Evidentiality and other types readjusted: interpersonal modality revisited

Interpersonal modality, bifurcating modalization and modulation, is an important construct of interpersonal meaning in the architecture of Systemic Functional Linguistics. By meticulously reviewing relevant researches from the perspectives of traditional modality and modality’s semantic map, three respects with respect to the system of interpersonal modality have been supplemented. Firstly, modalization, being subcategorized into possibility and usuality, is suggested to entertain evidentiality from the traditional sense. Secondly, considering the delicacy of the system of interpersonal modality, possibility in modalization should be further categorized into epistemic and root possibility; necessity as one subtype of modulation, superseding the original obligation in modulation, is subclassified into obligation and permission; inclination, being the other subtype of modulation, should be specified as the superordinate of volition and ability. Thirdly, the shifting of modal meanings from root possibility to epistemic possibility in modalization and from inclination to necessity in modulation should be clearly specified as far as language evolvement is concerned.

Application of bayesian networks in the evaluation of projects of corporate uniform

The article reveals the problem of making a decision on the design of a corporate uniform in the context of the contradictory requirements for uniformity of clothing for employees and the needs of participants in the dress code for self-expression. The combination of the above factors creates specificity in the assessment of uniform projects, since it becomes necessary to predict the stability of the dress code policy, the acceptance of its requirements by all interested parties. To solve this problem, a method is proposed for assessing the design decisions of a uniform from the standpoint of a balanced reflection in its design of the values of corporate culture and the possibilities of personal self-identification of participants in the dress code. The article considers an example of the development and testing of a semantic map, reflecting the probabilistic relationship of design parameters, in the assessment and refinement of models-proposals of a corporate uniform. The evaluation criteria presented in the semantic map format allow reflecting both the marketing role of the uniform in the promotion of the company′s services and the actual needs of the dress code participants identified during the expert discussions.

The Semantic Map of Aktionart and Troponymy of Old English Strong Verbs

The aim of this article is to analyse the semantic relations that hold between Old English primitive and derived verbs in terms of troponymy and Aktionsart. The results of this analysis are presented in a semantic map, while emphasis is made on the points of contact between these phenomena. The main conclusion is that semantic maps represent a more flexible and applicable methodology than previous work suggests since they have been used to deal with one language, to explain historical languages and to refer to specific lexical items. Likewise, this analysis shows evidence of an inherent relationship between both phenomena: troponymy and Aktionsart.

Semantic Mapping for Autonomous Subsea Intervention

This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based slam and 3D object recognition using a database of a priori known objects. The robot uses dvl, pressure, and ahrs sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the slam, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The slam provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.

Deep Visible and Thermal Camera-based Optimal Semantic Segmentation using Semantic Forecasting

Visible camera-based semantic segmentation and semantic forecasting are important perception tasks in autonomous driving. In semantic segmentation, the current frame's pixel level labels are estimated using the current visible frame. In semantic forecasting, the future frame's pixel-level labels are predicted using the current and the past visible frames and pixel-level labels. While reporting state-of-the-art accuracy, both of these tasks are limited by the visible camera's susceptibility to varying illumination, adverse weather conditions, sunlight and headlight glare etc. In this work, we propose to address these limitations using the deep sensor fusion of the visible and the thermal camera. The proposed sensor fusion framework performs both semantic forecasting as well as an optimal semantic segmentation within a multi-step iterative framework. In the first or forecasting step, the framework predicts the semantic map for the next frame. The predicted semantic map is updated in the second step, when the next visible and thermal frame is observed. The updated semantic map is considered as the optimal semantic map for the given visible-thermal frame. The semantic map forecasting and updating are iteratively performed over time. The estimated semantic maps contain the pedestrian behavior, the free space and the pedestrian crossing labels. The pedestrian behavior is categorized based on their spatial, motion and dynamic orientation information. The proposed framework is validated using the public KAIST dataset. A detailed comparative analysis and ablation study is performed using pixel-level classification and IOU error metrics. The results show that the proposed framework can not only accurately forecast the semantic segmentation map but also accurately update them.

Lexical diachronic semantic maps

This paper extends the scope of application of the semantic map model to diachronic lexical semantics. Combining a quantitative approach to large-scale synchronic polysemy data with a qualitative evaluation of the diachronic material in two text languages, ancient Egyptian and ancient Greek, it shows that weighted diachronic semantic maps can capture informative generalizations about the organization of the lexicon and its reshaping over time. The general methodology developed in the paper is illustrated with a case study of the semantic extension of time-related lexemes. This case study shows that the blend of tools well established in linguistic typology with proven methods of historical linguistics enables a principled approach to long-standing questions in the fields of diachronic semasiology and onomasiology.

The Method of Static Semantic Map Construction Based on Instance Segmentation and Dynamic Point Elimination

Semantic information usually contains a description of the environment content, which enables mobile robot to understand the environment and improves its ability to interact with the environment. In high-level human–computer interaction application, the Simultaneous Localization and Mapping (SLAM) system not only needs higher accuracy and robustness, but also has the ability to construct a static semantic map of the environment. However, traditional visual SLAM lacks semantic information. Furthermore, in an actual scene, dynamic objects will reduce the system performance and also generate redundancy when constructing map. these all directly affect the robot’s ability to perceive and understand the surrounding environment. Based on ORB-SLAM3, this article proposes a new Algorithm that uses semantic information and the global dense optical flow as constraints to generate dynamic-static mask and eliminate dynamic objects. then, to further construct a static 3D semantic map under indoor dynamic environments, a fusion of 2D semantic information and 3D point cloud is carried out. the experimental results on different types of dataset sequences show that, compared with original ORB-SLAM3, both Absolute Pose Error (APE) and Relative Pose Error (RPE) have been ameliorated to varying degrees, especially on freiburg3-walking-xyz, the APE reduced by 97.78% from the original average value of 0.523, and RPE reduced by 52.33% from the original average value of 0.0193. Compared with DS-SLAM and DynaSLAM, our system improves real-time performance while ensuring accuracy and robustness. Meanwhile, the expected map with environmental semantic information is built, and the map redundancy caused by dynamic objects is successfully reduced. the test results in real scenes further demonstrate the effect of constructing static semantic maps and prove the effectiveness of our Algorithm.