.
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Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. “Sparse semantic” refers to the detection of sparsely distributed objects such as doors and windows. We use sparse semantic information to self-localize on a human-readable 2D annotated map in the sensor model. Thus, compared to previous works using point clouds or other dense and large data structures, our work uses a small amount of sparse semantic information, which efficiently reduces uncertainty in real-time localization. Unlike complex 3D constructions, the annotated map required by our method can be easily prepared by marking the approximate centers of the annotated objects on a 2D map. Our approach is robust to the partial obstruction of views and geometrical errors on the map. The localization is performed using low-cost lightweight sensors, an inertial measurement unit and a spherical camera. We conducted experiments to show the feasibility and robustness of our approach.
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Zhan, Wenqiang, Changshi Xiao, Yuanqiao Wen, Chunhui Zhou, Haiwen Yuan, Supu Xiu, Xiong Zou, Cheng Xie, and Qiliang Li. "Adaptive Semantic Segmentation for Unmanned Surface Vehicle Navigation." Electronics 9, no. 2 (January 24, 2020): 213. http://dx.doi.org/10.3390/electronics9020213.
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The intelligentization of unmanned surface vehicles (USVs) has recently attracted intensive interest. Visual perception of the water scenes is critical for the autonomous navigation of USVs. In this paper, an adaptive semantic segmentation method is proposed to recognize the water scenes. A semantic segmentation network model is designed to classify each pixel of an image into water, land or sky. The segmentation result is refined by the conditional random field (CRF) method. It is further improved accordingly by referring to the superpixel map. A weight map is generated based on the prediction confidence. The network trains itself with the refined pseudo label and the weight map. A set of experiments were designed to evaluate the proposed method. The experimental results show that the proposed method exhibits excellent performance with few-shot learning and is quite adaptable to a new environment, very efficient for limited manual labeled data utilization.
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Li, B. S., B. Liu, X. S. Ni, P. Huang, and L. L. Pu. "RESEARCH ON SEMANTIC MAP GENERATION AND LOCATION INTELLIGENT RECOGNITION METHOD FOR SCENIC SPOT SPACE PERCEPTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 431–35. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-431-2020.
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Abstract. In recent years, Tourism has become more and more Chinese leisure travel choice The research on the smart scenic spot is getting deeper and deeper, but the problem of accurate location l in the natural scenic spot still needs to be solved. Semantic maps contain a wealth of environmental information and can be more efficient for location-aware services, and are attracting more and more attention from researchers at home and abroad. In order to better ensure the travel experience of tourists, the range of scenic spots is too large, and the signal interference is high. Complex terrain in the scenic area, Branch and leaf features Visitors cannot rely on traditional positioning systems to get their current accurate location. It is proposed to construct a navigation semantic map for the perception of scenic space. In the construction process, the operation based on the location perception of the tourists and the surrounding environment and the extraction of the feature information is the key to constructing the semantic map. The general image recognition method is used to obtain the environment image information, and the acquired feature image is recognized to obtain the semantic information in the environment; in order to obtain more feature environment information to better complete the location-aware service task, the GBP descriptor is used. The method divides and stores different semantic regions in the environment, and generates a semantic map with rich semantic information and feature information according to the three-dimensional map model.
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Liu, Liqi, Qinglin Wang, and Yuan Li. "Improved Chinese Sentence Semantic Similarity Calculation Method Based on Multi-Feature Fusion." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 4 (July 20, 2021): 442–49. http://dx.doi.org/10.20965/jaciii.2021.p0442.
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In this paper, an improved long short-term memory (LSTM)-based deep neural network structure is proposed for learning variable-length Chinese sentence semantic similarities. Siamese LSTM, a sequence-insensitive deep neural network model, has a limited ability to capture the semantics of natural language because it has difficulty explaining semantic differences based on the differences in syntactic structures or word order in a sentence. Therefore, the proposed model integrates the syntactic component features of the words in the sentence into a word vector representation layer to express the syntactic structure information of the sentence and the interdependence between words. Moreover, a relative position embedding layer is introduced into the model, and the relative position of the words in the sentence is mapped to a high-dimensional space to capture the local position information of the words. With this model, a parallel structure is used to map two sentences into the same high-dimensional space to obtain a fixed-length sentence vector representation. After aggregation, the sentence similarity is computed in the output layer. Experiments with Chinese sentences show that the model can achieve good results in the calculation of the semantic similarity.
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Huang, Xinping, Siyuan Zhu, and Yue Ren. "A Semantic Matching Method of E-Government Information Resources Knowledge Fusion Service Driven by User Decisions." Journal of Organizational and End User Computing 35, no. 1 (January 20, 2023): 1–17. http://dx.doi.org/10.4018/joeuc.317082.
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This study focuses on the knowledge fusion model of e-government information resources that supports user decision-making information needs, it discusses the user decision-making information needs model, the knowledge fusion service model, and the relationship between them. The inter-layer mapping matching mechanism realizes the ultimate value of knowledge fusion. Therefore, this paper analyses and studies the mapping mechanism between the user information demand model and the knowledge fusion service model. A semantic, similarity-based knowledge fusion service matching method for e-government information resources is proposed to address the problem of lack of semantics in traditional web service matching methods. This method uses the ontology description language OWL-S to map information requirement documents of user decisions and knowledge fusion service function documents into an ontology tree structure. The authors then use this as the basis to calculate the concept similarity and relationship similarity measures, and the service matching based on semantic similarity can be realized.
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Wei, Xinchun, Xing Li, Wei Liu, Lianpeng Zhang, Dayu Cheng, Hanyu Ji, Wenzheng Zhang, and Kai Yuan. "Building Outline Extraction Directly Using the U2-Net Semantic Segmentation Model from High-Resolution Aerial Images and a Comparison Study." Remote Sensing 13, no. 16 (August 12, 2021): 3187. http://dx.doi.org/10.3390/rs13163187.
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Deep learning techniques have greatly improved the efficiency and accuracy of building extraction using remote sensing images. However, high-quality building outline extraction results that can be applied to the field of surveying and mapping remain a significant challenge. In practice, most building extraction tasks are manually executed. Therefore, an automated procedure of a building outline with a precise position is required. In this study, we directly used the U2-net semantic segmentation model to extract the building outline. The extraction results showed that the U2-net model can provide the building outline with better accuracy and a more precise position than other models based on comparisons with semantic segmentation models (Segnet, U-Net, and FCN) and edge detection models (RCF, HED, and DexiNed) applied for two datasets (Nanjing and Wuhan University (WHU)). We also modified the binary cross-entropy loss function in the U2-net model into a multiclass cross-entropy loss function to directly generate the binary map with the building outline and background. We achieved a further refined outline of the building, thus showing that with the modified U2-net model, it is not necessary to use non-maximum suppression as a post-processing step, as in the other edge detection models, to refine the edge map. Moreover, the modified model is less affected by the sample imbalance problem. Finally, we created an image-to-image program to further validate the modified U2-net semantic segmentation model for building outline extraction.
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Homburg, Timo. "Connecting Semantic Situation Descriptions with Data Quality Evaluations—Towards a Framework of Automatic Thematic Map Evaluation." Information 11, no. 11 (November 15, 2020): 532. http://dx.doi.org/10.3390/info11110532.
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A continuing question in the geospatial community is the evaluation of fitness for use of map data for a variety of use cases. While data quality metrics and dimensions have been discussed broadly in the geospatial community and have been modelled in semantic web vocabularies, an ontological connection between use cases and data quality expressions allowing reasoning approaches to determine the fitness for use of semantic web map data has not yet been approached. This publication introduces such an ontological model to represent and link situations with geospatial data quality metrics to evaluate thematic map contents. The ontology model constitutes the data storage element of a framework for use case based data quality assurance, which creates suggestions for data quality evaluations which are verified and improved upon by end-users. So-created requirement profiles are associated and shared to semantic web concepts and therefore contribute to a pool of linked data describing situation-based data quality assessments, which may be used by a variety of applications. The framework is tested using two test scenarios which are evaluated and discussed in a wider context.
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Park, Min-Hong, Jae-Hoon Cho, and Yong-Tae Kim. "CNN Model with Multilayer ASPP and Two-Step Cross-Stage for Semantic Segmentation." Machines 11, no. 2 (January 17, 2023): 126. http://dx.doi.org/10.3390/machines11020126.
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Currently, interest in deep learning-based semantic segmentation is increasing in various fields such as the medical field, automatic operation, and object division. For example, UNet, a deep learning network with an encoder–decoder structure, is used for image segmentation in the biomedical area, and an attempt to segment various objects is made using ASPP such as Deeplab. A recent study improves the accuracy of object segmentation through structures that extend in various receptive fields. Semantic segmentation has evolved to divide objects of various sizes more accurately and in detail, and various methods have been presented for this. In this paper, we propose a model structure that reduces the overall parameters of the deep learning model in this development and improves accuracy. The proposed model is an encoder–decoder structure, and an encoder half scale provides a feature map with few encoder parameters. A decoder integrates feature maps of various scales with high area details and forward features of low areas. An integrated feature map learns a feature map of each encoder hierarchy over an area of previous data in the form of a continuous coupling structure. To verify the performance of the model, we learned and compared the KITTI-360 dataset with the Cityscapes dataset, and experimentally confirmed that the proposed method was superior to the existing model.
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Richter, Sven, Yiqun Wang, Johannes Beck, Sascha Wirges, and Christoph Stiller. "Semantic Evidential Grid Mapping Using Monocular and Stereo Cameras." Sensors 21, no. 10 (May 12, 2021): 3380. http://dx.doi.org/10.3390/s21103380.
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Accurately estimating the current state of local traffic scenes is one of the key problems in the development of software components for automated vehicles. In addition to details on free space and drivability, static and dynamic traffic participants and information on the semantics may also be included in the desired representation. Multi-layer grid maps allow the inclusion of all of this information in a common representation. However, most existing grid mapping approaches only process range sensor measurements such as Lidar and Radar and solely model occupancy without semantic states. In order to add sensor redundancy and diversity, it is desired to add vision-based sensor setups in a common grid map representation. In this work, we present a semantic evidential grid mapping pipeline, including estimates for eight semantic classes, that is designed for straightforward fusion with range sensor data. Unlike other publications, our representation explicitly models uncertainties in the evidential model. We present results of our grid mapping pipeline based on a monocular vision setup and a stereo vision setup. Our mapping results are accurate and dense mapping due to the incorporation of a disparity- or depth-based ground surface estimation in the inverse perspective mapping. We conclude this paper by providing a detailed quantitative evaluation based on real traffic scenarios in the KITTI odometry benchmark dataset and demonstrating the advantages compared to other semantic grid mapping approaches.
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Ni, Jianjun, Tao Gong, Yafei Gu, Jinxiu Zhu, and Xinnan Fan. "An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot." Computational Intelligence and Neuroscience 2020 (February 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/7490840.
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The robot simultaneous localization and mapping (SLAM) is a very important and useful technology in the robotic field. However, the environmental map constructed by the traditional visual SLAM method contains little semantic information, which cannot satisfy the needs of complex applications. The semantic map can deal with this problem efficiently, which has become a research hot spot. This paper proposed an improved deep residual network- (ResNet-) based semantic SLAM method for monocular vision robots. In the proposed approach, an improved image matching algorithm based on feature points is presented, to enhance the anti-interference ability of the algorithm. Then, the robust feature point extraction method is adopted in the front-end module of the SLAM system, which can effectively reduce the probability of camera tracking loss. In addition, the improved key frame insertion method is introduced in the visual SLAM system to enhance the stability of the system during the turning and moving of the robot. Furthermore, an improved ResNet model is proposed to extract the semantic information of the environment to complete the construction of the semantic map of the environment. Finally, various experiments are conducted and the results show that the proposed method is effective.
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Chiș, Andrei. "A Modeling Method for Model-Driven API Management." Complex Systems Informatics and Modeling Quarterly, no. 25 (December 31, 2020): 1–18. http://dx.doi.org/10.7250/csimq.2020-25.01.
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This article reports on the Design Science engineering cycle for implementing a modeling method to support model-driven, process-centric API management. The BPMN standard was hereby enriched on semantic, syntactic and tool levels in order to provide a viable solution for integrating API requests with diagrammatic business process models in order to facilitate the documentation or testing of REST API calls directly in a modeling environment. The method can be implemented by stakeholders that need to map and manage their API ecosystem, thus gaining more API management agility and improving their software engineering productivity. By assimilating API ecosystem conceptualization in the modeling environment, the proposal differs from both RPA (which typically employs non-BPMN process diagramming e.g., in UIPath) and BPM Systems (which typically isolate all API-related semantics outside the process modeling language to keep the diagrammatic representation standard-compliant).
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Zhou, Deyu, and Yulan He. "Semi-Supervised Learning of Statistical Models for Natural Language Understanding." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/121650.
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Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved inF-measure.
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Sahu, M., and A. Ohri. "VECTOR MAP GENERATION FROM AERIAL IMAGERY USING DEEP LEARNING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 157–62. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-157-2019.
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<p><strong>Abstract.</strong> We propose a simple yet efficient technique to leverage semantic segmentation model to extract and separate individual buildings in densely compacted areas using medium resolution satellite/UAV orthoimages. We adopted standard UNET architecture, additionally added batch normalization layer after every convolution, to label every pixel in the image. The result obtained is fed into proposed post-processing pipeline for separating connected binary blobs of buildings and converting it into GIS layer for further analysis as well as for generating 3D buildings. The proposed algorithm extracts building footprints from aerial images, transform semantic to instance map and convert it into GIS layers to generate 3D buildings. We integrated this method in Indshine’s cloud platform to speed up the process of digitization, generate automatic 3D models, and perform the geospatial analysis. Our network achieved &sim;70% Dice coefficient for the segmentation process.</p>
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Moore, Alison Rotha. "Progress and tensions in modelling register as a semantic configuration." Language, Context and Text 2, no. 1 (January 29, 2020): 22–58. http://dx.doi.org/10.1075/langct.00020.moo.
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Abstract Halliday (1978: 111) defines register as “the configuration of semantic resources that the member of a culture typically associates with a situation type.” Elsewhere, however, he stresses that when we talk of “a register” this is a term of convenience: register is more properly theorised as continuous variation along many linguistic dimensions. In this paper I review progress in our capacity to describe register and context of situation and ask whether the tension between discrete and continuous models of register might hinder such progress. I then consider Hasan’s (1983, 2013) contextually-open networked model of message semantics, arguing that in conjunction with context networks it has potential to map register variation but still needs to be tested across a large and varied set of domains. Examples from healthcare interaction ground the discussion.