Semantic System Research Articles

Yakut folk song of the central (Prilensky) region: traditions of the interaction of word and music in the Dyeretia of yry in Olonkho culture

The traditions of the interaction of word and music in the Yakut folk song of Dyeretia Yrya in Olonkho culture of the central (Prilensky) region are investigated. The study of the tradition of the interaction of word and music in the Yakut folk song is important due to the fact that it helps to understand the mechanisms in the relationship of word and music in folklore, in addition, this issue has not yet been considered as a special one in science, especially since the verse structure and their patterns in ancient poetic texts differ from modern folklore and vocal works, and their melodies are suprasemantic in nature. The song traditions of Dyeretia yrya in Olonkho were formed before the 1st century BC and many of their significant elements have survived to our time. The material of the study is the musical transcriptions of the Dyeretia Yrya Olonkho recognized by Olonkhosuts (Olonkho culture researchers). The methodology is based on theoretical developments in ethnography, philology and folk music. In this work, the author relied primarily on the method of comparative analysis. For the first time, on the basis of comparative works, it is shown that the traditions of the interaction of word and music in the Yakut folk songs of Dyereti Yrya in Olonkho culture of the central (Prilensky) region can be revealed by comparing the rhythm of the verse structure and the quantitative rhythmics of the chant, which in the future will help to reveal the deeper integrative possibilities of these two semantic systems and temporary art forms. The traditions of the interaction of word and music in the Dyeretia of the central (Prilensky) region of Yakutia are canonical and have been preserved for centuries. In the songs of Dyeretia Yrya, the melodic line develops according to the principle of "rotation in a closed circle" and has a pendulum-like character of the melodic pattern, which confirms the antiquity and patriarchy of such songs from Yakut legends. In the verse structure of the dyereti of yrya in olonkho, the tendency to equilocation and the presence of seven-syllable and six-syllable periods characteristic of Turkic versification are revealed. In the quantitative rhythm of the chant, the dominance of chants is observed to a greater extent with long durations, each sound of which is equal to short durations, expressed by eighth, sixteenth durations and trioles. The super-long ones represent the introductory part and necessarily open the melody, thereby emphasizing the beginning of the song.

A Novel RIS-Aided Optimization Strategy for Semantic Communication System

A Novel RIS-Aided Optimization Strategy for Semantic Communication System

Application of E-Learning entertainment learning based on intelligent interactive experience in English reading assistance mode

In the process of E-Learning, interactive entertainment experiences and digital technology provide new models for strengthening the development of online education. This article studies the application of E-Learning entertainment learning based on intelligent interactive experience in English reading assistance mode. The E-Learning entertainment learning mode based on intelligent interactive experience focuses on creating interest and interaction, designing an interesting and stimulating learning environment, selecting appropriate technology tools to achieve E-Learning intelligent interactive experience, and designing appropriate learning content and activities according to learning objectives. Firstly, this article designs a semantic inference system that is more convenient in calculating and storing resources, and can perform semantic inference on uncertain knowledge. Secondly, this article delves into the basic principles of Bayesian network algorithms and explores the construction mechanism of Bayesian networks. Then compared with traditional algorithms, the accuracy and stability of this method were demonstrated. Digital technology and artificial intelligence provide new technological support for E-Learning, allowing students to better experience entertaining learning experiences and customize learning content according to personal needs, enhancing educational value. Experiments have shown that using computer-assisted English reading can effectively improve students’ English proficiency. This system not only reduces the boredom in traditional English reading, but also provides students with a brand new English learning atmosphere, which can effectively improve their learning efficiency.

Монгол хэлний “дээр” хэмээх үгийн танин мэдэхүйн утга зүйн судалгаа

Cognitive linguistics regards the meaning of language as humans' subjective interpretation of the world. As men communicate with the world through their bodies, their views are self-centered. Those views are reflected in languages. Therefore, it is possible to reveal the unique way of thinking reflected in a language by analyzing the semantic system related to words. This research examines some spatial semantic characteristics of the spatial term “дээр” in the Mongolian language. For this, the study determines its fundamental meanings and schemas related to them based on the basic concepts of spatial semantics of Zlatev (2007) among the cognitive semantics. According to the frame of reference, the word “дээр” spatially indicates a level of height between two things and certain location on a thing. Due to these schematic characteristics, this word does not indicate active or directional motions but static situations.

Meet-Combination of Consequence Systems

We extend meet-combination of logics for capturing the consequences that are common to both logics. With this purpose in mind we define meet-combination of consequence systems. This notion has the advantage of accommodating different ways of presenting the semantics and the deductive calculi. We consider consequence systems generated by a matrix semantics and consequence systems generated by Hilbert calculi. The meet-combination of consequence systems generated by matrix semantics is the consequence system generated by their product. On the other hand, the meet-combination of consequence systems generated by Hilbert calculi is the consequence system generated by their interconnection. We investigate preservation of several properties. Capitalizing on these results we show that interconnection provides an axiomatization for the product. Illustrations are given for intuitionistic and modal logics, Łukasiewicz logic and some paraconsistent logics.

Techniques for Memorizing the Quran: A Comparative Study of the Memory System Tendencies of Maahad Integrasi Tahfiz Selangor (MITS) Students

Memorizing the Quran typically employs memorizers’ common techniques, which also result in manners appreciation, and the development of motivation and self-inspiration in them as well. The approach is frequently utilized as a spontaneous practice when memorizing the Quran. However, the memorizing strategies used frequently change depending on the memory system’s propensity: (a) semantics; (b) episodic; and (c) visual. This study aimed to identify and compare techniques for memorizing the Quran among the students of Maahad Integrasi Tahfiz Selangor (MITS) Klang, Selangor. MITS secondary school students from form one to form four were involved as a sample (n=192). The methodology used in this study is quantitative. A questionnaire and IBM SPSS Statistics (version 27) computer software were utilized in the instrumentation. The Friedman test and descriptive statistics were used to analyze the data. Techniques for memorizing the Quran using a semantic memory system indicated the most frequent practice (63, 32.8%) compared to the visual memory system (44, 23%) and episodic memory system (18, 9.4%). Additionally, the Friedman test found that the techniques in memorizing the Quran that use the semantic memory system (mean rank: 2.17) are the most frequently used with a significant difference χ2(2)=36.892, ρ=.000 compared to the visual memory system (mean rank: 2.02) and episodic memory system (mean rank: 1.82). This study suggests that secondary school students of MITS Klang have a tendency towards the semantic memory system in memorizing the Quran compared to the visual and episodic memory systems. Further studies related to semantic methods in hafazan can be explored, perhaps through qualitative methods as well as a wider range of studies.

Joint Optimization of Resource Allocation and Trajectory in UAV-assisted Wireless Semantic communication System

In this paper, we investigate the resource allocation design of an unmanned aerial vehicle (UAV)-enabled semantic communication system, in which UAVs are assigned to transmit semantic information to multiple user nodes. Our goal is to maximize the semantic information sum rate of the communication system by jointly optimizing the subcarrier allocation strategy and the trajectory of the UAVs, taking into account the minimum required semantic data rate for each user node, the minimum semantic similarity that can be received, the maximum cruise speed of the UAVs, and the initial/final position of the UAVs. The design is formulated as a generally tricky mixed-integer nonconvex optimization problem. Subsequently, a computationally efficient iterative algorithm is proposed to obtain a locally optimal solution, and further proofs demonstrate that the proposed algorithm is guaranteed to converge to a solution that satisfies at least the KKT condition of the original optimization problem.

Meaning as mentalization

The way we establish meaning has been a profound question not only in language research but in developmental science as well. The relation between linguistic form and content has been loosened up in recent pragmatic approaches to communication, showing that code-based models of language comprehension must be augmented by context-sensitive, pragmatic-inferential mechanisms to recover the speaker’s intended meaning. Language acquisition has traditionally been thought to involve building a mental lexicon and extracting syntactic rules from noisy linguistic input, while communicative-pragmatic inferences have also been argued to be indispensable. Recent research findings exploring the electrophysiological indicator of semantic processing, the N400, have raised serious questions about the traditional separation between semantic decoding and pragmatic inferential processes. The N400 appears to be sensitive to mentalization—the ability to attribute beliefs to social partners—already from its developmental onset. This finding raises the possibility that mentalization may not simply contribute to pragmatic inferences that enrich linguistic decoding processes but that the semantic system may be functioning in a fundamentally mentalistic manner. The present review first summarizes the key contributions of pragmatic models of communication to language comprehension. Then, it provides an overview of how communicative intentions are interpreted in developmental theories of communication, with a special emphasis on mentalization. Next, it discusses the sensitivity of infants to the information-transmitting potential of language, their ability to pick up its code-like features, and their capacity to track language comprehension of social partners using mentalization. In conclusion, I argue that the recovery of meaning during linguistic communication is not adequately modeled as a process of code-based semantic retrieval complemented by pragmatic inferences. Instead, the semantic system may establish meaning, as intended, during language comprehension and acquisition through mentalistic attribution of content to communicative partners.

Narrative analysis in individuals with Parkinson's disease following intensive voice treatment: secondary outcome variables from a randomized controlled trial.

Communication is often impaired in individuals with Parkinson's disease (PD), typically secondary to sensorimotor deficits impacting voice and speech. Language may also be diminished in PD, particularly for production and comprehension of verbs. Evidence exists that verb processing is influenced by motor system modulation suggesting that verb deficits in PD are underpinned by similarities in the neural representations of actions that span motor and semantic systems. Conversely, subtle differences in cognition in PD may explain difficulty in processing of complex syntactic forms, which increases cognitive demand and is linked to verb use. Here we investigated whether optimizing motor system support for vocal function (improving loudness) affects change in lexical semantic, syntactic, or informativeness aspects of spoken discourse. Picture description narratives were compared for 20 Control participants and 39 with PD, 19 of whom underwent Lee Silverman Voice Treatment (LSVT LOUD®). Treated PD narratives were also contrasted with those of untreated PD and Control participants at Baseline and after treatment. Controls differed significantly from the 39 PD participants for verbs per utterance, but this difference was largely driven by untreated PD participants who produced few utterances but with verbs, inflating their verbs per utterance. Given intervention, there was a significant increase in vocal loudness but no significant changes in language performance. These data do not support the hypothesis that targeting this speech motor system results in improved language production. Instead, the data provide evidence of considerable variability in measures of language production across groups, particularly in verbs per utterance.

A road semantic segmentation system for remote sensing images based on deep learning

With the rapid development of deep learning of computer science nowadays in China, many fields in academic research have experienced the powerful and efficient advantages of deep learning and have begun to integrate it with their own research. To be specific, in the field of remote sensing, the challenge of road extraction from the original images can be effectively solved by using deep learning technology. Getting a high precision in road extraction can not only help scientists to update their road map in time but also speed up the process of digitization of roads in big cities. However, until now, compared to manual road extraction, the accuracy is not high enough to meet the needs of high-precision road extraction for the deep learning model because the model cannot extract the roads exactly in complex situations such as villages. However, this study trained a new road extraction model based on UNet model by using only datasets from large cities and can get a pretty high precision in extraction for roads in big cities. Undoubtedly, this can lead to over-fitting, but its unique high accuracy ensures that the model's ability to extract roads can be well utilized under the situations of large cities, helping researchers to update road maps more conveniently and quickly in large cities.

Optimization Design of High-Dimensional Parameters MIMO Antenna in Semantic-Based Mobile Applications

To ensure the quality of semantic communication, an optimization scheme for multiple-input and multiple-output (MIMO) antenna design using in semantic-based mobile is proposed and verified. The scheme is based on a modified algorithm MOEA/D-BH, which integrates the black-hole (BH) algorithm into multi-objective EA based on decomposition (MOEA/D). By introducing controllable absorption distance and neighborhood learning mechanism, MOEA/D-BH can deal with high-dimension parameters well in a multi-objective optimization problem. Thus, in a limited design space, a satisfied antenna can be optimized by proposed scheme efficiently. This can be a feasible candidate scheme for MIMO antenna design. A single-band and dual-band MIMO antenna, which are applied for semantic-based mobile system, are optimized by proposed scheme sharing the same model. Both the data of simulation and measurement show good electrical and MIMO performances in the working frequency band. Thus not only the performance of the semantic communication definitely improved, but also it provides useful exploration for the development of intelligent semantic mobile systems.

Semantic hand gesture integration system using self-co-articulation and movement epenthesis detection

Semantic hand gesture integration system using self-co-articulation and movement epenthesis detection

Jenis dan Unsur Penentu Makna Dalam Kajian Semantik (Ad-Dilalah)

The study of the meaning of words in a particular language according to the semantic classification system is a branch of linguistics whose task is solely to examine the meaning of words, their origins, even how they developed, and the reasons for changes in meaning in the history of language. Many other fields of science are related to semantics, therefore meaning plays a role depending on the use of language as a tool for conveying mental experiences, thoughts and intentions in society. The field of semantics is limited to efforts to pay attention to and study the process of transposition of word meanings in language use. Ullman (1972) argues, if someone thinks about the meaning of a word, they simultaneously think about its reference or vice versa. The relationship between two things, meaning and words, is where meaning is born.

Lightweight convolutional neural networks with context broadcast transformer for real-time semantic segmentation

With the increasing application of embedded mobile devices in various fields, lightweight real-time semantic segmentation systems have attracted more and more attention. Many current methods have successfully reduced the model's parameters, but they have led to low model accuracy, diminishing their practical value. In recent years, the Transformer architecture has achieved good results in many tasks, effectively capturing long-range dependencies and enhancing accuracy. However, the Transformer is not adept at extracting local features, and the model's computational cost is generally too high, hindering real-time inference implementation. We propose a lightweight semantic segmentation network called LCBFormer-Net, which embeds Transformer units between asymmetric encoders and decoders to fully leverage their advantages. On the encoder side, we design the Lightweight Multi-Fusion Unit (LMFU) and Partition Grouping Shuffle Channel Attention (PGSCA). The former fully utilizes input features, merging information multiple times through multiple branches and employing depthwise convolutions with dilation rate to further obtain sufficient features. The latter includes a lightweight grouped channel attention, better guide feature extraction. The Lightweight Context Broadcast Transformer (LCB Transformer) is the Transformer unit we designed, with a lightweight structure that significantly reduces GPU memory consumption. It also improves self-attention and feed-forward networks, enhancing the model's robustness. The decoder includes the Multi-scale Semantic Information Attention Fusion (MSIAF) module, guiding the fusion of features at three different scales and employing a hybrid attention mechanism with both channel and spatial attention to guide feature extraction. LCBFormer-Net achieves good segmentation results with a parameter count of 0.88 M on multiple challenging datasets with diverse scenes.

The Archetype of Dragon in Different Mythological Systems

This article is devoted to the topic of mythological ideas of dragons in the different mythological systems. The purpose of this article is to show the semantic shades of these mythical creatures, their functions, associations with natural phenomena in the different mythological systems of West and East. The conclusions of this article are based on cult and vice versa on demonizing dragons. We are introducing new system for searching and analyzing collective and connective ideas of dragons through system of synthetic semantics.

Discovering urban block typologies in Seoul: Combining planning knowledge and unsupervised machine learning

Urban blocks are fundamental elements of urban form and play an important role in urban development. Identification of their typologies has been a long-standing challenge in urban planning and design. Traditionally, expert-based classification has been used, but recent studies have shifted towards data-driven clustering to achieve better results. However, most studies tend to use only one of these approaches and few integrate both. To address this gap, this study proposes a framework that combines planning knowledge and machine learning to identify urban block typologies in complex urban environments. The framework first classifies blocks based on their size types according to planning regulations and development types. Then, a combined hierarchical and Gaussian mixture model is used to discover clusters with complex feature data distributions. These clusters are translated into block typologies using a semantic naming system and illustrated with representative blocks. Finally, the validity of the results is tested by comparing them with other building features, such as building age. The proposed integrated framework was applied to Seoul, resulting in the discovery of 27 residential block typologies across the entire city. These typologies show significant correlations with the average building age in the blocks. The application of expert knowledge in the clustering method demonstrates higher accuracy compared to conventional clustering approaches. Overall, this framework holds significant potential for discovering urban block typologies that are consistent with planning domain knowledge and provide urban planners and designers with valuable insights into the characteristics of urban block typologies, thereby helping decision-making regarding zoning regulations, urban design, and urban development strategies.

Empiric Systems Theory – An Experimental Approach

Based on an overarching science- and humanities approach to biopsychosocial interaction, such as that desired in care, healthcare, and social care, an appropriate systems theory has been introduced. Taking interactions, not single processes, as its smallest element, it allows one to focus on the meaningful content of structured interactions, as related to their present context. The latter contains its constituting quantitative and qualitative, locally, and situationally meaning-generating aspects, in the form of the interaction partners’ role-specific aspectual qualities. Engaged in specifying systematised interaction, contributing partners are seen as providing, recruiting, and implementing information, acting as individual agencies in their relational matrix to possibly converge to systematically combined co-acting units, as systems made of systems. The systematised approach gives a more empiric view of mandatorily interacting systems and their content-as-meaning-generating potency. Empiric systems theory can be applied wherever primordially embedded agencies’ processing occurs and extended decontextualization fails or is inappropriate. Its advantage is its consideration of relational structure and corresponding perspectivity right from the beginning, which enables one to conceptualise and understand systems as containers and producers of perspectival meaning. Accordingly, the theory is a semantic systems theory as well.

Reduced false memory in the second language of Turkish-English bilinguals

In the Deese-Roediger-McDermott (DRM) paradigm, studying a list of semantically related words leads to false memory for the critical non-studied word that is related to all the words in the study list. Previous studies questioning whether bilinguals are more prone to false memory in their first language (L1) or second language (L2) in the DRM paradigm revealed mixed results. The present study investigated the same question with Turkish-English bilinguals. The revised hierarchical model proposes that the link between the lexicon and the semantic system is weaker in L2 than in L1, suggesting that false memory in the DRM paradigm that relies on semantic relatedness would be higher in L1 than in L2. Furthermore, previous studies showed that L2 is more resistant to errors in decision-making when the two languages are dissimilar, but not when they are similar, and Turkish and English are historically distant and typologically dissimilar languages. We tested Turkish-English bilingual participants whose L1 is Turkish with Turkish and English DRM word lists that had similar prior norms for generating false recognition. In the recognition test, some of the studied items and the critical non-studied items were presented and participants identified the studied items. False recognition for the critical non-studied items was lower and correct recognition for studied items was higher in L2 than in L1. The results suggest that L2 is more resistant to false memory due to its weaker lexicon and semantic system associations, at least when the two languages spoken by the bilingual are dissimilar.

DLSF: Deep learning and semantic fusion based recommendation system

An intelligent recommendation system facilitates users by recommending items based on the user’s preferences. Most of the existing recommender systems employ collaborative filtering which consider homogenous data only and does not exploit different knowledge sources. Such approaches encounter many challenges such as Cold Start problem, Sparsity, and Scalability. To overcome these setbacks, a novel deep learning & semantic fusion-based recommendation system named DLSF is proposed in this paper which collaborates multiple knowledge sources by employing three modules to generate top-N recommendations. The first module precludes sparsity by using latent representations and deep learning for understanding user-item interactions. The second module exploits semantic information provided to similar items by the users for dealing with cold start problem. And the third module works for new users and items which don’t have any prior information. A comprehensive experimental evaluation is conducted over benchmark datasets. The proposed DLSF showed on average 16.02% and 16.71% more efficient over the baselines recommendation systems taken into account in terms of MAE and RMSE respectively. DLSF has found to have significant efficiency in terms of Precision, Recall and overall accuracy.

Enhanced semantic communication schemes for speech signals

AbstractTwo new models for semantic communication systems are proposed. The first model incorporates the convolutional block attention module, which considers attention techniques in both the channel and spatial domains. The second model applies the efficient channel attention (ECA) network with reduced complexity. Experimental results demonstrate that the convolutional block attention module‐equipped model improved signal‐to‐distortion ratio performance by at a signal‐to‐noise ratio of while maintaining a similar number of parameters compared to the existing model using squeeze‐and‐excitation network. Meanwhile, the efficient channel attention‐equipped model reduced parameters by approximately without any degradation in performance compared to the existing model.