Semantic Interpretation Research Articles

A Multidimensional Approach for Formal Modeling and Analyzing Medical Cyber-Physical Systems

The combination of integrated software controlling devices, networking capabilities, and sensing/actuation technologies in Medical Cyber-Physical Systems (M-CPS) highlights some specific research challenges. The major challenge is to formally ensure the confidentiality of the data or resources they handle. This study tackles this problem by proposing a formal approach that combines CA-BRS (Control Agent and Bigraphical Reactive Systems) and BPMN (Business Process Model Notation) to specify and analyze CPS in general, while respecting several dimensions. The structural dimension of the CPS, representing the space (physical and cyber entities) in which agents exist and interact, is defined with BRS. Control agents constitute the virtual dimension and observe and control the physical and cyber entities of their environment. The complex and adaptive behavior of CPS (behavioral dimension) is defined through several types of rules, each managing a possible evolution of a CPS component (physical, cyber, or virtual). Two distinctive perspectives are associated with the semantic interpretation of these rules: the states perspective and the activities perspective. This study focuses on the activities perspective that specifies the behavior of control agents with a BPMN activity diagram. This highlights how these two models (CA-BRS and BPMN) complement each other to assist the designer in defining formal models for CPS. Additionally, it reveals how to provide the CA-BRS model with means to control unauthorized access to an electronic health record system.

Word embedding factor based multi-head attention

The natural language processing (NLP) field has made significant progress using deep learning models based on multi-head attention mechanisms, such as Transformer and BERT. However, there are two major limitations to this approach. First, the number of heads is often manually set based on empirical experience, and second, it is not clear enough in semantic understanding and interpretation. In this study, we propose a novel attention mechanism called Factor Analysis-based Multi-head (FAM) Attention, which combines the theory of explorative factor analysis and word embedding. The experimental results demonstrate that FAM Attention achieves better performance and requires fewer parameters compared to traditional methods while also having better semantic understanding ability and interpretability at the token level. This also has significant implications for current Large Language Models (LLMs), particularly in terms of effectively reducing parameter counts and enhancing performance.

Databases and Information Models for Semantic and Evolutionary Analysis in Fortified Cultural Heritage

The paper explores two fortified systems, as case studies, to evaluate different modelling approaches, the parameterisation of architectural components, and semantic interpretation, to define a repeatable methodology for classifying, accessing, and sharing architectural information. The use of informational structures to categorise data and relations through semantic attributes addresses the need to make data queryable and interoperable. In architectural documentation, this practice opens up new perspectives for creating and sharing collaborative repositories, radically transforming the way of disseminating and accessing knowledge. Linking qualitative data information with 3D models enables the development of semantic structures that provide a deeper understanding of intrinsic characteristics, historical transformations of architectural complexes, and their cultural context. While databases effectively manage structured information and relationships between different signifiers, semantically enriched 3D modelling, through the management of surfaces or parameters, offers an accurate and flexible representation of each component. The evaluation of these case studies not only improves the ability to understand and appreciate built heritage but also provides new opportunities for researchers in architectural documentation and history. Semantic subdivision processes of built complexes into individual components facilitate the analysis and the digital preservation of heritage, enabling more precise interpretations and faithful reconstructions.

Phraseological unit na zubakh (on the teeth) in the light of new contextual interpretations

Linguistic studies, related to the emergence and life of phraseological units, show the importance of a neological trend in their investigation, which allows you to see the fragments of the picture of the world relevant to speakers, and the variety of ways used to verbalize ideas about them. However, the meanings of neofraseologisms, recorded in dictionaries, do not always adequately reflect the real picture of their speech functions. The article considers a neological unit whose spectrum of semantic interpretations is far from being unambiguous. The contexts of the National Corps of the Russian language, illustrating the active usage of the phrase “na zubakh” (“on the teeth – gave it one’s all”), show that it has developed quite clear syntagmatic connections that speak in favor of at least two possibilities of its interpretation - with dictionaries fixating only one of the versions. A stable collocation with the verbs to pull out, to win, etc., on the one hand, and contextual supports, united by the meaning “to win, to achieve a goal," on the other, gradually form a different meaning of the idiom, very typical of sports discourse. It characterizes sport competition, excitement, manifestation of perseverance, stamina, etc. In the course of its functioning in speech, it is gradually “magnetized” by the semantics of “win”, “at any cost”, “by all means”. All this requires the need to seriously correct the modern semantics of the phraseological unit in the dictionary.

Something That Looks Like Money: Ethnopragmatic Analysis of Wealth-Related Title Names Among Young Igbo Businessmen

African anthroponyms are a rich repository of the socio-cultural materials of ethnolinguistic groups. They reveal speakers’ collective beliefs, norms, traditions, practices, philosophies and worldview. Among other things, literature on Igbo personal names show current trends in contemporary naming and the reasons for this, the social structure of their society. Igbo names also provide insight into what speakers value and uphold. This includes ideas they have about concepts such as wealth, which has also been interrogated. It has been discovered that titles also capture the ways wealth is viewed by Igbo-speaking people. This is obvious in the informal titles held by young businessmen of this ethnolinguistic group. This paper examines title names which are conferred informally by young Igbo businessmen. It particularly investigates the value holders place on wealth, which are captured by these names. While it explicates the identity that holders create for themselves when they have attained a certain level of financial success, it draws attention to the importance placed on wealth possession as a fundamental index for attaining high social status in Igbo land. The ethnopragmatic framework was employed to explain wealth-related title naming as a speech practice, which is different from the traditional order of this community of speakers. It is chosen for its emphasis on the understanding of speakers’ cultural nuances for the use of language, one of which is naming. Using ethnographic tools such as semi-structured interviews, passive participation and participant observation, information on forty-one such title names have been elicited. Questions asked included the semantic interpretation of the names, what circumstances inform the choice of the names and the importance bearers place on wealth. It is revealed that, for young Igbo businessmen, being wealthy is equated with the possession of money. The values they place on money are highly influenced by the capacity it gives to bearers, its usefulness in defending oneself and attacking adversaries and the provision of succor to those in need.

Attention-based lightweight deep hybrid CNN framework for image restoration

ABSTRACT Underwater image enhancement and processing have gained prominence in the field of image processing due to marine scientists' interest in uncovering new species and environments. This work presented a lightweight, attention-based, deep hybrid convolutional neural network (LW-AB-DHCNN) architecture to enhance overall efficiency. The traditional CNNs use subnetworks to enhance model depth and achieve the same functionality, but the proposed method employs multiple depth-wise separable convolutions, thereby reducing the computational complexity of the system. The proposed approach integrates Deep CNN with CBAM to provide an enhanced U-Net model. CBAM employs a self-attention method to acquire both local and global data in underwater images, thereby augmenting their semantic interpretation. This work also employed a unified error function to direct the training and optimization of the model. When the presented scheme was evaluated against benchmark datasets, it achieved an average PSNR of 25.69 dB and an average SSIM of 0.8624.

PhenoBench: A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain.

The production of food, feed, fiber, and fuel is a key task of agriculture, which has to cope with many challenges in the upcoming decades, e.g., a higher demand, climate change, lack of workers, and the availability of arable land. Vision systems can support making better and more sustainable field management decisions, but also support the breeding of new crop varieties by allowing temporally dense and reproducible measurements. Recently, agricultural robotics got an increasing interest in the vision and robotics communities since it is a promising avenue for coping with the aforementioned lack of workers and enabling more sustainable production. While large datasets and benchmarks in other domains are readily available and enable significant progress, agricultural datasets and benchmarks are comparably rare. We present an annotated dataset and benchmarks for the semantic interpretation of real agricultural fields. Our dataset recorded with a UAV provides high-quality, pixel-wise annotations of crops and weeds, but also crop leaf instances at the same time. Furthermore, we provide benchmarks for various tasks on a hidden test set comprised of different fields: known fields covered by the training data and a completely unseen field.

Arithmetic abilities of SNP systems with astrocytes producing calcium

Are the membrane systems able of performing arithmetic operations? In the last dozen years, there were published several implementations of the arithmetic operations based on membrane systems by using all available topologies (cell-like, tissue-like, or neural-like). In particular, the spiking neural P systems perform arithmetic operations by using the numbers represented in binary base. In this paper, we consider numbers represented in unary base (to each number n corresponds an object with multiplicity n), and we propose two encodings for the main arithmetic operations (addition, subtraction, multiplication and division) between numbers given in unary base: (i) for each pair of input values generate an instance of a spiking neural P system with astrocytes producing calcium with rules based on these values; (ii) generate a spiking neural P system with astrocytes producing calcium that does not depend on these values. While the second approach is commonly used in membrane computing to construct only a system for each operation, the first approach is interesting because each system is uniquely constructed based on a pair of input values , and so it performs faster the desired arithmetic operation. The main advantage (with respect to other attempts) of using any of these two approaches to perform arithmetic operations consists in the reduced size of created systems (number of locations and used rules). Additionally, we extend a semantic interpreter (in Haskell) for spiking neural P systems to test all the encodings of the arithmetic operations presented in this paper.

Through the ages and times to the crisis understanding the essence of Criminal Law

In modern jurisprudence, ideas about the crisis are usually associated with the state of law enforcement or law-making. To a lesser extent, crisis assessments relate to jurisprudence itself and its cognitive programs, which is most likely due to the high self-esteem of legal science of the results of its activities. At the same time, a critical analysis of the latter revealed that the basis of criminal law is the paradigm of cognition of social reality through the concepts of crime and punishment. Depending on their meaning, the law is given an appropriate semantic interpretation. It has come to the perception of criminal law as a framework term that only groups together more capacious concepts, norms and institutions. Taking into account the nature of the revealed problem, the purpose of the study was to clarify the reasons for the formation of the identified paradigm, as well as the factors that have supported its relevance for centuries. The achievement of this goal was facilitated by solving the tasks of identifying: the genesis of the paradigm and the determinants that feed it; the presence of its undeniable advantages and inevitable costs; the conformity of the evaluated concept with the nature of the crime and those values that are traditionally protected by Criminal Law. To achieve the intended goal and solve the tasks set, philosophical – axiological, ontological and epistemological – approaches, as well as general scientific (analysis, synthesis, induction, deduction) and sectoral (comparative legal, formal legal) methods are applied. As a result, it was revealed that the impetus for the formation of the studied paradigm was given by psychosomatic prerequisites. Its development was also influenced by the volitional concept of law, the adaptation of which led to the formation of a reflex science from Criminal Law. The absolutization of the volitional element has shifted the conscious factor to the background, which does not correspond to the nature of the crime and the values protected by law. The further exploitation of crime and punishment as the initial basis of Criminal Law and a source of knowledge about its ontological foundations has no prospects.

The visual effects of emoji in social media travel sharing on user engagement

Emojis in social media travel sharing are an effective tool for enhancing emotional expression or promoting semantic interpretation. However, the visual appeal of emojis as small icons remains uncertain. This study shows that using facial emojis in desirability travel sharing enhances tourists' visual attention and social media engagement. In contrast, using non-facial emojis in feasibility travel sharing leads to more effective visual attention and social media engagement. These relationships are mediated by imagery fluency and influenced differently depending on the emoji's position within a sentence (mid-sentence vs. end of sentence). Our research contributes to the study of emojis' visual appeal by providing a theoretical explanation of how and why the interaction effects of emoji visual types and travel sharing content occur. This study also offers practical implications for how tourists, travel marketers, and social media platforms can use emojis appropriately to increase engagement.

Anti-Diodorean Quantum Logic of Observables

The conception of “Anti-Diodorean” logic is based on the reversal of the definitions of Diodorus Cronus, who proposed to define modal concepts in terms of temporal ones. In case of quantum logic this is done by using the semantic interpretation of anti-Diodorean definitions, i.e. semantically defining spatio-temporal concepts by means of a quantum accessibility relation. Since Quantum Logic of Observables could be treated as the many-valued extension of the usual Quantum Logic, we can consider the Anti-Diodorean Logic of Observables in the same way.

Semantic Effects of Long-Distance Wh-Scrambling in Japanese

This paper investigates the semantic effects of long-distance wh-scrambling in Japanese. Through qualitative analysis, it demonstrates that long-distance scrambling is a semantically vacuous movement in declarative sentences where a wh-phrase undergoes radical reconstruction for interpretation. It is demonstrated that it need not flow into semantic interpretations, in contrast to wh-movement. Long-distance wh-scrambling, on the other hand, has an impact on sentential readings in interrogative sentences since the wh-phrase must draw scope from its nearest question marker, which must be understood through the c-command relationship. The difference is discovered to originate from a processing technique, with each alternative interpretation being more prominent in both the jumbled sentence and the canonical form. This study provides a syntactic-semantic perspective and offers new insights into the semantic implications of scrambling, thus contributing to the existing literature.

Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networks

This paper presents a sentiment analysis approach using Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Networks to train predictive models for sentiment analysis on social media, particularly focusing on Algerian Arabic Dialect. The method leverages word-to-vector embedding for word representation and incorporates natural language understanding of emojis to improve semantic interpretation. The model achieves a high accuracy of 94%, demonstrating its effectiveness in analyzing sentiments in online discussions. The originality lies in applying Bi-LSTM to handle multilingual challenges on social platforms. The findings have practical implications for business, policymaking, and public sentiment evaluation, while also contributing positively to fostering informed online discourse.

SF-GPT: A training-free method to enhance capabilities for knowledge graph construction in LLMs

Knowledge graphs (KGs) are constructed by extracting knowledge triples from text and fusing knowledge, enhancing information retrieval efficiency. Current methods for knowledge triple extraction include ”Pretrain and Fine-tuning” and Large Language Models (LLMs). The former shifts effort from manual extraction to dataset annotation and suffers from performance degradation with different test and training set distributions. LLMs-based methods face errors and incompleteness in extraction. We introduce SF-GPT, a training-free method to address these issues. Firstly, we propose the Entity Extraction Filter (EEF) module to filter triple generation results, addressing evaluation and cleansing challenges. Secondly, we introduce a training-free Entity Alignment Module based on Entity Alias Generation (EAG), tackling semantic richness and interpretability issues in LLM-based knowledge fusion. Finally, our Self-Fusion Subgraph strategy uses multi-response self-fusion and a common entity list to filter triple results, reducing noise from LLMs’ multi-responses. In experiments, SF-GPT showed a 55.5% increase in recall and a 32.6% increase in F1 score on the BDNC dataset compared to the UniRel model trained on the NYT dataset and achieved a 5% improvement in F1 score compared to GPT-4+EEF baseline on the WebNLG dataset in the case of a fusion round of three. SF-GPT offers a promising way to extract knowledge from unstructured information.

Middle Leadership in Educational Teams: Practices and Effects on the Development of Learning Communities

In a new context of management and teaching action, framed by the organisation of the school into educational teams, middle leaders can play a key role in promoting learning communities, teacher development and improving teaching. Therefore, it seems relevant to understand the practices of these leaders that make their leadership effective. To this end, we adopted a qualitative research paradigm, operationalised through a case study, combining a quantitative and qualitative approach. Thus, we articulated data analysis processes based on descriptive statistics, which we subjected to structural and semantic analysis and interpretation, with a qualitative approach, essentially based on content analysis, to delve into some unique contexts and the perspective of individual actors. After analysing the data, we conclude that educational team coordinators play a fundamental role in facilitating the interpersonal characteristics of learning communities, involving teachers in a more collaborative and collegial working environment. However, their role in facilitating teacher learning is weaker, which hinders their contribution to deepening teacher learning and changing classroom practices.

Middle Leadership in Educational Teams: Practices and Effects on the Development of Learning Communities

In a new context of management and teaching action, framed by the organisation of the school into educational teams, middle leaders can play a key role in promoting learning communities, teacher development and improving teaching. Therefore, it seems relevant to understand the practices of these leaders that make their leadership effective. To this end, we adopted a qualitative research paradigm, operationalised through a case study, combining a quantitative and qualitative approach. Thus, we articulated data analysis processes based on descriptive statistics, which we subjected to structural and semantic analysis and interpretation, with a qualitative approach, essentially based on content analysis, to delve into some unique contexts and the perspective of individual actors. After analysing the data, we conclude that educational team coordinators play a fundamental role in facilitating the interpersonal characteristics of learning communities, involving teachers in a more collaborative and collegial working environment. However, their role in facilitating teacher learning is weaker, which hinders their contribution to deepening teacher learning and changing classroom practices.

Granular computing-based time series anomaly pattern detection with semantic interpretation

Time series analysis may suffer from the “curse of dimensionality” due to its high-dimensionality characteristics. In terms of this issue, information granulation offers an effective vehicle to process time series at a higher level of abstraction. To take this advantage, this study conducts an interpretable time series anomaly pattern detection under the framework of granular computing, where each time series is granulated into a series of semantics according to its patterns and the anomaly ones are identified based on the obtained semantics. First, the time series is partitioned into a predefined number of segments and trend-based information granules are formed for the data points in each time interval. Guided by the principle of justifiable granularity, the granular results maintaining the main features of the original time series realize informative feature extraction and meaningful dimensionality reduction. Then, to realize semantic anomaly pattern detection, the Axiomatic Fuzzy Set (AFS) theory is generalized to construct and compute with semantic time series, and an AFS-based anomaly score is proposed to discover anomaly patterns. In the experiments, the proposed method is conducted on both UCR data sets and real-world time series, where the detected anomaly patterns are equipped with well-defined semantics.

Hierarchical neural processing in γ oscillations for syntactic and semantic operations accounts for first- and second-language epistemology.

We discuss event-related power differences (ERPDs) in low- and broadband-γ oscillations as the embedded-clause edge is processed in wh-dependencies such as Which decision regarding/about him/her did Paul say that Lydie rejected without hesitation? in first (L1) and second language (L2) French speakers. The experimental conditions manipulated whether pronouns appeared in modifiers (Mods; regarding him/her) or in noun complements (Comps; about him/her) and whether they matched or mismatched a matrix-clause subject in gender. Across L1 and L2 speakers, we found that anaphora-linked ERPDs for Mods vs. Comps in evoked power first arose in low γ and then in broadband γ. Referential elements first seem to be retrieved from working memory by narrowband processes in low γ and then referential identification seems to be computed in broadband-γ output. Interactions between discourse- and syntax-based referential processes for the Mods vs. Comps in these ERPDs furthermore suggest that multidomain γ-band processing enables a range of elementary operations for discourse and semantic interpretation. We argue that a multidomain mechanism enabling operations conditioned by the syntactic and semantic nature of the elements processed interacts with local brain microcircuits representing features and feature sets that have been established in L1 or L2 acquisition, accounting for a single language epistemology across learning contexts.

English-to-Hindi translation divergence study of English phrasal verbs

English phrasal verbs pose complex semantic interpretations and cause significant problems in their translation into the target language. The paper selects some frequent and highly polysemous English phrasal verbs listed with 10 or more senses in the English WordNet, collects sample English sentences from the British National Corpus and examines their Hindi translation equivalents in terms of the morpho-syntactic structure of Hindi verbs and the representation of semantic information. This study attempts to present the semantic equivalence in rendering the identical semantics in the verbal system of both languages under consideration. The study found that the most frequent and common way of realising the concepts represented by English phrasal verbs in Hindi is by incorporating the compound verb constructions in the Hindi language. Besides that, we observe multiple mapping patterns of English phrasal verbs in Hindi, including the simple Hindi verb (v), compound verb (v1-v2), and conjunct verb (noun/adjective/adverb-verb) constructions.

Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach

Recently, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.