Semantic View Research Articles

CATransformer: A Cycle-Aware Transformer for High-Fidelity ECG Generation From PPG.

Electrocardiography (ECG) is the gold standard for monitoring heart function and is crucial for preventing the worsening of cardiovascular diseases (CVDs). However, the inconvenience of ECG acquisition poses challenges for long-term continuous monitoring. Consequently, researchers have explored non-invasive and easily accessible photoplethysmography (PPG) as an alternative, converting it into ECG. Previous studies have focused on peaks or simple mapping to generate ECG, ignoring the inherent periodicity of cardiovascular signals. This results in an inability to accurately extract physiological information during the cycle, thus compromising the generated ECG signals' clinical utility. To this end, we introduce a novel PPG-to-ECG translation model called CATransformer, capable of adaptive modeling based on the cardiac cycle. Specifically, CATransformer automatically extracts the cycle using a cycle-aware module and creates multiple semantic views of the cardiac cycle. It leverages a transformer to capture detailed features within each cycle and the dynamics across cycles. Our method outperforms existing approaches, exhibiting the lowest RMSE across five paired PPG-ECG databases. Additionally, extensive experiments are conducted on four cardiovascular-related tasks to assess the clinical utility of the generated ECG, achieving consistent state-of-the-art performance. Experimental results confirm that CATransformer generates highly faithful ECG signals while preserving their physiological characteristics.

Knowledge-driven hierarchical intents modeling for recommendation

Previous studies on user–item interaction graphs have typically concentrated on simple interactions, often overlooking the significant role of user intent in shaping these interactions. While some recent research has explored intent relationships to enhance modeling, these approaches mainly focus on user preferences derived from interactions, ignoring the knowledge information with good interpretation in knowledge graphs. In addition, some recent work usually utilize undenoised graph structure information to learn the node representations, which introduces plenty of noise and impedes the well learning of users’ preference. In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel knowledge-driven hierarchical intent modeling framework called KHIM. The focus is on designing a hierarchical user intent modeling process and an intent-based multi-view contrastive learning mechanism. The former extracts both the popular and personalized preferences of users from attribute tuples within the knowledge graph at the global-level and local-level, respectively. For global level, we capture the user’s true intents from positive view and augment the positive intent representation with negative view. While the latter generates high-quality user and item representations through multi-level cross-view contrastive learning. Two data augmentation strategies are designed during the contrastive process to mitigate the effects of noise in the learning process. Additionally, we also designed a neighbor filtering strategy based on semantic view to obtain more neighbor semantic information of user and item nodes, so as to further improve the recommendation performance. Experimental results on three benchmark datasets demonstrate that KHIM significantly outperforms various state-of-the-art approaches, highlighting its effectiveness in leveraging knowledge graph information for better recommendations.

Information-flow interfaces

AbstractContract-based design is a promising methodology for taming the complexity of developing sophisticated systems. A formal contract distinguishes between assumptions, which are constraints that the designer of a component puts on the environments in which the component can be used safely, and guarantees, which are promises that the designer asks from the team that implements the component. A theory of formal contracts can be formalized as an interface theory, which supports the composition and refinement of both assumptions and guarantees. Although there is a rich landscape of contract-based design methods that address functional and extra-functional properties, we present the first interface theory designed to ensure system-wide security properties. Our framework provides a refinement relation and a composition operation that support both incremental design and independent implementability. We develop our theory for both stateless and stateful interfaces. Additionally, we introduce information-flow contracts where assumptions and guarantees are sets of flow relations. We use these contracts to illustrate how to enrich information-flow interfaces with a semantic view. We illustrate the applicability of our framework with two examples inspired by the automotive domain.

Meaning is Use: the Case of Propositional Identity

We study natural deduction systems for a fragment of intuitionistic logic with propositional identity from the point of view of proof-theoretic semantics. We argue that the identity connective is a natural operator to be treated under the elimination rules as basic approach.

Nonverbal components of communication in dialogues as a marker of character relationships in the work by A. P. Chekhov "Three Years"

Communication, which plays an integral role in human life, is reflected in a work of art mainly in the form of dialogue. With the help of dialogue, the author develops events, forms images of characters, and conveys the idea of the work. The subject of this study is the non-verbal components of communication included in the remarks to the replicas of Alexei Laptev, a character in the story by A.P. Chekhov's "Three Years". The purpose of the work is to determine ways of introducing non-verbal components of communication into the author’s comments accompanying Laptev’s speech when communicating with Belavina, to describe the remarks from the point of view of structure and semantics and to trace how nonverbal components reflect changes in the characters’ relationships. The relevance of the study is due to the fact that there are not many works on the study of the content and structure of remarks to Alexei Laptev’s replicas and the semantic analysis of the linguistic units included in the remarks, representing non-verbal components. The following results were obtained: 1) the methods of introducing non-verbal components of communication and the means of their expression were analyzed, 2) a constant non-linguistic characteristic of Laptev’s communicative behavior was determined – a change in voice volume and clarity of pronunciation, 3) the position of remarks in relation to the character’s replicas was identified – the author’s comments are prepositive and interpositive, 4) a number of verbs are proposed that capture changes in Laptev’s inner world and, as a consequence, the transformation of his attitude towards his wife. In the process of conducting the study we used the method of contextual analysis of a text fragment, semantic and structural analysis of language units.

Review-Enhanced Hierarchical Contrastive Learning for Recommendation

Designed to establish potential relations and distill high-order representations, graph-based recommendation systems continue to reveal promising results by jointly modeling ratings and reviews. However, existing studies capture simple review relations, failing to (1) completely explore hidden connections between users (or items), (2) filter out redundant information derived from reviews, and (3) model the behavioral association between rating and review interactions. To address these challenges, we propose a review-enhanced hierarchical contrastive learning, namely ReHCL. First, ReHCL constructs topic and semantic graphs to fully mine review relations from different views. Moreover, a cross-view graph contrastive learning is used to achieve enhancement of node representations and extract useful review knowledge. Meanwhile, we design a neighbor-based positive sampling to capture the graph-structured similarity between topic and semantic views, further performing efficient contrast and reducing redundant noise. Next, we propose a cross-modal contrastive learning to match the rating and review representations, by exploring the association between ratings and reviews. Lastly, these two contrastive learning modes form a hierarchical contrastive learning task, which is applied to enhance the final recommendation task. Extensive experiments verify the superiority of ReHCL compared with state-of-the-arts.

A Graph Contrastive Learning Model Based on Structural and Semantic View for HIN Recommendation

With the rapid growth of information in the Internet era, people are in great need of recommendation methods to filter information. At present, recommendation methods which based on heterogeneous information network (HIN) have attracted wide attention. Recently, HIN-based recommendation methods need to be modeled from two aspects: node structural association and semantic association. To this end, we propose a graph contrastive learning model based on structural and semantic view for HIN recommendation (GCL-SS). GCL-SS utilizes U-I interactive view to obtain node structural embeddings, and utilizes U-I semantic view to obtain node semantic embeddings. Based on these two kinds of embeddings, we establish a self-supervised contrastive learning mechanism to effectively integrate structural information and semantic information of user (item) nodes in HIN, and finally learn a more discriminative user (item) embedding. In addition, in order to strengthen the semantic association between nodes, we innovatively utilize time sequence encoder (TSE), such as LSTM, to encode semantic homogeneous network decomposed by HIN in U-I semantic view. At last, based on the user and item embeddings, we adopt bilinear decoder to model the potential association between user and item, so as to realize rating prediction of user to item. The experimental results on three real datasets confirm that our GCL-SS model performs better than state-of-the-art recommendation methods in rating prediction task. In addition, the results of four ablation experiments indicate that our GCL-SS model can effectively improve the performance of rating prediction in recommendation.

Verbal Humor in American Situational Comedy the Office: Semantics View

The semantic analysis attempts to define and completely comprehend the word’s meaning. The purpose of this study was to delve into the types of lexical relations in verbal humor and investigate the humor styles of the American sitcom The Office. This study used a qualitative design to collect data on verbal humor. The findings revealed that The Office frequently uses homonym, polysemy, hyponym, antonym and synonym in the construction of humor and potentially creates a humorous effect. Additionally, the humor style of the sitcom is sarcastic humor. It can be concluded that linguistically the use of homonym, polysemy, hyponym, antonym and synonym in the punchline provides an amusing effect and generates a comical situation. On the other hand, it also creates an ambiguity effect which results in a double interpretation of certain jokes due to the multiple meanings of a single word.

Design and Implementation of IoT-Based Weather Monitoring System

Due to advancement in technology, various fields have boosted the development of systems that improve people’s life quality, contributing to the welfare of the community by providing relevant and pertinent information for decision-making. On the Internet of Things (IoT), the systems demand measuring and monitoring several environmental variables. The heterogeneity of the captured data and the measuring instruments used to hinder the interoperability among the different components of the IoT. The problems are raised an interest in the development of methods and tools that support the heterogeneity of the data from the sensors, the measurements, and the measuring devices. Some existing tools have resolved some of these interoperability problems. However, it forces to IoT developers to use sensors from specific brands, limiting their generalized use in the community. Furthermore, it is required to solve the challenge of integrating different protocols in a same IoT project. Besides, by generating alerts, it may help making decisions daily, considering the data provided by the sensors. it is required to solve the challenge of integrating different protocols in a same IoT project. To overcome the limitations of the existing glitches, there is need to develop a framework based on network of sensors via software that allows communication-using protocols in a specific environment to monitor the quality of air and to alarm users about this. In this paper, a prototype of proposal is mentioned about the architecture, list of hardware, software and different APIs are utilized to gather data in a systematic way so as users can visualize data in a semantic view. The visualization is shown later by using Matplotlib, Seaborn tools of Machine Learning (ML) and Deep Learning (DL) to plot the temperature along with humidity in a historical span. The result shows that accuracy obtained via Machine Learning Classifier is 87% in the context of Weather Prediction.

Композиционно-структурные, семантические и пресуппозиционально-прагматические параметры и дефекты сгенерированных коротких текстов в языковой нейросети GigaChat

Introduction. The relevance of the study lies primarily in the fact that the increasingly active appeal of the widest circles of users to the generation of texts of different genres, properties and volumes using the so-called LLM (Large Language Model) creates the need to study the actual linguistic capabilities of these generative models, including the need for linguistic research of the texts they generate. It is also important that a large number of specialists from different fields actively use the generative capabilities of linguistic neural networks for purely professional purposes, which means that the quality of generated texts has acquired the status of a production factor and directly affects success in professional activities, which also indicates the relevance of our research. Materials and Methods. The texts of short stories were chosen as the object of study, since in a certain respect, such texts can be considered as archetypal structures that underlie texts of certain functional types (scripts, advertising texts and stories). Analysis. This state of affairs requires studying the generated stories, including in the aspect of the representation of frame structures in it, since this will allow us to get an idea of how texts of this type are structured from the point of view of standard linguistic semantics, including syntactic semantics. Results. It has been established that, despite all grammatical, structural-semantic and compositional adequacy, the texts of short stories generated in GigaChatPro, created by the network on a certain topic or according to a certain frame, may contain violations in global semantic organization, generated by both presuppositional-pragmatic violations and violations in text reference. Violations in the general functional pragmatics of the generated stories were also noted: pronounced edification and the length of sentences up to 5-7 words are characteristic of stories for children of primary school age, despite the fact that the themes of these stories initially do not correspond to their age interests.

Empirical Network Structure of Malicious Programs

A modern binary executable is a composition of various types of networks. Control flow graphs are a commonly used representation of an executable program used for classification tasks. Control flow and term frequency representations are widely adopted, but provide only a partial view of program semantics and present challenges to increases in resolution. By performing a quantitative analysis of program networks, we enable the identification of patterns within these features that are correlated to structure. This allows for increases in feature resolution and pattern recognition in classification tasks. These are necessary steps in order to obtain greater explainability in classification results. We demonstrate the presence of Scale-Free properties of network structure for program data dependency and control flow graphs, and show that data dependency graphs also have Small-World structural properties. We show that program data dependency graphs have a degree correlation that is structurally disassortative, and that control flow graphs have a neutral degree assortativity, indicating the use of random graphs to model the structural properties of program control flow graphs would show increased accuracy. An increase in feature resolution allows for the structural

Neosemantization in “Joke dictionaries”

The article examines the phenomenon of secondary nomination (neosemantization) in “Joke Dictionaries”. Such words are most common on the pages of humor in periodicals. The basis for the study of the phenomenon was the materials of the column of the same name in the magazine “Perets”, the newspapers “Silski Visti”, “Literary Ukraine” and others. Some of the neosemanticisms are also published by humorist writers in the form of books or presented on specialized literary portals. The purpose of this research is to investigate neosemantization in “Joke Dictionaries”, hence the subject of research is the author’s neosemantisms with a humorous basis. Such words pose a problem for scientific understanding, since they did not attract the attention of linguists before. The most expedient, and therefore the main method, is descriptive. The novelty of the article is that for the first time neosemantisms are described and an attempt is made to classify them by meaning. In the results of the study, it was found that words most often acquire new meanings with the help of neometaphorization and paronomasia, as well as the decomposition of a simple word into two consonant roots. However, a certain number of such innovations can be considered not too successful from the point of view of semantics, when the author looks for seemingly consonant words, but this consonance is not enough to create a successful new value. An interesting way of creating such lexical units is to interpret words of foreign origin as composites or abbreviations with paronymic or homonymous meaning. The question of the connection between the meanings of neosemanticisms and jargonisms (slangisms) remains open. The results of the study show that the authors explain the same word in different ways: in some cases the new meanings are identical or close, in others they are different depending on the author’s vision. In some cases, the author may interpret the word in different ways, publishing these meanings in different editions or releases; because of this, the issue of plagiarism is important, although it can be argued that the absolute majority of coincidences (if not all) are accidental. Sometimes the formed lexemes are characterized by enantiosemy. Since this section is quite regular in the latest editions of these publications, in the future it is possible to study more words in greater detail and compile a dictionary of humorous neosemanticisms.

Mansi Lexical Materials in the Manuscript Heritage of the Russian Academician Gottlieb Siegfried Bayer (1694–1738)

This article analyses the Mansi vocabulary from the handwritten Latin-Tatar-Vogul dictionary kept in Glasgow (Great Britain), in the archive of the Russian academician Gottlieb Siegfried Bayer (1694–1738), which has never been done before. Bayer’s scholarly heritage is of great interest and has not been sufficiently studied yet. The author manages to establish that Bayer’s dictionary is a partial list of lexical materials compiled by another Russian academician, Gerhard Friedrich Mьller (1705–1783), Head of the Academic Unit of the Second Kamchatka (Great Northern) Expedition (1733–1743). It is also established that these Mansi (Vogul) materials were recorded by Müller in Tobolsk in 1734 from a native speaker of the Pelym dialect and preserved in Mьller’s archive in the Russian State Archive of Ancient Acts (RGADA) in Moscow. A comparison of Bayer’s and Mьller’s dictionaries shows that Bayer copied only the most interesting lexemes from Müller — 90 Vogul words, while Müller has about 300 of them. Most of the words copied by Bayer are reflected in lexicographic sources, which makes it possible to trace their correspondences in Mansi dialects, as well as to identify lexical parallels. The article considers several lexemes that are of the greatest interest from the point of view of semantics and word formation. More particularly, the name of the kopeck uses the designation of a ‘squirrel’ as a monetary unit in Siberia, hence ‘rouble’ (Schдtlin) is ‘one hundred kopecks’, or ‘one hundred squirrels’; in the name оf paper, the Mansi ‘birch bark’ (-schasch) is added to the word of Komi origin ‘paper’ (nepag). Additionally, the author reveals lexemes that are not marked in dictionaries or have other semantics, i.e. the name of the ‘water spirit’ (Uнtkas) is used to denote ‘the devil’; the adverb ‘above’ (Nùmna) is used to denote ‘the sky’; the month as a period of time is denoted by ‘four weeks’ (Nille-Sat); ‘the cloudberry colour’ (Morochoschp) juxtaposition is used to denote ‘yellow’. Finally, the article mentions some inaccuracies made by Bayer when copying Mьller’s dictionary.

MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA-Disease Association.

A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA.

The connection of mathematics and logic in the structure of axiomatized and formalized theories

The aim of the research is to study the relationship between logic and mathematics in the structure of axiomatized and formalized scientific theories. The object of the study is the explication of this connection and its explanation. The subject of the study is syntactic and semantic views on the structure of scientific theories, the relationship between logic and mathematics has not been studied in detail in them. In the syntactic view, the structure of the theory is understood as a linguistic construct build from various logical propositions of the theoretical level, correspondence propositions and observation propositions. The structure of the theory does not take into account the variety of model representations of the theory that generate a variety of language constructs. The semantic view overcomes this disadvantage, and in it the structure of the theory is presented as a hierarchy of models: from axioms to theoretical-level models, experimental models and data models. The structure of the theory, the connection of logic and mathematics were studied using comparative analysis, methods of interpretive analysis and reconstruction of scientific theories. The methods made it possible to explicate mathematical concepts in the structure of the theory and correlate them with logic and natural language. Comparative analysis has shown that in the syntactic view, the connection between logic and mathematics lies in the fact that mathematical concepts of physics are interpreted in the language of logic of first-order predicates with equality. The connection between mathematical concepts is provided by the axiomatic method, which serves as a means of formalizing concepts. Mathematics comes down to logic. In the semantic approach, in order to identify the connection between mathematics and logic, it was necessary to reconstruct the structure of non-relativistic quantum mechanics. With the help of the set-theoretic predicate of Suppes, its axioms were determined, the connection between mathematical structures, postulates of the theory, axioms, and observable quantities was established. Logic and mathematics are related to each other in such a way that metamathematics or linguistics is a part of mathematics. Mathematics includes set theory and model theory, i.e. mathematical logic. The connection of mathematical formalisms with phenomena and with natural language remains problematic, and there is this drawback in the syntactic approach. The novelty lies in the fact that the research contributes to the methodology and logic of science, to the explanation of the connection between logic and mathematics in scientific theory, which was illustrated by various examples from various fields of physics.

The critical dimension of Brandom’s normative pragmatism

For all of Brandom’s self-professed allegiance to Hegel, there is something perplexing about his fixation on semantic and epistemological issues at the expense of the type of social and political considerations that are at the heart of Hegel’s system. However, and although Brandom himself concedes that his work is circumscribed to a number of highly specialized and technical issues in the philosophy of mind and language, the truth is that his views often radiate to other philosophical fields, if not always explicitly. My claim in this article is that at the heart of Brandom’s semantic theory, there are elements of a critical project, one that offers a normative standpoint to judge and improve our current practices. Moreover, these progressive features of Brandom’s normative pragmatics should be seen in the light of his adoption of a series of hermeneutic themes, ultimately culminating in his recollective conception of rationality and his edifying view of semantics.

Pose-guided self and external attention feature matching and aggregation network for person re-identification

Traditional methods that use pose information for person re-identification (ReID) often focus only on relationships between different parts of the same sample and ignore the correlation between poses of different samples, which hinders further improvement in recognition accuracy. In view of this, this paper proposes a pose-guided self and external attention feature matching and aggregation net which contains a visual context self and external attention module and a pose-guided feature matching and aggregation module. It can divide global features into local features without strict spatial feature alignment, and enables the model to focus on pose correlation between different samples. Thus, it not only learns pose-related identity identification features, but also avoids interference from the samples with large pose changes. The visual context self and external attention module realizes feature embedding through a transformer-based image classification model ViT and a human pose estimation model. Also, it extracts encoder features and local features containing rich pose information by adding an external attention. The pose-guided feature matching and aggregation module obtains a learnable part semantic view through a transformer decoder and pose heat maps. Then, it matches and aggregates with local features, adaptively learns pose-related features, and enhances the robustness of the model to background and occlusion. In this paper, experiments are conducted based on three data sets, including Market-1501, DukeMTMC-reID and MSMT17. The Rank-1 values are 95.3%, 90.1%, and 82.5%, respectively, and the mAP values are 88.3%, 81.1%, and 64.1%, respectively. In conclusion, our method introduces an external attention into the ReID task, and the ReID model can obtain more accurate results by extracting pose-related identity identification features and avoiding interference from samples with large pose changes.

Key factors for child participation - an empowerment model for active inclusion in participatory processes.

Child participation is advocated for in many contexts related to children, such as in education and health services, but also in everyday life settings. To facilitate children's real opportunities for participation, it is proposed that children need to be adequately empowered to liberate the child's autonomous voice and activate their powers of action. This involves an understanding of children as opinion-forming and social individuals, who play an active part in social relationships. This article aims at a theoretical exploration of empowerment in the organization of ethically justifiable participation situations with children. With a semantic view of theory, an eclectic research design has been used to search for a theoretical framework based on basic human needs to be an active agent in one's own life. The purpose is to define key factors that promote the sense of empowerment as a prerequisite for being an active participant in interaction with adults. This conceptualization aims to raise clearer guidelines for the implementation of child participation and presents an empowerment model with four dimensions for the active inclusion of children in participatory processes with adults. These dimensions are information, autonomy, recognition, and alliance.

The Development of Abstract Word Meanings.

Extensive research has shown that children's early words are learned through sensorimotor experience. Thus, early-acquired words tend to have more concrete meanings. Abstract word meanings tend to be learned later but less is known about their acquisition. We collected meaning-specific concreteness ratings and examined their relationship with age-of-acquisition data from large-scale vocabulary testing with children in grade 2 to college age. Earlier-acquired meanings were rated as more concrete while later-acquired meanings as more abstract, particularly for words typically considered to be concrete. The results suggest that sensorimotor experiences are important to early-acquired word meanings, and other experiences (e.g., linguistic) are important to later-acquired meanings, consistent with a multi-representational view of lexical semantics.

ALGEBRAIC SEMANTICS FOR RELATIVE TRUTH, AWARENESS, AND POSSIBILITY

AbstractThis paper puts forth a class of algebraic structures, relativized Boolean algebras (RBAs), that provide semantics for propositional logic in which truth/validity is only defined relative to a local domain. In particular, the join of an event and its complement need not be the top element. Nonetheless, behavior is locally governed by the laws of propositional logic. By further endowing these structures with operators—akin to the theory of modal Algebras—RBAs serve as models of modal logics in which truth is relative. In particular, modal RBAs provide semantics for various well-known awareness logics and an alternative view of possibility semantics.