Semantic Perception Research Articles

Causal Conditions of the Persian Curriculum Model Centered on Storytelling

Background and Aim: Undoubtedly, in the formal education system of any country, all levels of education are considered the most important and fundamental. The impact of this level on the academic success of students is significant throughout their lives and educational period. The purpose of the current research was to investigate the causal conditions of the Persian curriculum model centered on storytelling. Methods: The research method was qualitative, using content analysis. The population consisted of all pre-school teachers in the city of Kerman, totaling 1400 individuals. Sampling was done purposively until theoretical saturation of data (involving 21 participants). The research instrument in the qualitative part was deep and unstructured interviews. For data analysis in the qualitative section, open, axial, and selective coding methods were used. Results: Results indicated that the dimensions and components of the causal conditions of the Persian curriculum model centered on storytelling included 8 selective codes: emotional stimulation, strengthening perceptual power, enhancing critical thinking, academic effort, fluid learning, moral development, semantic perception, and identity strengthening. Conclusion: It is suggested that visualization techniques be used to facilitate the understanding of learning objectives in the storytelling process for children. In such a scenario, the comprehension of concepts for them becomes easier. It is recommended that educators endeavor to establish a connection with children during the storytelling process and consistently seek their opinions on the topics presented in the story.

Multi-Modal Remote Sensing Image Segmentation with Intuition-Inspired Hypergraph Modeling.

Multi-modal remote sensing (RS) image segmentation aims to comprehensively utilize multiple RS modalities to assign pixel-level semantics to the studied scenes, which can provide a new perspective for global city understanding. Multi-modal segmentation inevitably encounters the challenge of modeling intra- and inter-modal relationships, i.e., object diversity and modal gaps. However, the previous methods are usually designed for a single RS modality, limited by the noisy collection environment and poor discrimination information. Neuropsychology and neuroanatomy confirm that the human brain performs the guiding perception and integrative cognition of multi-modal semantics through intuitive reasoning. Therefore, establishing a semantic understanding framework inspired by intuition to realize multi-modal RS segmentation becomes the main motivation of this work. Drived by the superiority of hypergraphs in modeling high-order relationships, we propose an intuition-inspired hypergraph network (I2HN) for multi-modal RS segmentation. Specifically, we present a hypergraph parser to imitate guiding perception to learn intra-modal object-wise relationships. It parses the input modality into irregular hypergraphs to mine semantic clues and generate robust mono-modal representations. In addition, we also design a hypergraph matcher to dynamically update the hypergraph structure from the explicit correspondence of visual concepts, similar to integrative cognition, to improve cross-modal compatibility when fusing multi-modal features. Extensive experiments on two multi-modal RS datasets show that the proposed I2HN outperforms the state-of-the-art models, achieving F1/mIoU accuracy 91.4%/82.9% on the ISPRS Vaihingen dataset, and 92.1%/84.2% on the MSAW dataset. The complete algorithm and benchmark results will be available online.

Concealed Object Detection for Passive Millimeter-Wave Security Imaging Based on Task-Aligned Detection Transformer

Passive millimeter-wave (PMMW) is a significant potential technique for human security screening. Several popular object detection networks have been used for PMMW images. However, restricted by the low resolution and high noise of PMMW images, PMMW hidden object detection based on deep learning usually suffers from low accuracy and low classification confidence. To tackle the above problems, this paper proposes a Task-Aligned Detection Transformer network, named PMMW-DETR. In the first stage, a Denoising Coarse-to-Fine Transformer (DCFT) backbone is designed to extract long- and short-range features in the different scales. In the second stage, we propose the Query Selection module to introduce learned spatial features into the network as prior knowledge, which enhances the semantic perception capability of the network. In the third stage, aiming to improve the classification performance, we perform a Task-Aligned Dual-Head block that decouples the classification and regression tasks, and enhances the interaction between the two heads. Based on our self-developed PMMW security screening dataset, experimental results including comparison with State-Of-The-Art (SOTA) methods and ablation study demonstrate that the PMMW-DETR obtains higher accuracy and classification confidence than previous works, and exhibits robustness to the PMMW images of low quality.

Классическая сонатная форма: контекст современного музыкально-педагогического опыта

Importance. The range of issues of fortepiano pedagogy related to the development of the classical sonata form in succession with the composing and performing experience of the pre-classical period is considered. The problem of mastering the modern musical and pedagogical experience in the field of studying the classical sonata form in the fortepiano classroom is solved. Materials and methods. The semiotic approach to the musical text is used as the main methodological principle, as well as historical and cultural commentary and reconstruction of the authentic environment in which the classical sonata form was created and developed. The expediency of using the “quasi-score of the performing concept” in pedagogical communication is shown. The justification of the introduced concept contains various examples of reading the urtext. The proposed methods can be widely used in the sequential process of mastering a large form in an instrumental classroom. Results and Discussion. A useful result of the proposed approach is a meaningful attitude to performance technique as a resource for generating variants of the correct author’s interpretation of a work of art. Despite the fact that the large form is an obligatory part of music pedagogy, practicing musicians face problems of formal and semantic perception of the text in the process of participating in the dialogue environment “teacher – student”, “composer – instrumentalist”. By analogy with the term quasi-scores used in the art criticism analysis of a large form, the concept of “quasi-score of the performing concept” is introduced, which allows to enrich pedagogical communication with a large volume of historical and cultural information tied to the artistic text of the work and the technology of its performance. Concrete examples of the mutual influence of the largest representatives of the composer and performing practice of the 18th century are given and interpreted. Conclusion. The idea of the need to develop the ability of an instrumentalist to understand, hear and reproduce in performing practice the constructs of the musical language generated in the syncretism of the compositional and performing practice of the epochs of Johann Kunau, the Bach family, Domenico Scarlatti, Georg Friedrich Handel, Joseph Haydn, Muzio Clementi, Wolfgang Amadeus Mozart, Ludwig van Beethoven is argued.

Shared-Weight-Based Multi-Dimensional Feature Alignment Network for Oriented Object Detection in Remote Sensing Imagery.

Arbitrarily Oriented Object Detection in aerial images is a highly challenging task in computer vision. The mainstream methods are based on the feature pyramid, while for remote-sensing targets, the misalignment of multi-scale features is always a thorny problem. In this article, we address the feature misalignment problem of oriented object detection from three dimensions: spatial, axial, and semantic. First, for the spatial misalignment problem, we design an intra-level alignment network based on leading features that can synchronize the location information of different pyramid features by sparse sampling. For multi-oriented aerial targets, we propose an axially aware convolution to solve the mismatch between the traditional sampling method and the orientation of instances. With the proposed collaborative optimization strategy based on shared weights, the above two modules can achieve coarse-to-fine feature alignment in spatial and axial dimensions. Last but not least, we propose a hierarchical-wise semantic alignment network to address the semantic gap between pyramid features that can cope with remote-sensing targets at varying scales by endowing the feature map with global semantic perception across pyramid levels. Extensive experiments on several challenging aerial benchmarks show state-of-the-art accuracy and appreciable inference speed. Specifically, we achieve a mean Average Precision (mAP) of 78.11% on DOTA, 90.10% on HRSC2016, and 90.29% on UCAS-AOD.

The time-course of real-world scene perception: Spatial and semantic processing.

Real-world scene perception unfolds remarkably quickly, yet the underlying visual processes are poorly understood. Space-centered theory maintains that a scene's spatial structure (e.g., openness, mean depth) can be rapidly recovered from low-level image statistics. In turn, the statistical relationship between a scene's spatial properties and semantic content allows for semantic identity to be inferred from its layout. We tested this theory by investigating (1) the temporal dynamics of spatial and semantic perception in real-world scenes, and (2) dependencies between spatial and semantic judgments. Participants viewed backward-masked images for 13.3 to 106.7ms, and identified the semantic (e.g., beach, road) or spatial structure (e.g., open, closed-off) category. We found no temporal precedence of spatial discrimination relative to semantic discrimination. Computational analyses further suggest that, instead of using spatial layout to infer semantic categories, humans exploit semantic information to discriminate spatial structure categories. These findings challenge traditional 'bottom-up' views of scene perception.

A Semantic Mapping Method of Relation Representation Enhancement for Few-Shot Knowledge Graph Completion

Few-shot knowledge graph completion (FKGC) tasks involve determining the authenticity of triple candidates using a small number of reference triples with a given relation. Intuitively, the expression of relation features contributes to the close correlation among the triples with the same relation. Therefore, the relation features are not comprehensive enough, leading to the fact that the triples cannot learn sufficient association information for the FKGC task. In this paper, an enhanced relation semantic representation model is constructed for associative reference triples from both aspects of external structure and internal semantics. On the one hand, as the structure around the triple is helpful to understand the relation semantics implicated in the triple, a graph convolution network with attention and relation features is proposed to obtain the graph structure features. Furthermore, the local structure information of triples can be used to learn the deep relation semantics. On the other hand, entity information can enhance the perception of the relation semantics in the triple. Afterward, in order to associate the triples with the same relation by enhanced relation semantics, a semantic mapping method is proposed, which uses shared merged variables to map the relation, entity, and graph structure features into the same embedding space. Finally, a prototype network based on attention convolution is established to extract the relation prototype representation, and then classify query triples to achieve the purpose of completing the knowledge graph. Through experimental verification, the proposed model achieves excellent performance on two datasets commonly used in the FKGC.

METAPHYSICS OF SPEECH COMMUNICATION

The article deals with the construction of a methodology for the theoretical analysis of speech communication (SC). The article analyzes theoretical reductions of the speech communication model, which are considered an obstacle to an adequate study of SC. The program of a non-reductionist approach to the analysis of SC includes the requirement 1. to consider speech as speech actions aimed at organizing both SC and joint activities (JA); 2. to distinguish images of consciousness from their verbal models; 3. to see the semantic perception of speech as the construction of the speech message content by the recipient from his/her knowledge; 4. to allow the existence of speech only in the processes of its production and reception, assuming that outside these processes, the speech content exists as a certain set of images of communicants’ consciousness; 5. to introduce both single and multiple communicants in to the SC objectarea.

Semantic Cameras for 360-Degree Environment Perception in Automated Urban Driving

The European UP-Drive project addresses transportation-related challenges by providing key contributions that enable fully automated vehicle navigation and parking in complex urban areas, which results in a safer, inclusive, affordable and environmentally friendly transportation system. For this purpose, the project consortium developed a prototype electrical vehicle equipped with cameras and LiDARs sensors that is capable to autonomously drive around the city and find available parking spots. In UP-Drive, we created an accurate, robust and redundant multi-modal environment perception system that provides 360° coverage around the vehicle. This paper summarizes the work of the project related to the surround view semantic perception using fisheye and narrow field-of-view semantic virtual cameras. Deep learning-based semantic, instance and panoptic segmentation networks, which satisfy requirements in accuracy and efficiency have been developed and integrated into the final prototype. The UP-Drive automated vehicle has been successfully demonstrated in urban areas after extensive experiments and numerous field tests.

CSF: Closed-mask-guided semantic fusion method for semantic perception of unknown scenes

• A mask-guided semantic fusion method for optimizing the results of semantic segmentation. • A closed mask generation (CEG) module for post-processing of the edge detection algorithm. • A semantic confidence fusion (SCF) module to fuse the semantic segmentation results. • This method does not need to obtain the target data in advance. • This method has been verified in two scenarios. Though many high-precision semantic segmentation models have been proposed, how to improve the generalization ability of these models is still an urgent problem. Recently, a great number of unsupervised domain adaptation (UDA) algorithms around domain adaptation problems have been studied in semantic segmentation. These methods require labeled source domain data and unlabeled target domain data. In this paper, we propose a closed-mask-guided semantic fusion method (CSF) to improve the semantic segmentation results of unknown scenes, where the target domain data is not obtained in advance. First, a Closed Mask Generation (CMG) module is designed to convert the edge detection result into a mask that can segment the image into several image blocks. Then, a Semantic Confidence Fusion (SCF) module based on information entropy and voting method is introduced, which can select reliable semantic segmentation results for each image block by comparing the confidence of several semantic segmentation networks . In addition, the experimental results on both KITTI and COCO Stuff datasets validate the effectiveness of this method. The code is publicly available at https://github.com/tryhere/CSF .

Невладение приемами и средствами медиации как педагогический дефицит в методике преподавания иностранного языка

The purpose of the research is to identify the psycholinguistic aspects of mediation in the methodology of foreign language teaching. The paper provides a methodological justification of the approach to characterising the lack of mediation competencies as a pedagogical deficit of a foreign language teacher, provides a brief definitional analysis of the notion of “mediation” and approaches to its interpretation, substantiates the psycholinguistic approach to consideration of mediation, gives examples of the need for mediation competencies. Scientific novelty of the research lies in interpreting mediation non-mastery as a teacher’s pedagogical deficit, compensating for which it is possible to overcome various kinds of issues regarding students’ semantic perception of educational speech messages in a foreign language. The researcher proposes a psycholinguistic approach, namely the theory of speech activity, as a methodological justification. The result of the analysis amounts to the identification of the theoretical and methodological basis for the study of mediation as a teacher’s specific educational speech actions to create optimal conditions for understanding.

RSPMP: real-time semantic perception and motion planning for autonomous navigation of unmanned ground vehicle in off-road environments

RSPMP: real-time semantic perception and motion planning for autonomous navigation of unmanned ground vehicle in off-road environments

Is there an empirical case for semantic perception?

ABSTRACT I argue that results in perception science do not support the claim that there is semantic perception or that typical, unreflective utterance comprehension is a perceptual process. Phenomena discussed include evidence-insensitivity, the Stroop effect, pop-out, and adaptation – as well as how these phenomena might relate to the function, format, and structure of perceptual representations. An emphasis is placed on non-inferential transitions from perceptual to conceptual representations, which are important for debates about the admissible contents of perception more generally.

Research on Optimization of 3D Tourism Virtual Crossover Scene based on Semantic Perception Analysis

The objective of this paper is to optimize the scene of tourism virtual perception space. Based on the abstract method of semantic feature points, the computing model of semantic perception of single-cultural landscape and multi-cultural landscape is established. Using the digital elevation model, an empirical study on the semantic perception of cultural landscape in the western Tombs of Qing Dynasty is carried out. Taking the traditional Chinese culture of the site selection of royal tombs and the feudal hierarchy represented as the semantic criteria, eighteen feature points were extracted from two representative tomb cultural landscapes from different landscape perspectives, and the corresponding weight coefficients were assigned to each feature point from different landscape perspectives; based on the results of perceptual degree calculation, the semantic mining of the existing sightseeing routes is carried out and the optimization scheme is designed. From the perspective of tourists’ perception of landscape, tourism resources are deeply mined to better reflect the value of landscape and realize the coupling and interaction between virtual tourism and tourism economy.

Digitalization Trends in Education and Blended Learning

The article under review actualizes the problem of using digital computer technologies in the process of blended learning. The notion of «blended learning» is determined and specified according to various sources. Various models and ways of organizing this kind of work as an alternative to the standard form of learning are presented. The article presents a positive experience of using blended learning technology. Much attention is paid to visualization as one of the teaching tools to create educational materials for blended learning. The synthesis of verbal materials (especially spoken) and visual elements (pictures, graphic notes, animations, films, plots, and diagrams, etc.) within the limits of one text is an instrument to optimize the process of semantic perception and understanding text information. All the benefits of this modern technology allow students to establish a holistic adoption of this model and a positive direction for the development of blended learning as an innovative teaching technology

Visible–infrared person re-identification based on key-point feature extraction and optimization

Feature extraction for visible–infrared person re-identification (VI-ReID) is challenging because of the cross-modality discrepancy in the images taken by different spectral cameras. Most of the existing VI-ReID methods often ignore the potential relationship between features. In this paper, we intend to transform low-order person features into high-order graph features, and make full use of the hidden information between person features. Therefore, we propose a multi-hop attention graph convolution network (MAGC) to extract robust person joint feature information using residual attention mechanism while reducing the impact of environmental noise. The transfer of higher order graph features within MAGC enables the network to learn the hidden relationship between features. We also introduce the self-attention semantic perception layer (SSPL) which can adaptively select more discriminant features to further promote the transmission of useful information. The experiments on VI-ReID datasets demonstrate its effectiveness.

SEMANTIC PERCEPTION OF NEOLOGISM BORROWINGS IN TERMS OF MEDIA LANGUAGE

Purpose. This study is developed in order to identify the degree of understanding of neologism borrowings meanings used in the mass media language, hypothetical determination of the digestibility level of the articles, abounding in borrowings, by means of correlating borrowing meanings with thematic areas.
 Research method and methodology. The research methods were as follows: a conducting a questionnaire Internet survey, as well as carrying out a descriptive and a comparative analysis.
 Results. An analysis of the semantic perception of neologism borrowings by a wide range of the reading public demonstrated the lack of a 100% degree of understanding and the impossibility of unanimous correlation of neologism borrowings meanings with topic areas. This fact indicates the impossibility of a full and quality processing of the information received from the mass media. The need for language canons compliance was identified.
 Practical implications. Theoretical and practical results can be applied in lecture courses on the lexicology of the English language and special courses on the discourse of the modern media.

Unity Assumption in Audiovisual Emotion Perception.

We experience various sensory stimuli every day. How does this integration occur? What are the inherent mechanisms in this integration? The “unity assumption” proposes a perceiver’s belief of unity in individual unisensory information to modulate the degree of multisensory integration. However, this has yet to be verified or quantified in the context of semantic emotion integration. In the present study, we investigate the ability of subjects to judge the intensities and degrees of similarity in faces and voices of two emotions (angry and happy). We found more similar stimulus intensities to be associated with stronger likelihoods of the face and voice being integrated. More interestingly, multisensory integration in emotion perception was observed to follow a Gaussian distribution as a function of the emotion intensity difference between the face and voice—the optimal cut-off at about 2.50 points difference on a 7-point Likert scale. This provides a quantitative estimation of the multisensory integration function in audio-visual semantic emotion perception with regards to stimulus intensity. Moreover, to investigate the variation of multisensory integration across the population, we examined the effects of personality and autistic traits of participants. Here, we found no correlation of autistic traits with unisensory processing in a nonclinical population. Our findings shed light on the current understanding of multisensory integration mechanisms.

A Kernel Correlation-Based Approach to Adaptively Acquire Local Features for Learning 3D Point Clouds

3D models are used in a variety of CAX fields, and their key is 3D data geometry and semantic perception. However, semantic learning of 3D point clouds is a challenge due to the naturally distinct and disordered data structure, particularly for local features of point clouds. In this paper, we aim to provide machines with 3D object shape awareness, enhance the recognizability of 3D models, and enable them to allow precise geometric and semantic information in 3D point clouds. Firstly, a novel structure is proposed, namely kernel correlation learning block (KCB), which adaptively learns the local geometric features and global features at different layers, thereby enhancing the perception capacity of the network. Secondly, we developed a method to adaptively acquire and learning geometric features based on kernel correlation, and combine it with global information in the proposed KCB. Thirdly, the proposed KCB can be integrated and compatible with the typical point cloud structure in an end-to-end manner. Numerous experiments demonstrate the advantages of the proposed methods on typical 3D shape analysis approaches such as object classification, object segmentation, and semantic segmentation.

Omnisupervised Omnidirectional Semantic Segmentation

Modern efficient Convolutional Neural Networks (CNNs) are able to perform semantic segmentation both swiftly and accurately, which covers typically separate detection tasks desired by Intelligent Vehicles (IV) in a unified way. Most of the current semantic perception frameworks are designed to work with pinhole cameras and benchmarked against public datasets with narrow Field-of-View (FoV) images. However, there is a large accuracy downgrade when a pinhole-yielded CNN is taken to omnidirectional imagery, causing it unreliable for surrounding perception. In this paper, we propose an omnisupervised learning framework for efficient CNNs, which bridges multiple heterogeneous data sources that are already available in the community, bypassing the labor-intensive process to have manually annotated panoramas, while improving their reliability in unseen omnidirectional domains. Being omnisupervised, the efficient CNN exploits both labeled pinhole images and unlabeled panoramas. The framework is based on our specialized ensemble method that considers the wide-angle and wrap-around features of omnidirectional images, to automatically generate panoramic labels for data distillation. A comprehensive variety of experiments demonstrates that the proposed solution helps to attain significant generalizability gains in panoramic imagery domains. Our approach outperforms state-of-the-art efficient segmenters on highly unconstrained IDD20K and PASS datasets.