Semantic Mapping Research Articles

Gender Identification of Chinese Mitten Crab Juveniles Based on Improved Faster R-CNN

The identification of gender in Chinese mitten crab juveniles is a critical prerequisite for the automatic classification of these crab juveniles. Aiming at the problem that crab juveniles are of different sizes and relatively small, with unclear male and female characteristics and complex background environment, an algorithm C-SwinFaster for identifying the gender of Chinese mitten crab juveniles based on improved Faster R-CNN was proposed. This algorithm introduces Swin Transformer as the backbone network and an improved Path Aggregation Feature Pyramid Network (PAFPN) in the neck to obtain multi-scale high-level semantic feature maps, thereby improving the gender recognition accuracy of Chinese mitten crab male and female juveniles. Then, a self-attention mechanism is introduced into the region of interest pooling network (ROI Pooling) to enhance the model’s attention to the classification features of male and female crab juveniles and reduce background interference on the detection results. Additionally, we introduce an improved non-maximum suppression algorithm, termed Softer-NMS. This algorithm refines the process of determining precise target candidate boxes by modulating the confidence level, thereby enhancing detection accuracy. Finally, the focal loss function is introduced to train the model, reducing the weight of simple samples during the training process, and allowing the model to focus more on samples that are difficult to distinguish. Experimental results demonstrate that the enhanced C-SwinFaster algorithm significantly improves the identification accuracy of male and female Chinese mitten crab juveniles. The mean average precision (mAP) of this algorithm reaches 98.45%, marking a 10.33 percentage point increase over the original model. This algorithm has a good effect on the gender recognition of Chinese mitten crab juveniles and can provide technical support for the automatic classification of Chinese mitten crab juveniles.

Indoor functional subspace division from point clouds based on graph neural network

Indoor scenes are closely related to human life and contain rich geometric and semantic information. Dividing indoor spaces from data is crucial for multiple applications, such as navigation and digital twins. However, achieving this task is challenging. Traditional indoor space division methods only represent buildings, floors, and rooms to some extent, lacking semantic descriptions of indoor elements and their spatial arrangements. To divide an indoor space more finely, a novel indoor space subdivision method is proposed in this paper. Our method leverages the flexible space subdivision framework (FSS) to categorize indoor space into free navigation subspace, object subspace, and functional subspace. To define functional subspaces, we present a taxonomy for the spatial layout patterns of common indoor elements (like tables, chairs, ceilings, walls, floors, etc.). Then, we introduce scene graphs to represent indoor 3D scenes, where each node represents an indoor element and each edge encodes the spatial relationship between the elements. Finally, a node classification network is proposed to segment indoor scene into subspaces and predicts their (semantic) functions. We select 9 buildings of Matterport3D and 6 areas in S3DIS and merge them to form our dataset for training and testing our method. Experiments yield good results with up to 90.42% accuracy and 85.28% F1-scores in overall space subdivision. Moreover, compared with the various graph node classification networks, our method has achieved the best performance in indoor space subdivisions.

Causal inference of diachronic semantic maps from cross-linguistic synchronic polysemy data

Semantic maps are used in lexical typology to summarize cross-linguistic implicational universals of co-expression between meanings in a domain. They are defined as networks which, using as few links as possible, connect the meanings so that every isolectic set (i.e., set of meanings that can be expressed by the same word in some language) forms a connected component. Due to the close connection between synchronic polysemies and semantic change, semantic maps are often interpreted diachronically as encoding potential pathways of semantic extension. While semantic maps are traditionally generated by hand, there have been attempts to automate this complex and non-deterministic process. I explore the problem from a new algorithmic angle by casting it in the framework of causal discovery, a field which explores the possibility of automatically inferring causal structures from observational data. I show that a standard causal inference algorithm can be used to reduce cross-linguistic polysemy data into minimal network structures which explain the observed polysemies. If the algorithm makes its link deletion decisions on the basis of the connected component criterion, the skeleton of the resulting causal structure is a synchronic semantic map. The arrows which are added to some links in the second stage can be interpreted as expressing the main tendencies of semantic extension. Much of the existing literature on semantic maps implicitly assumes that the data from the languages under analysis is correct and complete, whereas in reality, semantic map research is riddled by data quality and sparseness problems. To quantify the uncertainty inherent in the inferred diachronic semantic maps, I rely on bootstrapping on the language level to model the uncertainty caused by the given language sample, as well as on random link processing orders to explore the space of possible semantic maps for a given input. The maps inferred from the samples are then summarized into a consensus network where every link and arrow receives a confidence value. In experiments on cross-linguistic polysemy data of varying shapes, the resulting confidence values are found to mostly agree with previously published results, though challenges in directionality inference remain.

Parallel cognitive maps for multiple knowledge structures in the hippocampal formation.

The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information. However, objects can often be characterized by different types of relations simultaneously. How does the hippocampal formation handle the embedding of stimuli in multiple relational structures that differ vastly in their mode and timescale of acquisition? Does the hippocampal formation integrate different stimulus dimensions into one conjunctive map or is each dimension represented in a parallel map? Here, we reanalyzed human functional magnetic resonance imaging data from Garvert et al. (2017) that had previously revealed a map in the hippocampal formation coding for a newly learnt transition structure. Using functional magnetic resonance imaging adaptation analysis, we found that the degree of representational similarity in the bilateral hippocampus also decreased as a function of the semantic distance between presented objects. Importantly, while both map-like structures localized to the hippocampal formation, the semantic map was located in more posterior regions of the hippocampal formation than the transition structure and thus anatomically distinct. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps that reflect the embedding of objects in diverse relational structures.

Construction of Semantic Coherence Diagnosis Model of English Text based on Sentence Semantic Map

The current English composition automatic correction system rarely involves coherence quality analysis of compositions. Therefore, a semantic coherence diagnosis model for English texts was constructed based on sentence semantic maps, and its effectiveness was verified through experiments. Experimental results show that sub Graphical model 10, 12 and 13 exceed 200 on the first two test texts and 1 on the last two test texts. However, the differences between these three subgraphs on different test texts are not significant, with differences below 30 and 0.3. In addition, when extracting incoherent sentences, the F1 value reaches the optimal value at a threshold of 0.34, which is 87.54%. When the threshold is fixed at 0.34, the accuracy of extracting non coherent sentences also increases with the number of articles, reaching a maximum of 88.43%. At the same time, there was no significant difference in accuracy, recall, and F1 values among different English composition numbers, maintaining between 83% and 89%. The Pearson coefficient calculated in the actual comparison with the teacher's manual composition score is 0.6025, indicating a strong correlation between the two, indicating that the diagnostic results are reliable. Overall, the diagnostic model constructed in the study has strong accuracy and effectiveness, and is practical in the diagnosis of semantic coherence in actual English texts.

Controllable multi-domain semantic artwork synthesis

We present a novel framework for the multi-domain synthesis of artworks from semantic layouts. One of the main limitations of this challenging task is the lack of publicly available segmentation datasets for art synthesis. To address this problem, we propose a dataset called ArtSem that contains 40,000 images of artwork from four different domains, with their corresponding semantic label maps. We first extracted semantic maps from landscape photography and used a conditional generative adversarial network (GAN)-based approach for generating high-quality artwork from semantic maps without requiring paired training data. Furthermore, we propose an artwork-synthesis model using domain-dependent variational encoders for high-quality multi-domain synthesis. Subsequently, the model was improved and complemented with a simple but effective normalization method based on jointly normalizing semantics and style, which we call spatially style-adaptive normalization (SSTAN). Compared to the previous methods, which only take semantic layout as the input, our model jointly learns style and semantic information representation, improving the generation quality of artistic images. These results indicate that our model learned to separate the domains in the latent space. Thus, we can perform fine-grained control of the synthesized artwork by identifying hyperplanes that separate the different domains. Moreover, by combining the proposed dataset and approach, we generated user-controllable artworks of higher quality than that of existing approaches, as corroborated by quantitative metrics and a user study.

Whole‐body motion planning and tracking of a mobile robot with a gimbal RGB‐D camera for outdoor 3D exploration

AbstractAutonomous exploration is a fundamental problem for certain applications of unmanned ground vehicles (UGVs). Depth cameras are widely used in exploring indoor and specific outdoor environments. However, existing methods suffer from inefficient and unstable performance of exploration in open outdoor environments. This study develops a framework to achieve fast and robust three‐dimensional exploration by UGVs with a gimbal camera in challenging outdoor environments. A hierarchical reactive planning approach based on a multilayered map is proposed to improve exploration efficiency. Additionally, an adaptive semantic mapping algorithm is proposed to more accurately and stably represent the environmental localization uncertainty. Localization uncertainty is integrated into the planning approach to decrease odometry drift and prevent localization failure, further enhancing the exploration robustness. A viewpoint tracking controller based on terrain‐aware model predictive control is proposed to guarantee the accuracy of the planned viewpoint tracking and to further improve the robustness of localization and exploration on rough terrains. Finally, several physical‐engine simulations and experiments in outdoor environments are performed. The comparisons to existing state‐of‐the‐art approaches have verified the effectiveness of the proposed approach. The code of our system will be available at https://github.com/HITSZ-NRSL/Open3DExplorer.git.

Symptomatic progression of frontotemporal dementia with the TARDBP I383V variant

ABSTRACT We present a longitudinal description of a man with the TARDBP I383V variant of frontotemporal dementia (FTD). His progressive changes in behavior and language resulted in a diagnosis of the right temporal variant of FTD, also called the semantic behavioral variant (sbvFTD). We also present data from a small series of patients with the TARDBP I383V variant who were enrolled in a nationwide FTD research collaboration (ALLFTD). These data support slowly progressive loss of semantic function. While semantic dementia is infrequently considered genetic, the TARDBP I383V variant seems to be an exception. Longitudinal analyses in larger samples are warranted.

FACEMUG: A Multimodal Generative and Fusion Framework for Local Facial Editing.

Existing facial editing methods have achieved remarkable results, yet they often fall short in supporting multimodal conditional local facial editing. One of the significant evidences is that their output image quality degrades dramatically after several iterations of incremental editing, as they do not support local editing. In this paper, we present a novel multimodal generative and fusion framework for globally-consistent local facial editing (FACEMUG) that can handle a wide range of input modalities and enable fine-grained and semantic manipulation while remaining unedited parts unchanged. Different modalities, including sketches, semantic maps, color maps, exemplar images, text, and attribute labels, are adept at conveying diverse conditioning details, and their combined synergy can provide more explicit guidance for the editing process. We thus integrate all modalities into a unified generative latent space to enable multimodal local facial edits. Specifically, a novel multimodal feature fusion mechanism is proposed by utilizing multimodal aggregation and style fusion blocks to fuse facial priors and multimodalities in both latent and feature spaces. We further introduce a novel self-supervised latent warping algorithm to rectify misaligned facial features, efficiently transferring the pose of the edited image to the given latent codes. We evaluate our FACEMUG through extensive experiments and comparisons to state-of-the-art (SOTA) methods. The results demonstrate the superiority of FACEMUG in terms of editing quality, flexibility, and semantic control, making it a promising solution for a wide range of local facial editing tasks.

Utilization of the Modified Frayer Model and Semantic Map Its Effectiveness on The Attitude of Grade 7 Science Learners

Utilization of the Modified Frayer Model and Semantic Map Its Effectiveness on The Attitude of Grade 7 Science Learners

ЖЕНСКИЙ РОМАН – ЛИТЕРАТУРА, МАКУЛАТУРА И… ПСИХОЛОГИЯ

In the traditional women’s novel, as a significant genre of the impression industry, a system of views and values is very openly proclaimed, which significantly diverges from the value system of the male part of the population. The end of the twentieth century was marked by the formation of various value systems in women’s novels. Moreover, one of these value systems most likely approaches “male”, while the other is clearly close to mass culture. A traditional women’s novel is always characterized by a fairly typical set of very similar characters. They may be fatally different from each other in appearance, but they always perform the same semantic function. For example, the main character, in addition to a constant pursuer, constantly finds herself with a disinterested helper. At the same time, the personal qualities ofa man in romance novels traditionally do not matter at all. The main character, fulfilling the function of the object of the great love of the main character, however, is never an outstanding personality. Essentially, as a rule, he is not busy with anything serious in life either. At the same time, he is always financially secure and always restless. As a result, even if he is married, it will be to a bitch. But the most important thing is that an affair with him is entirely the choice and decision of the main character.

Семантика и функции шорских маркеров подобия в сфере компаративности

This paper describes the semantics and etymology of comparative operators in the Shor language compared to other Turkic languages. Shor similarity constructions as structures of equality are referred to as “ungraded constructions,” with two components of such a construction being equal or similar. The characterizing component expressed by a noun and marked by a postpositional unit called “an operator” forms a comparative combination. Shor comparative operators exhibit unique features as markers of similarity. Comparative combinations characterizing nouns use the syntactic words tӧӧy, chüünüγ, oshqash, sheni, che, an adjective teƞ, and affixes =tiγ, =tiyi as operators. These combinations can be attributes or predicates in a sentence. The semantic function of these operators is mainly “equative,” covering qualitative and quantitative characteristics. The operators tӧӧy, chüünüγ, teƞ denote a high degree of similarity between objects. The operator oshqash denotes the similarity in the appearance and character of objects. The operators sheni and che designate the equal size of objects. These operators, excluding sheni and che, take predicative affixes in the present indicative tense in the predicate position; otherwise, they are used with an auxiliary verb. The affix =tiγ expresses a comparative meaning in metaphors; otherwise, it denotes the possession of some feature. The affix =tiyi is used in the position of a predicate. Comparative combinations characterizing verbs use the syntactic words chilep (chylap), sheni (shenep, sheninche), and che as operators. These combinations can serve as adverbial modifiers of manner, with their semantic function emphasizing the “similative” identity of the manner of action.

Semantic Segmentation-Based Occupancy Grid Map Learning With Automotive Radar Raw Data

Semantic Segmentation-Based Occupancy Grid Map Learning With Automotive Radar Raw Data

Millimeter Wave Radar and Vision Fusion based Semantic Simultaneous Localization and Mapping

Millimeter Wave Radar and Vision Fusion based Semantic Simultaneous Localization and Mapping

Construction of Image Education Knowledge Map Model Based on Data Mining Technology

Abstract Data mining (DM) technology is increasingly used in higher education, especially imaging education. The IEKMM model connects knowledge, problems, and abilities, addressing asymmetrical relationships and supporting network reasoning tasks. The SSME model preserves IEKMM’s semantic information, enhancing instruction quality and efficiency, and advancing personalized learning initiatives. Findings reveal that the distributed representation of entities and relationships, trained using the SSME (Semantic Symbol Mapping Embedding) model, effectively preserves the original semantic information of the IEKMM. This provides a foundation for implementing knowledge maps in educational settings and is crucial for advancing personalized learning initiatives.

TextSLAM: Visual SLAM With Semantic Planar Text Features.

We propose a novel visual SLAM method that integrates text objects tightly by treating them as semantic features via fully exploring their geometric and semantic prior. The text object is modeled as a texture-rich planar patch whose semantic meaning is extracted and updated on the fly for better data association. With the full exploration of locally planar characteristics and semantic meaning of text objects, the SLAM system becomes more accurate and robust even under challenging conditions such as image blurring, large viewpoint changes, and significant illumination variations (day and night). We tested our method in various scenes with the ground truth data. The results show that integrating texture features leads to a more superior SLAM system that can match images across day and night. The reconstructed semantic 3D text map could be useful for navigation and scene understanding in robotic and mixed reality applications.

Air-Ground Collaboration With SPOMP: Semantic Panoramic Online Mapping and Planning

Air-Ground Collaboration With SPOMP: Semantic Panoramic Online Mapping and Planning

Robust Data Association Against Detection Deficiency for Semantic SLAM

Robust and accurate object association is essential for precise 3D object landmark inference in semantic Simultaneous Localization and Mapping (SLAM), and yet remains challenging due to the detection deficiency caused by high miss detection rate, false alarm, occlusion and limited field-of-view, etc. The 2D location of an object is a crucial complementary cue to the appearance feature, especially in the case of associating objects across frames under large viewpoint changes. However, motion model or trajectory pattern based methods struggle to infer object motion reliably with a moving camera. In this paper, by exploiting the local projective warping consistency, a local homography based 2D motion inference method is proposed to sequentially estimate the object location along with uncertainty. By integrating the deep appearance feature and semantic information, an object association method, named HOA, which is robust to detection deficiency is proposed. Experimental evaluations suggest that the proposed motion prediction method is capable of maintaining a low cumulative error over a long duration, which enhances the object association performance in both accuracy and robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work aims to consistently associate 2D detection boxes corresponding to the same 3D object across images. In tasks of landmark-based navigation, collision avoidance, grasping and manipulation, objects in the task space are commonly simplified into 3D enveloping surfaces (e.g. cuboid or ellipsoid) by using 2D object detection boxes from multiple image views, and accurate data association is a prerequisite for precise enveloping surface reconstruction. This problem remains challenging considering the imperfect object detections, the appearance similarity of objects and the unpredictable trajectory of the moving camera. This work proposes a long-term reliable 2D location prediction algorithm that is capable of handling the complex motion of the target. Along with the appearance feature extracted by a retrain-free deep learning based model, this work proposes an object association method that can simultaneously deal with multiple objects with unknown object categories under the moving camera scenario.

Քրոնոտոպի գործառույթը Խ.Աբովյանի «Վերք Հայաստանի» վեպում / THE FUNCTION OF CHRONOTOPE IN KH. ABOVYAN’S NOVEL «WOUNDS OF ARMENIA»

The subject of the analysis of the article is the function of the chronotope, the spacetime continuum in the historical novel “Wounds of Armenia” by Khachatur Abovyan. Drawing upon fundamental principles of the theory of the relationship between the forms of time and the chronotope of M. Bakhtin and the theory of the spatial chronotope and the cultural memory of Y. Lotman, we approached the text of the novel from a fresh perspective to show that there is nothing spontaneous in the “Wounds of Armenia” novel: it is a clear and precisely thought-out novel structure formed on the basis of a certain system, where the chronotope plays an important organizing role in the author’s plan and construction of the artistic world. Our purpose is to consider, within the limits of possibilities, the features of the manifestation of the chronotope in the “Wounds of Armenia” and to show whether the manifestation of space-time regular connections in the historical novel should be considered as a general artistic device or these are the most important, genre-forming features of the novel form that first appeared in the Armenian literature. In accordance with the above, we have formulated the main objectives of the study: given the importance of the combined application of various methods of analysis of the artwork, using hermeneutic and structural methods of criticism to show certain spatial and temporal laws underlying the artistic text, organizing the text, to reveal the complex chain of combinations of chronotopes, to explore the relationship between the part and the whole, to interpret the iconic, coded space that determines the genre features and poetics of text structure, to identify some typologically stable chronotopes in the novel and show their semantic and constructive role and function in the process of forming the composition and structure of the novel. We have summarized the findings of the research concerning the philosophical and creative worldview of the great Armenian enlightenment novelist, his intentions in creating novels and the tools for their realization in Conclusion.

Renewable energy and agriculture: Developing a common language for technological integration

In recent years, the agricultural sector has recognized the importance of renewable energy for its sustainable development. This shift is driven by the need to address global challenges such as climate change, energy security, and rising costs of traditional energy sources. Renewable energy, including solar, wind, bioenergy, and other sustainable sources, offers a promising solution while enhancing agricultural practices’ resilience and efficiency. The integration of renewable energy in agriculture serves multiple purposes. It provides farmers with cleaner, more sustainable, and cost-effective energy sources. For example, solar energy can power irrigation systems, reducing reliance on diesel pumps, and wind energy can support on-farm operations. Bioenergy derived from agricultural waste not only offers a renewable energy source but also helps manage waste.This article explores the application of Cross-Disciplinary Semantic Mapping in Renewable Energy and Agriculture. It analyzes key terms and concepts in both sectors and their semantic overlaps and divergences, highlighting the importance of linguistic nuances in interdisciplinary communication and collaboration.