Parsing Research Articles

Syntactic–Semantic Detection of Clone-Caused Vulnerabilities in the IoT Devices

This paper addresses the problem of IoT security caused by code cloning when developing a massive variety of different smart devices. A clone detection method is proposed to identify clone-caused vulnerabilities in IoT software. A hybrid solution combines syntactic and semantic analyses of the code. Based on the recovered code, an attributed abstract syntax tree is constructed for each code fragment. All nodes of the commonly used abstract syntax tree are proposed to be weighted with semantic attribute vectors. Each attributed tree is then encoded as a semantic vector using a Deep Graph Neural Network. Two graph networks are combined into a Siamese neural model, allowing training to generate semantic vectors and compare vector pairs within each training epoch. Semantic analysis is also applied to clones with low similarity metric values. This allows one to correct the similarity decision in the case of incorrect matching of functions at the syntactic level. To automate the search for clones, the BinDiff algorithm is added in the first stage to accurately select clone candidates. This has a positive impact on the ability to apply the proposed method to large sets of binary code. In an experimental study, the developed method—compared to BinDiff, Gemini, and Asteria tools—has demonstrated the highest efficiency.

Constant Values Distribution Investigation in the C Programs Source Code

Currently, software engineering plays a key role in software development, one of the criteria for the development of which is the investigation of its factology and various scientific and practical patterns. An important aspect of this area is the logic of program execution, operating with internal data, and, in particular, constant values, the identification of patterns in which actualizes this research. The main applications of this pattern include obtaining fundamental knowledge about algorithms, creating new and expanding existing metrics for evaluating and comparing program code, developing methods for its optimization, using it in genetic programming, etc.The purpose of this article is to obtain the frequency distribution of constant values in the source code of programs in the C programming language.The essence of the presented approach is to create a method for statistical analysis of the text of the source codes of programs contained in the ExeBench dataset (which consists of a huge amount of source code of functions in the C programming language, their assembler code for various processor architectures, compilation errors and other information).The proposed method is based on the use of algorithms for lexical and syntactic analysis of source code functions, semantic definition of constant types, and conversion of the recording of programming language symbols into the corresponding numeric or string values.The method has an implementation in the form of a software tool in the Python programming language, given in the form of an intuitive pseudocode. Experiments using this prototype allowed us to obtain the desired distribution of constant values for the source code of programs in the C programming language. Analysis of the obtained results allowed us to make a number of important theoretical and practical conclusions regarding the most frequently used constants, the correspondence of the obtained distribution to the Zipf law and its proximity to the exponential function, the anomalous appearance of a number of constants in the Top 50, etc.The scientific novelty of the proposed approach lies in the fact that the distribution of constant values for the source code of programs in the C programming language is obtained for the first time.The theoretical significance consists in obtaining new fundamental knowledge regarding the features and patterns of source code constructions, which can be extended to other programming languages.The practical significance consists in applying the distribution to a wide range of tasks, including the author's genetic reverse engineering, which in itself is a qualitatively new direction.

Improving semi-autoregressive machine translation with the guidance of syntactic dependency parsing structure

The advent of non-autoregressive machine translation (NAT) accelerates the decoding superior to autoregressive machine translation (AT) significantly, while bringing about a performance decrease. Semi-autoregressive neural machine translation (SAT), as a compromise, enjoys the merits of both autoregressive and non-autoregressive decoding. However, current SAT methods face the challenges of information-limited initialization and rigorous termination. This paper develops a layer-and-length-based syntactic labeling method and introduces a syntactic dependency parsing structure-guided two-stage semi-autoregressive translation (SDPSAT) structure, which addresses the above challenges with a syntax-based initialization and termination. Additionally, we also present a Mixed Training strategy to shrink exposure bias. Experiments on seven widely-used datasets reveal that our SDPSAT surpasses traditional SAT models with reduced word repetition and achieves competitive results with the AT baseline at a 2×∼3× speedup.

Analysis of Sentence Structure and Verbs Diversity of High School Physics Achievement Standards in the 7th – 2022 Revised Curriculum

Analysis of Sentence Structure and Verbs Diversity of High School Physics Achievement Standards in the 7th – 2022 Revised Curriculum

Exploring Word-Level Representations in Modern Natural Language Processing

Abstract: Natural Language Processing (NLP) is a dynamic and rapidly advancing field at the intersection of artificial intelligence and linguistics, focused on enabling computers to understand, process, and generate human language. Recent advancements intransformer-based models have significantly improved NLP capabilities, enablingmachines to understand and generate human language more effectively. This paper provides a comprehensive overview of NLP, tracing its historical development and recent trends. The discussion includes the different phases of NLP, from text pre- processing and tokenization to syntactic and semantic analysis, along with pragmatic considerations. Text normalization techniques, such as stemming, lemmatization, and removing stopwords, are explored to emphasize their importance in preparing raw text for analysis. Additionally, this paper presents a comparative analysis of popular word-level representation techniques used in NLP, including One-Hot Encoding, Bag of Words (BoW), and Term Frequency-Inverse Document Frequency (TF-IDF).

CUIDADOS DE ENFERMAGEM EM SITUAÇÕES TRAUMÁTICAS

In Brazil, nursing care in traumatic situations was based on the North American models adopted by the Fire Departments and the Mobile Emergency Care Service (SAMU). Due to issues related to legal aspects, in our country, one of the greatest challenges encountered in emergency care units, and one of the reasons that contributed to the support of several structures. METHODOLOGY: This is a descriptive epidemiological narrative review with a qualitative approach. It is a narrative review of the literature, a method that consists of a syntactic analysis of publications, enabling a synthesis of published studies and eliminating gaps that need to be filled by knowledge, through new bibliographic research. RESULTS AND DISCUSSIONS: Nursing professionals, equipped with solid scientific knowledge, play a crucial role in improving the quality of health services, especially in the care of patients in critical situations. This expertise contributes significantly to a better prognosis for patients, demonstrating that evidence-based practice is essential for positive results. CONCLUSION: With the development of this work, it was possible to evaluate the evidence found from literature on nursing care in traumatic situations and their complications. In addition, during this study, we saw the awareness of the nursing team about the importance of medical record notes, which, when well prepared, present valuable elements for the diagnosis of the patient’s needs and for the planning and evaluation of the assistance provided within the scope of nursing care.

Syntactic Analysis of Nominal Clauses Based on Chomsky’s Generative Grammar and Implications for Language Teaching

Nominal clause is a clause with nominal feature and syntactic analysis is the study of laws of sentence constructions. And Chomsky’s Generative Grammar is a very useful method to analyse a sentence in an explanatory way. This paper aims to focus on how to understand a nominal clause under Generative Grammar (GG) for the explanatory adequacy. In doing so, it will give a brief explanation to the generative mechanisms of simple sentence under GG, then move on to the similar mechanisms of complex sentence, here, it will focus on nominal clauses. On the syntactic analysis of nominal clauses, there are two steps: first recognizing its position in the whole sentence, then picking a proper connective for the nominal clause. Notice this paper will analyse a complex noun sentence for two sentences, one is the whole sentence, and another is the small clause, namely, the nominal clause within the whole sentence. Thus, it can conclude a way to analyse nominal clauses syntactically, and understand their inner constructions. Furthermore, it can explain why a nominal clause form like this and have a better understanding of complex sentences. More importantly, such method can play its role in linguistics and foreign language teaching, which would help language teachers build up a better understanding of the nature of language and the process of language learning.

Performance Assessment of GPT 4.0 on the Japanese Medical Licensing Examination.

To evaluate the accuracy and parsing ability of GPT 4.0 for Japanese medical practitioner qualification examinations in a multidimensional way to investigate its response accuracy and comprehensiveness to medical knowledge. We evaluated the performance of the GPT 4.0 on Japanese Medical Licensing Examination (JMLE) questions (2021-2023). Questions are categorized by difficulty and type, with distinctions between general and clinical parts, as well as between single-choice (MCQ1) and multiple-choice (MCQ2) questions. Difficulty levels were determined on the basis of correct rates provided by the JMLE Preparatory School. The accuracy and quality of the GPT 4.0 responses were analyzed via an improved Global Qualily Scale (GQS) scores, considering both the chosen options and the accompanying analysis. Descriptive statistics and Pearson Chi-square tests were used to examine performance across exam years, question difficulty, type, and choice. GPT 4.0 ability was evaluated via the GQS, with comparisons made via the Mann-Whitney U or Kruskal-Wallis test. The correct response rate and parsing ability of the GPT4.0 to the JMLE questions reached the qualification level (80.4%). In terms of the accuracy of the GPT4.0 response to the JMLE, we found significant differences in accuracy across both difficulty levels and option types. According to the GQS scores for the GPT 4.0 responses to all the JMLE questions, the performance of the questionnaire varied according to year and choice type. GTP4.0 performs well in providing basic support in medical education and medical research, but it also needs to input a large amount of medical-related data to train its model and improve the accuracy of its medical knowledge output. Further integration of ChatGPT with the medical field could open new opportunities for medicine.

Spatial reconfiguration of Iranian traditional houses to explore effective strategies of flexibility using space syntax analysis

Spatial reconfiguration of Iranian traditional houses to explore effective strategies of flexibility using space syntax analysis

Application Of the National Corpus Of The Kazakh Language In Linguistic Research

National corpora are essential tools in modern linguistics, offering valuable data for analyzing language structures and their connection to the world.The primary goal of this study is to enhance the perception and analysis of the Kazakh language. It introduces a methodological approach that deepens the understanding of the structure and features of Kazakh.The research utilizes frequency analysis, syntactic parsing, and statistical methods to examine Kazakh language texts. The results demonstrate that the National Corpus provides extensive data for studying linguistic patterns. Frequency analysis identifies the most common words and phrases, while syntactic analysis uncovers patterns and connections between words. The study also highlights the variation in word usage and constructions, showing the language's adaptability to modern communication.The findings are applicable to lexicography, semantics, and syntax studies of the Kazakh language. Additionally, they offer practical benefits in language teaching, lexicographic resource creation, machine translation, and natural language processing.In conclusion, the research provides valuable tools for in-depth analysis of the Kazakh language, with broad applications across linguistic studies and practical fields.

A spatial syntactic analysis of temple cultural heritage settlements on the western Sichuan plateau of China

The protection and development of the cultural heritage of temple settlements, the utilization of cultural resources, and the inheritance of settlement characteristics are pressing issues that require urgent attention. To address these challenges, this study employs the spatial syntax research method to delve into the spatial form of the cultural heritage of temple settlements on the western Sichuan plateau. Specifically, the study focuses on four distinct settlement types in Ganzi Tibetan Autonomous Prefecture, China: Baiyu Temple, Ganzi Temple, Dongga Temple, and Gesang Temple. Using Depthmap software, the study quantifies settlement parameter values and analyzes connection value, integration degree, and comprehensibility, to reveal their syntactic structural characteristics. Our findings indicate that these four temple settlements present an organic whole in terms of spatial form. However, there are distinct differences in their syntactic structures, which are closely linked to factors such as settlement scale, historical inheritance, and other contextual influences. Based on our spatial syntax analysis, the study proposes a series of protective measures and development strategies. First, emphasis should be placed on preserving the overall spatial layout and organic integrity of temple settlements to prevent overdevelopment and the disruption of original patterns. Second, planners must strengthen the protection and management of internal spatial elements to maintain their interrelationships. Finally, planners should formulate differentiated protection and development plans tailored to the unique characteristics and values of each temple settlement type. While the study focuses on traditional settlement spatial forms and their relationship with human behavior, further exploration is needed to investigate the impact of political, economic, and environmental factors on these forms.

The Argument of the Matrix Verb Seems in Spec T: The Raised DP or the Given DP

Generative linguists claim that the argument of the matrix verb as in Mary seemed to be innocent is the DP (determine phrase), which is the raised argument of the to-infinitive clause being raised to be landed in the Spec T. However, the current study argues that the DP argument as the subject of the matrix verb seems to be the given DP and this is supported by functional linguistics. The argument in this study is statistically supported by the empirical evidence of international applied linguistic research articles. The data collection was English for Specific Purposes (ESP), which were indexed in the Q1 SCOPUS database. A total number of approximately 250,000 words was derived from 25 applied linguistics research articles, which contained 47 tokens concerning the argument with the matrix verb seem. The data analysis was divided into the framework of syntactic analysis and the quantitative study of inferential statistical analysis. The framework of syntactic analysis follows functional linguistics called the pragmatic discourse of givenness. The statistical analysis in this study was Pearson Correlation in SPSS29. The results showed that there was a statistically significant relationship between the given DP of the matrix verb seem and applied linguistic research articles. The p-value was reported at 0.044. Thus, the hypothesis in this study was accepted. The discussion follows the principle of cohesion and fixed colligations. It is recommended that the hypothesis in this study be tested by other English materials, such as novels and business documents, to contribute to the field.

Discourse complex <i>i da</i> ‘and yes’ as a dialogue actualizer in modern Russian speech

The article examines semantic and pragmatic features of the discursive complex i da (“and yes”). The relevance of the research is explained by the active inclusion of i da in contemporary speech practice, various genres and styles over the past decade, which was not observed before. The novelty of the research lies in the fact that the element i da becomes the object of linguistic analysis for the first time. This study contributes to the understanding of the discursive complex i da in the context of modern linguistic practices. The findings of this research can be beneficial for further linguistic studies and practical applications. The element i da is not recorded in dictionaries and reference books, which also testifies to the novelty of the material presented in the article, which was extracted from the “National Corpus of the Russian Language”, the electronic media bank “Integrum”. The paper is also based to some degree on the personal observations of the authors. This study provides a fresh perspective on the linguistic element i da, contributing to the existing body of linguistic research. The research was conducted within the framework of the structural and descriptive method using semantic, syntactic, and contextual analysis. As a result, the main discursive role of the complex i da was established, i. e., maintaining the interactive nature of conversation through a ‘delayed response’ mechanism, which allows considering i da as a feedback marker. The discursive complex i da involves a real or potential (in the case of a monologue) interlocutor/opponent in the discourse, performing the role of a dialogic actualizer. The core seme of the complex (‘agreement’) can be explicitly supported by the context, and the addressee of the agreement can be not only the ‘other’ but also the speaker himself. The study reveals the rhetorical potential of i da in monologic speech. This research provides a comprehensive analysis of the discursive complex i da, highlighting its role in dialogic actualization and its rhetorical potential in monologic speech. The variants of punctuation design of the complex i da identified during the research indicate the need for its further study and the creation of a theoretical basis for subsequent codification in punctuation guides. The study proposes a hypothesis about the appearance of i da in Russian as a result of calquing English phrases and yes / and yeah, which are of international nature. This research emphasizes the importance of further investigation into the discursive complex i da, its punctuation variants, and its origins in the Russian language.

Historic street layout and its impact on shaping residents’ daily lives: insights from Sanxue Street, Xi’an

ABSTRACT Historic street layouts are integral to historic districts’ tangible cultural heritage and are intricately linked to the residents’ daily lives; however, existing studies have only examined their spatial aspects, primarily using quantitative methods. Conversely, studies on tangible cultural heritage’s impact on residents’ lives have principally employed qualitative methods. Therefore, this study bridges qualitative and quantitative approaches to propose a layout – daily life model, which explains the relationship between the layout of Xi’an, China’s Sanxue Street historic and cultural district, and its residents’ daily lives. A combination of space syntax and geographic information system-based land-use analyses revealed that eating, shopping, calligraphy, and transportation were the main activities of residents. Questionnaire responses from 335 participants validated the proposed model and confirmed its feasibility. When certain types of facilities are widely distributed in streets with high choice and integration, residents’ daily lives tend to involve the functions represented by them. Furthermore, calligraphy is an important part of most residents’ daily lives, regardless of the transportation mode used. This study contributes to the understanding of the effect of tangible cultural heritage, particularly street layouts, on residents’ daily lives in historic districts.

HSP-UNet: An Accuracy and Efficient Segmentation Method for Carbon Traces of Surface Discharge in the Oil-Immersed Transformer

Restricted by a metal-enclosed structure, the internal defects of large transformers are difficult to visually detect. In this paper, a micro-robot is used to visually inspect the interior of a transformer. For the micro-robot to successfully detect the discharge level and insulation degradation trend in the transformer, it is essential to segment the carbon trace accurately and rapidly from the complex background. However, the complex edge features and significant size differences of carbon traces pose a serious challenge for accurate segmentation. To this end, we propose the Hadamard production-Spatial coordinate attention-PixelShuffle UNet (HSP-UNet), an innovative architecture specifically designed for carbon trace segmentation. To address the pixel over-concentration and weak contrast of carbon trace image, the Adaptive Histogram Equalization (AHE) algorithm is used for image enhancement. To realize the effective fusion of carbon trace features with different scales and reduce model complexity, the novel grouped Hadamard Product Attention (HPA) module is designed to replace the original convolution module of the UNet. Meanwhile, to improve the activation intensity and segmentation completeness of carbon traces, the Spatial Coordinate Attention (SCA) mechanism is designed to replace the original jump connection. Furthermore, the PixelShuffle up-sampling module is used to improve the parsing ability of complex boundaries. Compared with UNet, UNet++, UNeXt, MALUNet, and EGE-UNet, HSP-UNet outperformed all the state-of-the-art methods on both carbon trace datasets. For dendritic carbon traces, HSP-UNet improved the Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and Class Pixel Accuracy (CPA) of the benchmark UNet by 2.13, 1.24, and 4.68 percentage points, respectively. For clustered carbon traces, HSP-UNet improved MIoU, PA, and CPA by 0.98, 0.65, and 0.83 percentage points, respectively. At the same time, the validation results showed that the HSP-UNet has a good model lightweighting advantage, with the number of parameters and GFLOPs of 0.061 M and 0.066, respectively. This study could contribute to the accurate segmentation of discharge carbon traces and the assessment of the insulation condition of the oil-immersed transformer.

Peculiarities of Adverb Placement in English Sentences (exemplified by the adverb "fairly")

The article deals with the problem of adverb placement in English sentences. The paper aims at studying the main tendencies and patterns which influence the choice of the syntactic position of adverbs in English. Special attention is paid to the correlation between the semantic and syntactic features of adverbs. The article also describes possible syntactic positions of adverbs belonging to different semantic classes and the factors which determine the change of the place of the adverb in the sentence. The paper also considers the peculiarities of functioning of adverbs in the sentence based on the analysis of the adverb "fairly". The material for study includes contexts of use of the adverb "fairly" taken from the British National Corpus (BNC). The research uses the following methods: descriptive and statistical methods, methods of contextual analysis and syntactic analysis. The relevance of the subject of the research is due to the fact that in modern English adverbs are characterized by a high level of variability in syntactic placement, and even adverbs belonging to the same semantic class may have different syntactic properties. The analysis showed that "fairly" can be used as an intensifying adverb, an emphasizer, an adverb of manner, an evaluation adverb; in a number of cases it can also acquire additional modal meanings or serve as a speech act-related adverb. Each meaning of the adverb is characterised by its specific variants of syntactic positioning: before the word it modifies, in medial position, in end position and as a parenthetic word detached from the rest of the sentence. The results of the research show the necessity to study the syntactic properties of adverbs in more detail and to clarify their existing dictionary definitions.

Digital family bonds are strengthened through effective communication dynamics on YouTube in Indonesia

General Background: Family communication plays a critical role in maintaining healthy relationships among family members, promoting openness, support, and understanding. Effective communication strengthens family bonds, especially during shared moments such as gatherings. Specific Background: Fadil Jaidi's YouTube channel provides an intriguing case study of family communication, showcasing how members interact and resolve issues. These video interactions offer valuable insights into the dynamics of family communication, particularly in the digital age. Knowledge Gap: Despite the extensive focus on communication in family studies, few have explored how family interactions are represented in social media content, especially YouTube channels, where family moments are broadcast to the public. The role of these platforms in influencing family communication patterns remains underexplored. Aims: This study aims to describe and explain the family communication patterns that occur on Fadil Jaidi's YouTube channel, focusing on openness, support, empathy, positiveness, and equality through content analysis. Results: A qualitative content analysis of five selected videos revealed that family communication on Fadil Jaidi’s channel runs smoothly. The interactions were characterized by 24 scenes: 8 scenes showing openness, 5 scenes of support, 2 scenes of empathy, 6 scenes of positiveness, and 3 scenes of equality. These categories were identified using thematic and syntactic analysis. Novelty: This study is among the first to analyze family communication within a public YouTube context, applying Eriyanto's content analysis framework. It highlights how online platforms can both reflect and influence real-life family dynamics. Implications: The findings suggest that public displays of family communication on social media platforms, like YouTube, can serve as examples for fostering positive interactions in real-life family settings. This research contributes to the understanding of how digital platforms can be used to promote healthy communication in family contexts. Highlights: Family communication on YouTube shows openness, support, empathy, positiveness, equality. Fadil Jaidi’s videos highlight effective family interactions in everyday scenarios. The study reveals digital platforms’ role in shaping family communication dynamics. Keywords: family communication, YouTube analysis, content analysis, social media, qualitative research

IDSV-GCN: Integrating Dual Syntactic Views Graph Convolutional Network for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards given aspects in a sentence. Recent research has highlighted that graph neural networks based on syntactic dependency trees effectively convey rich syntactic information and are suitable for ABSA tasks. Most existing work typically adopt either dependency type or minimum distance between nodes in the original syntactic tree to construct different views. However, existing work overlooks the high-dimensional local feature information in dependency relations, resulting in inadequate exploration of this view. Meanwhile, most work ignores that the minimum distance view and the dependency view can be complementary and integrated in syntactic analysis. Unfortunately, directly adding the two views introduces noise issues, leading to unsatisfactory results after their integration. To address these issues, this paper introduces an integrating dual syntactic views graph convolutional network (IDSV-GCN) model that seamlessly integrates dependency type view and minimum distance view, enabling comprehensive utilization of syntactic structure and information. To better address the computation of weights in the dependency type view, we innovatively propose a fusion attention mechanism. This approach effectively captures both global and local feature information. To more effectively integrate the two views and reduce the impact of noise, we employ a syntactic mask layer to filter out noise in the dependency type view and highlighting important dependency feature information. Then, we construct a minimum distance weight matrix based on the matrix obtained from the dependency type view. Finally, we merge the two matrices and feed them into the GCN to facilitate the model’s learning of syntactic information. Experimental results on three widely-used benchmark datasets demonstrate that our proposed IDSV-GCN model outperforms state-of-the-art methods.

Influence of Campus Outdoor Spaces on Students Behavior: Enhancing Social Interaction and Learning at An-Najah University

This study examines how the physical features of university campus outdoor spaces affect student behavior, aiming to promote social interaction and improve learning outcomes. Two central questions guide the research: how student movements relate to open space features, and how campus layout influences student circulation and interaction. By employing a mixed-method approach, including field observations and space syntax analysis, five areas at An-Najah University Campus are studied. The findings highlight the importance of these insights in urban design and planning, especially for teaching staff, students, and visitors. Results indicate that spatial layout and physical features significantly affect space usage, with students favoring areas with seating, shade, and social opportunities, adapting to informal setups when formal options are limited. Visibility is key, with students favoring spaces offering maximum visibility for seating. This understanding informs architects and planners in creating engaging campus environments for social interaction and learning.

Investigating the Linguistic Fingerprint of GPT-4o in Arabic-to-English Translation Using Stylometry

This study explores the linguistic and stylistic characteristics of machine-generated texts, focusing on the output of GPT-4o. Using various natural language processing (NLP) techniques, including word frequency and stopword count analysis, readability and sentence structure metrics, lexical diversity measures, syntactic frequency analysis, and named entity recognition (NER), the research aims to uncover the stylometric fingerprints present in machine-generated content. The results reveal that GPT-4ogenerated texts exhibit moderate lexical diversity and syntactic complexity, with certain chapters reflecting higher readability and more varied sentence structures, while others lean toward simpler linguistic patterns. The findings also highlight thematic variation across chapters, as observed in the distribution of named entities, which contributes to understanding the model’s handling of different contextual content. The research suggests that while GPT-4o maintains a consistent style in its generated text, there are distinguishable characteristics that may serve as indicators of machine authorship. This provides valuable insights for stylometric analysis, authorship attribution, and the identification of machine-generated texts in various contexts. Future research could extend this work by exploring deeper stylometric features, conducting cross-model comparisons, and developing advanced authorship detection algorithms tailored for AI-generated content. Moreover, the ethical implications of stylometric analysis in the context of AI-generated texts warrant further investigation, particularly as machine-generated content becomes increasingly prevalent across different domains.