Semantic Mapping Research Articles

Fragmentation of Legal Texts and its Impact on Reducing the Correct Meaning

This research aims to show the real function of semantics and their effect in deducing meanings from texts. The origin of texts is semantic integration between them, and each text is complementary to the other, to reach the correct acceptable meaning. It is a process of linking obtained through multiple perspectives in which the meanings of the texts are combined together to be one product. This matter saves the texts from opposition and conflict between them. This appears clearly from following the methods of inference and deduction and the controls of understanding the texts, as they all urge the mujtahid and the reader of the legal texts to link between them and not to extract from the texts a meaning that suits him, because in that there is potential to distort the text.

IOT Based Perception Assistive Device for Visually Impaired Using AI

This document The goal of visual implants is to create artificial vision that can partially restore function. It can enhance the quality of life for visually challenged individuals by allowing them to experience light, even after years of darkness, by the use of 60 microelectrodes implanted in the retina. The artificial vision that is made possible by current visual system stimulators has very low resolution because of their small number of microelectrodes. Numerous researchers have sought to enhance artificial vision produced by low-resolution implants through the application of machine learning and image processing techniques. Because phosphine images have low resolution, users report unhappiness with the Retinal Prosthesis System. This underscores the important need for targeted research aimed at improving visual clarity and user pleasure in general. This study presents a novel system that uses voice-based help and real-time dynamic obstacle detection to improve the freedom and safety of blind people when navigating. The system creates a comprehensive and context-aware indoor environment by combining RFID-based pre-implemented tags with sophisticated indoor mapping and semantic mapping technologies. This system's indoor map editor converts building structures into a digital format by parsing geometric information from architectural models. Semantic mapping uses this digital representation as its foundation and is enhanced with RFID-based tags that offer crucial contextual data such room names, exit points, and landmarks. By guaranteeing that the visually impaired user has access to pertinent spatial data, this semantic mapping improves interior navigation and orientation for the visually impaired user. The system's capacity to detect dynamic obstacles in real-time is one of its primary features. The system continuously scans the environment to identify moving items or obstacles in the user's path using a combination of sensors, cameras, and machine learning algorithms. The technology ensures the user's safety during navigation by detecting objects and instantly adjusting the intended navigation path to avoid collisions. The system incorporates voice-based help to offer simple and easy-to-use interface. Key Words: Visual Implants, Artificial Vision, RFID-Based Tags, Geometric Information, Voice-Based Assistance.

Specific Features of Cycle Interpretation in Global and Kazakh Piano Music for Children

The article focuses on piano cycles for children as a distinctive area of compositional creativity, characterized by its alignment with children’s perception and performance abilities. The relevance of this topic stems from the significant role and prevalence of such works in concert and pedagogical practice, alongside the lack of specialized theoretical studies examining composers’ approaches to children’s cycles within a broad cultural and historical framework. The study examines both well-known piano cycles for children by Robert Schumann, Pyotr Tchaikovsky, Franz Liszt, and Sofiya Gubaidulina, as well as lesser-studied works by Kazakhstani composers (Boris Yerzakovich, Gaziza Zhubanova, Aida Issakova, Zholan Dastenov, Bakhtiyar Amanzhol, Beibit Daldenbay, Balnur Qydyrbek, Lyazzat Zhumanova, Dmitry Ostankovich and others). The authors analyze the interpretation of children’s piano cycles using musicological and historical approaches, as well as comparative analysis, which highlight key artistic and structural features of these works. A historical and cultural review of the phenomenon from the Baroque period to the present reveals the pivotal role of Romanticism in the development of the children’s piano cycle. During this era, works with distinctive authorial characteristics emerged, which the authors define as “author’s cycles.” These cycles are marked by a unifying idea, internal semantic connections between parts, and a tendency toward genre reinterpretation. The study demonstrates that the musical practices of the 20th and early 21st centuries have further developed these Romantic traditions, offering diverse “author’s” visions of the children’s piano cycle as a conceptual whole. At the same time, modern piano music for children has undergone significant changes, incorporating expanded compositional techniques and new semantic functions introduced by composers. These developments reflect a shift in the cycle interpretation, which now often transcend their didactic purpose to address broader educational goals, transforming into a tool for the cultural and intellectual development of younger generations. The findings emphasize the universality, multifunctionality, and remarkable diversity of children’s piano cycles. To deepen the theoretical understanding of this phenomenon and enrich concert and pedagogical repertoires with fresh and engaging works, the authors advocate for further study of this genre, including the exploration of new compositions from various national cultures.

The ‘Spiritual’ and the ‘Religious’ in the Twittersphere: A Topic Model and Semantic Map

Abstract One of the principal challenges of religious studies is the lack of a widely accepted normative definition of the terms ‘religion’ and ‘spirituality.’ Responding to recent calls for methodological innovation in this field, in this paper we present a “bottom-up” approach to outlining these concepts based on an interdisciplinary, usage-based approach, which combines qualitative analysis with methods of distributional semantics. Based on the Firthian principle that a word may be known “by the company it keeps,” we used a convenience sample of 138,000 Tweets gathered between March and April 2021 to calculate seven clusters of terms related to ‘spirituality’ and ‘religion.’ Three authors then coded these clusters to propose a tentative name for each. An unsupervised sentiment analysis was conducted and presented on a concept map. We suggest that the Twitter (now ‘X’) data at hand echoes the popular perception of ‘spirituality’ as a positively connoted term, which contrasts with a negatively connoted ‘religion.’ However, the data may be interpreted to arrive at more nuanced and, at times, contradictory interpretations.

Implementing FAIR Semantic Mappings Leveraging on RO-Crate

Implementing FAIR Semantic Mappings Leveraging on RO-Crate

Incorporating Lexical Semantics in Vocabulary Teaching: Effects and Student Perceptions in Language Acquisition

This study adopts a mixed-methods approach to explore the integration of lexical semantics in vocabulary instruction at Thang Long University. Using pre- and post-tests, Likert-scale surveys, semi-structured interviews, focus groups, and classroom observations, the study examines the impact of teaching vocabulary through semantic relationships on students’ language acquisition. Results indicate a significant improvement in vocabulary retention and comprehension, with mean test scores increasing from 2.39 (pre-test) to 4.04 (post-test). Both students and teachers viewed semantic-based instruction favourably, particularly for its use of contextualized learning and semantic mapping. However, lower-proficiency learners faced challenges in grasping abstract semantic relationships. The study suggests that scaffolding techniques and context-driven learning can enhance the effectiveness of lexical semantics in vocabulary teaching, leading to more meaningful language acquisition.

Phonation in Textualization

Abstract This article argues for ancient Greek particles representing sounds and functioning as aural signs. Greek particles are susceptible to considerable phonetic reduction and do not have a stable thesis in prosody; as such, they are phonation substance, much like syllables and affixes, without much autonomous, lexical or nuancing (poly)semantic function, let alone independent semantics. Usage of lexemes as particles is the direct result of phonetic reduction, itself the effect of intonational variance. As phonation, particles extend the phonetic word’s intonational rise and slope, and facilitate, as do other morphemes, the cohesion and demarcation of rhythmical unities. Particles are thus written representations of phonemes, of sound, and their prosodic versatility makes them exceptionally suitable for emphasis. Their preservation in writing reflects ancient Greek’s idiosyncrasies as a spoken language.

A comparison between preschool children’s verbal participation during the shared reading of a wordless and a text-based rendition of Aesop’s fable The Lion and the Mouse

ABSTRACT This study compared the story-focused language of two groups of 40 Chinese preschool children who separately read and discussed a wordless and a text-based version of one Aesop’s fable with their educators. Drawing on systemic functional linguistic theory, the children’s language was analyzed according to their semantic function: label, describe, infer, compare, generalize and explain. The results showed that 3–4-year-olds generated fewer story-focused clauses than 4–5-year-olds in both book reading genres, yet the gap was much narrower in wordless booking reading where the younger children were more engaged in story-focused discussion. The younger children used more ‘infer’ and ‘explain’ clauses during wordless book reading, whereas the older children used more ‘describe’ clauses. The situation was reversed for the children reading the text-based book. These findings have implications for educators to optimize the learning opportunities afforded to young children during shared reading, considering the influence of book types on different age groups.

Semantic information-based attention mapping network for few-shot knowledge graph completion.

Semantic information-based attention mapping network for few-shot knowledge graph completion.

A semantic and ontology-based framework for enhancing interoperability and automation in IoT systems

The ever-growing and increasing diversity of IoT ecosystems, the seamless interoperability, scale, and near real-time automation have remained grand challenges. This paper presents a solution enterprise semantic and ontology-based framework to mitigate against these challenges through distributed edge cloud architecture with semantic mapping and ontology models. It seamlessly handles data coordination, protocol reconciliation, and interaction between dynamic devices within heterogeneous IoT environments. In this research paper, work simulation covered 100–500 IoT devices communicating using various protocols, such as MQTT, HTTP, and CoAP. The interoperability success rate of the proposed framework reached up to 98%, and the latency was reduced by 65% through edge processing while keeping the system efficiency at 85% with 500 devices, which proves that the proposed research novel framework is scalable and robust enough for real-world use cases. On top of this, The key research output and innovations involve ontology-based semantic mapping, machine learning models to interpret data, and practical resource usage across edge and cloud layers. The research paper findings reveal real-world applications of the proposed framework for smart cities, healthcare, and industrial IoT. Our ongoing work addresses the integration of AI-based (dynamic) ontologies and applying the framework in real-world deployments for more scalable and adaptable scenarios.

Effects of input modality and second-language vocabulary proficiency on processing of Japanese compound verbs

This paper investigates how input modality and second-language (L2) vocabulary proficiency affect the processing of different types of Japanese compound verbs. In this study, 50 graduate students from Japanese language programs at a Chinese university were placed in the visual group (N = 25) or auditory group (N = 25), and were asked to judge whether the presented Japanese compound verbs and pseudo-words were actual Japanese words or not. The compound verbs and pseudo-words were randomly presented visually or auditorily. Our findings highlighted several key points. First, the headedness effect in the processing of Japanese compound verbs emphasised the importance of the second verb in the compound, and this effect was modulated by both input modality and L2 vocabulary proficiency. Second, when compound verbs were presented in isolation, input modality and vocabulary level independently affected the processing of different types of compound verbs. Third, upon visual presentation, the processing of different types of compound verbs tended to shift from decomposed to holistic processing depending on the absence of grammatical and semantic functions in the first or second verbs; conversely, upon auditory presentation, a tendency toward decomposed processing prevailed across all types of compound verbs. Fourth, lower L2 vocabulary proficiency resulted in more pronounced differences between decomposed and holistic processing across various compound verb types; in contrast, higher L2 vocabulary proficiency favoured holistic processing. These observations contribute to a deeper understanding of the cognitive processing involved in L2 acquisition and have implications for both teaching and further research in this area.

Enhancing SLAM algorithm with Top-K optimization and semantic descriptors

To address the computational challenges faced by edge devices using deep learning to process LiDAR point cloud data, this paper proposes a SLAM algorithm incorporating Top-K optimization to generate semantic descriptors and global semantic map for laser data efficiently. This approach aims to reduce computational complexity while enhancing processing speed. The algorithm extracts semantic information from LiDAR data, constructs two-dimensional semantic descriptors, and improves the robot’s semantic understanding of its surrounding environment. In the loop closure detection phase, the algorithm identifies loop candidates by calculating the geometric and semantic similarities of the descriptors. It utilizes front-end odometry to stitch together subgraphs from these loop candidates, thereby detecting true loop closures. Finally, true loop closures add constraints in the factor graph, facilitating pose optimization. Experimental results show that this descriptor can match more loop closures without affecting accuracy. The algorithm enhances the pose estimation accuracy of the robot and generates global point cloud maps rich in semantic information. Under the influence of the Top-K strategy, the average inference time is reduced by 10.7%, and the memory usage decreases by 19.5% compared with before in the Network Inference module. This Top-K strategy significantly conserves computational resources for optimizing edge-device deep learning algorithms, particularly when processing LiDAR point cloud data. Additionally, it effectively reduces the computational load in practical applications while maintaining inference accuracy and efficiency.

The Association Between Metalinguistic Awareness and Chinese Word Reading: A Three‐Level Meta‐Analysis

AbstractThis study involved a three‐level meta‐analysis on the correlations between metalinguistic awareness (i.e., orthographic, phonological, and morphological awareness) and Chinese word reading. Based on 16,823 individuals from 81 studies, the results revealed moderate associations between all three metalinguistic skills and Chinese word reading. Additionally, location, grade, and measurement type moderated these relationships. Orthographic awareness showed stronger associations with Chinese word reading in preschool than in other grades, among participants from Taiwan than those from mainland China, and when measurement involved semantic radical function awareness than when it involved form and phonetic radical function awareness. Additionally, syllable‐level phonological awareness showed stronger correlations than other levels only in preschool, and the associations declined after low primary. Subsyllable awareness showed the strongest association in low primary. Furthermore, word reading measurement moderated its association with phonological and morphological awareness. Phonological awareness correlated more with word reading accuracy than fluency, and morphological awareness correlated more with multicharacter than single‐character word reading accuracy.

Weakly-supervised thyroid ultrasound segmentation: Leveraging multi-scale consistency, contextual features, and bounding box supervision for accurate target delineation.

Weakly-supervised thyroid ultrasound segmentation: Leveraging multi-scale consistency, contextual features, and bounding box supervision for accurate target delineation.

Semantic-based defense mechanism for ai model networks using rich semantic identifier mapping

Semantic-based defense mechanism for ai model networks using rich semantic identifier mapping

LCFFNet: A Lightweight Cross-scale Feature Fusion Network for human pose estimation

LCFFNet: A Lightweight Cross-scale Feature Fusion Network for human pose estimation

A land cover change framework analyzing wildfire-affected areas in bitemporal PRISMA hyperspectral images

A land cover change framework analyzing wildfire-affected areas in bitemporal PRISMA hyperspectral images

The Conjunction of the Conditional Adverbial (Lammā) With the Letters (Fāʾ) and (Wāw) in Surat Yusuf: A Novel Functional Interpretation

Objectives: study aims to reveal conditional adverbial function of (Lammā) when it’s combined with (Fāʾ) and (Wāw), as it has taken Surah Yusuf as a subject for applied study, since the surah is characterized by large number of events that can be summed up, the context of the situation contributed to not mentioning them, in order that the recipient could appreciate them without difficulty. Methods: The study adopts modern functional method, as its scholars have researched linguistic tools in Arabic and contemplated semantic function, analyzed the sentence and looked closely at its inflection, clarified its meanings, explained apparent formal connections and showed its implicit order in its coherence and the importance that tools add to constructing sentences and conveying meanings. Results: template model contributes to the analysis of linguistic expression of vocabulary. It allocates elements and gives the concept breadth, it takes in consideration internal and external components of sentence. The conditional sentence carries the meaning of the penalty between the conditional verb and its answer, and the letter (Lammā) gives when it is combined with the (Wāw) a broad significance in time. Conclusions: When (Lammā) is combined with (Wāw), time becomes extended to accommodate number of inter-events that occur between conditional verb and answer, and most likely the recipient interprets them and the picture is complete for him. When (Lammā) is combined with the (Fāʾ), time becomes accelerated, the two verbs in the conditional sentence follow each other.

On the Execution and Runtime Verification of UML Activity Diagrams

The unified modelling language (UML) is an industrial de facto standard for system modelling. It consists of a set of graphical notations (also known as diagrams) and has been used widely in many industrial applications. Although the graphical nature of UML is appealing to system developers, the official documentation of UML does not provide formal semantics for UML diagrams. This makes UML unsuitable for formal verification and, therefore, limited when it comes to the development of safety/security-critical systems where faults can cause damage to people, properties, or the environment. The UML activity diagram is an important UML graphical notation, which is effective in modelling the dynamic aspects of a system. This paper proposes a formal semantics for UML activity diagrams based on the calculus of context-aware ambients (CCA). An algorithm (semantic function) is proposed that maps any activity diagram onto a process in CCA, which describes the behaviours of the UML activity diagram. This process can then be executed and formally verified using the CCA simulation tool ccaPL and the CCA runtime verification tool ccaRV. Hence, design flaws can be detected and fixed early during the system development lifecycle. The pragmatics of the proposed approach are demonstrated using a case study in e-commerce.

Perceptual and semantic maps in individual humans share structural features that predict creative abilities

Building perceptual and associative links between internal representations is a fundamental neural process, allowing individuals to structure their knowledge about the world and combine it to enable efficient and creative behavior. In this context, the representational similarity between pairs of represented entities is thought to reflect their associative linkage at different levels of sensory processing, ranging from lower-order perceptual levels up to higher-order semantic levels. While recently specific structural features of semantic representational maps were linked with creative abilities of individual humans, it remains unclear if these features are also shared on lower level, perceptual maps. Here, we address this question by presenting 148 human participants with psychophysical scaling tasks, using two sets of independent and qualitatively distinct stimuli, to probe representational map structures in the lower-order auditory and the higher-order semantic domain. We quantify individual representational features with graph-theoretical measures and demonstrate a robust correlation of representational structures in the perceptual auditory and semantic modality. We delineate these shared representational features to predict multiple verbal standard measures of creativity, observing that both, semantic and auditory features, reflect creative abilities. Our findings indicate that the general, modality-overarching representational geometry of an individual is a relevant underpinning of creative thought.