Parsing Research Articles

A Graph‐Based Methodology for Optimal Design of Inerter‐Based Passive Vibration Absorbers With Minimum Complexity

Passive vibration absorbers (PVAs) play a crucial role in mitigating excessive vibrations in engineering structures. Traditional PVA design typically begins with proposing a beneficial topological layout, incorporating stiffness, damping, and inertance elements, followed by optimal sizing of each element to minimise specific response of dynamically excited structures. An alternative approach involves first designing the impedance function of a PVA and then identifying a passive mechanical layout that replicates this impedance using network synthesis techniques. However, both methods struggle to identify the most efficient PVA layout using the minimum number of elements (referred to as “complexity”) for a given vibration suppression problem. To this end, this study introduces a graph‐based methodology for designing optimal configurations (i.e., layout + sizing) of two‐terminal spring‐damper‐inerter PVAs that achieve specified performance goals with minimum complexity. In this approach, a PVA is represented as a weighted coloured multigraph, enabling the application of a novel graph‐based enumeration technique to generate the full set of potential layouts from any given number of mechanical elements. This enumeration is followed by a performance assessment of all layouts to pinpoint the optimal absorber configuration for the given problem. The methodology’s automation capability and versatility make it suitable for various civil and mechanical engineering applications. The effectiveness of the proposed methodology is demonstrated through two case studies: a vibration absorber design for a wind‐excited tall building and a suspension design for a road vehicle. In both cases, the proposed methodology successfully identifies innovative PVA layouts that surpass traditional designs with minimum additional elements.

Learning Dayak Literature through Information Systems

This study aims to analyze old poetry (mantras) as part of learning literature during the Corona pandemic that is currently hitting Indonesia. Also, this study revitalizes culture by creating an information system application. This study used descriptive qualitative. There were 16 participants, consisting of the village community of Ranyai, cultures, traditional stakeholders, and village heads. This method provided an overview and described the research results on literary culture and an analysis of the structure of prayer/mantra in the Gawai Dayak tradition and its revitalization efforts. The results showed that the structure of the text in the form of mantra utterances was based on the analysis of syntax, rhyme, and rhythm. The context of the Gawai Dayak tradition consists of cultural, situational, social, and ideological contexts. The context of the Gawai Dayak tradition consisted of motion, proxemic, paralinguistic, and material contexts. The design of an information system on Dayak literature could help realize the revitalization of cultural traditions and teaching and learning processes in a directed, effective, and consistent manner during the Corona pandemic. The implication for further research was the opening of further research developments, such as looking for values from tradition. Learning that used information systems was also still wide open to be developed with other more complete applications and under the times.

Advancements in Natural Language Processing (NLP) and Its Applications in Voice Assistants and Chatbots

Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, revolutionizing various fields including voice assistants and chatbots. This paper explores the latest developments in NLP technologies and their applications in enhancing the functionality and intelligence of voice assistants and chatbots. Beginning with an overview of NLP and its core components, such as tokenization, syntactic analysis, semantic understanding, and machine learning techniques, this paper delves into the cutting-edge techniques driving the evolution of NLP. Furthermore, it discusses the challenges faced by NLP systems and the strategies employed to address them. Subsequently, the paper explores the utilization of NLP in voice assistants and chatbots, elucidating how these technologies are reshaping human-computer interaction, improving user experiences, and expanding the scope of applications in various domains. Additionally, it examines the impact of NLP advancements on the future trajectory of voice assistants and chatbots, highlighting potential opportunities and implications for research and industry

Review to unfold the role of Machine Learning Algorithms in Natural Language Processing

This research review has been written to explore the role of machine learning algorithms in natural language processing (NLP), a computer field that offers human-like comprehension of virtual text. The main motive of the article is to reveal the participation of machine learning (ML) in NLP and its scope in coherent fields. In this study, the researchers have used a systematic literature review approach to explore the role of ML algorithms in NLP. Highlighting the techniques of ML algorithms as supervised, unsupervised, semi-supervised, and reinforcement methods, this study discloses the connectivity with morphological, semantic, syntactic, pragmatic, and discourse analysis in NLP. The article describes that ML/NLP has immense applications in different fields where several tools of ML/NLP are utilized. The researchers have studied the use of chatbots, text summarization tools, web scrapping, sentimental analysis in the social media stock market, medical field disease detection, and fraud detection. ML’s role and contributions to the progressions are detained in current studies. A distinct comparison has been made between the past present and future of ML in NLP. ML algorithms and applications with examples are disclosed in the classification of logistic regression, SVM, Naive Bayes, K-Nearest Neighbor, and decision tree. Data distortion, interpretation of the research findings, and contextual ambiguity are visibly determined as challenges and discussed in the study. This writing opens up to the recent advancements and advantages in multiple areas correspondingly. The discussions and findings expose the role of ML in opening the gateway to the profound revolutionary search engines, algorithms, and multiple techniques through the development of technology and its evolution to adapt to new language differences, slang, and changes in language use.

PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection

Personality detection identifies personality traits in text. Current approaches often rely on deep learning networks for text representation but they overlook the significance of psychological language knowledge in connecting user language expression to psychological characteristics. Consequently, the accuracy of personality detection is compromised. To address this issue, this paper presents PS-GCN, a model integrating Psychological knowledge and Sentiment semantic features through Graph Convolution Networks. Firstly, the Bi-LSTM network captures local features of preprocessed sentences to accurately represent the output of sentence sentiment features. Secondly, GCNs map psycholinguistic knowledge, forming semantic networks of entities and relationships. P-GCN is designed to capture the dependency information between psycholinguistic features, while S-GCN utilises syntactic structure analysis to gather more abundant information features and enhance semantic understanding ability. Finally, attention calculation is employed to reinforce key features and weaken irrelevant information. Additionally, a sentence group model captures combined features of related sentences, effectively utilising the text structure to mine sentimental features. Experimental results on multiple datasets demonstrate that the proposed method significantly improves the classification accuracy in personality detection tasks.

The role of street network metrics in shaping distance distributions in a residential neighbourhood

Walkability studies often rely on physical proximity metrics for destination accessibility. However, some recent studies argue that configuration (topological) distance provides a more accurate reflection of the actual distances experienced by individuals. This study therefore aims to test the extent to which an association exists between network-metric proximity to several destinations and two main measures of space syntax – integration and choice – in a dendriform network structure such as Putrajaya city (Malaysia). Using GIS, multiple buffer-service areas were generated around each housing unit (N = 2,392). Interpolation and space syntax analyses were conducted to assess metric and topological distance at local and global levels for each buffer. The statistical analysis showed that distances to commercial areas, transportation and average distances to all destinations were strongly affected by the syntactical properties of the neighbourhood. Moreover, global measures were more powerful in detecting metric-distance changes compared to local measures. Aligned with natural movement theory, these results support the idea that the spatial properties of urban form have a significant impact on distance distributions within residential settings, specifically distances to commercial areas. Therefore, policies that promote mixed-use development, especially in areas with good transportation access, should consider the impact of syntactical accessibility of the network.

Label-Free Data Mining of Scientific Literature by Unsupervised Syntactic Distance Analysis.

Label-free data mining can efficiently feed large amounts of data from the vast scientific literature into artificial intelligence (AI) processing systems. Here, we demonstrate an unsupervised syntactic distance analysis (SDA) approach that is capable of mining chemical substances, functions, properties, and operations without annotation. This SDA approach was evaluated in several areas of research from the physical sciences and achieved performance in information mining comparable to that of supervised learning, as shown by its satisfactory scores of 0.62-0.72, 0.60-0.82, and 0.86-0.95 in precision, recall, and accuracy, respectively. We also showcase how our approach can assist robotic chemists programmed to perform research focused on double-perovskite colloidal nanocrystals, gold colloidal nanocrystals, oxygen evolution reaction catalysts, and enzyme-like catalysts by designing materials, formulations, and synthesis parameters based on data mined from 1.1 million literature references.

Comparative Analysis of Syntax and Semantics Sering with Jingchang

The learning of a foreign language cannot be separated from an understanding of the structure and characteristics of that language, especially in terms of understanding grammar. This research aims to analyze the syntactic and semantic differences between the Indonesian word sering and the Mandarin word经常jingchang. The research method used in this writing is a qualitative method involving an analysis of two linguistic corpora, namely the Indonesian corpus and the Center for Chinese Linguistics PKU corpus. Based on the corpus data analysis, the results are as follows: 1. Sentence patterns and relationships between sentences in the Indonesian word sering can be used in declarative and interrogative sentence patterns; In sentence position, sering can be used in the middle of a sentence with the condition of use after verbs/adjectives that function as predicates. In informal usage, it can also be used at the beginning of a sentence, but not at the end of a sentence. In terms of synonym usage, the word sering can be used with action verbs, desires, existence, commands, decisions, and with adjectives indicating attitudes and feelings, but cannot be paired with adjectives indicating distance, form, and taste. 2. In terms of meaning, both the Indonesian word sering and the Mandarin word经常jingchang have the meaning of frequency and high intensity. AbstrakPembelajaran bahasa asing tidak dapat terlepas dari pemahaman akan struktur dan kekhasan bahasa tersebut, terutama dalam hal pemahaman akan tata bahasa. Penelitian ini bertujuan menganalisis perbedaan sintaksis dan semantik kata bahasa Indonesia sering dan bahasa Mandarin经常jingchang. Metode penelitian yang digunakan dalam penulisan ini adalah metode kualitatif berupa analisis terhadap dua korpus linguistik, yakni korpus Indonesia dan Center for Chinese Linguistics PKU. Berdasarkan pada analisis data korpus, didapatkan hasil: 1. Pola kalimat dan hubungan antarkalimat pada kata bahasa Indonesia sering dapat digunakan pada pola kalimat deklaratif dan interogatif; Pada posisi kalimat, sering dapat digunakan pada tengah kalimat dengan syarat penggunaan setelah kata kerja/sifat yang berupa predikat, pada penggunaan informal, bisa juga digunakan pada awal kalimat, tetapi tidak dapat digunakan pada akhir kalimat; pada penggunaan padanan kata, kata sering dapat digunakan dengan kata kerja penanda tindakan, keinginan, keberadaan, memerintah, dan memutuskan, serta pada kata sifat penunjuk sikap dan perasaan, namun tidak dapat dipadankan dengan kata sifat penunjuk jarak, bentuk dan rasa. 2. Dalam segi makna, bahasa Indonesia sering' dan bahasa Mandarin经常jingchang keduanya memiliki makna berulang dan intensitas tinggi.

MIMIC-CXR Radyoloji Raporlarının DDİ Yöntemleriyle Anlamsal ve Yapısal Analizi

Artificial intelligence that aims to imitate human decision-making processes, using human knowledge as a foundation, is a critical research area with various practical applications in different disciplines. In the health domain, machine learning and image processing techniques are increasingly being used to assist in diagnosing diseases using laboratory results, findings, MRI, tomography, or radiology images, and etc. However, many healthcare reports, such as epicrisis summaries prepared by clinical experts, contain crucial and valuable information. In addition to information extraction from healthcare reports, applications such as automatic healthcare report generation are among the natural language processing research areas based on this knowledge and experience. The primary goals are to reduce the workload of clinical experts, minimize the likelihood of errors, and save time to speed up the diagnosis process. The MIMIC-CXR dataset is a huge dataset consisting of chest radiographs and reports prepared by radiology experts related to these images. This study focuses on the structural and semantic analysis of MIMIC-CXR radiography reports. Before developing a natural language processing-based model, preprocessing steps were applied to the dataset, and the results of syntactic and semantic analyses performed on unstructured report datasets are presented. This study is expected to provide insights for developing language models, particularly for developing a natural language processing model on the MIMIC-CXR dataset.

Prefabricated orality and the translation of Spanish and Catalan approximators into English in a TV series

Discourse markers have been studied in translation studies in the variety known as prefabricated orality, present in audiovisual products in which spontaneous orality is feigned through a priori script writing. These particles are made explicit or implied when transferring from one language to another. In the present work, I delve into the translation of four discourse markers: como and com, from Spanish and Catalan, respectively, and their variants como que and com que, categorized as approximators. The corpus used is a TV series based on improvisation, so the present article challenges the adequacy of the label of prefabricated orality considering the lack of a script and the performative factor of language. The analysis is divided into the classification of the textual and pragmatic values of the markers in the source languages and their translation in the English subtitling. The results align with past studies in which the preference seems to be for the omission of markers, regardless of their contribution to the discourse, with the losses that this entails. Beyond this, it has been found that other factors, such as co-occurrence with other markers or the marker's position, seem relevant in their translation, so a syntactic analysis of translation techniques is proposed.

Methods of transference stylistic units to the target language in literary texts

This paper focuses on the stylistic approach to literary texts in their original language, evaluated through unique grammatical structures inherent to each language unit. Morphological and syntactical analyses contribute to the formation of logical steps in stylistic units. Word order, a key category in lexicology, plays a crucial role in the creation of stylistic units, influencing the use of stylistic devices and expressive means.

It’s complicated: The relationship between lexis, syntax and proficiency

This paper explores the relationship between lexical and syntactic complexity measures and proficiency in L2 English argumentative essays written by L1 Czech high school students. Syntactic complexity is generally understood as referring to the “range and sophistication” (Ortega 2015) of grammatical constructions, whereas lexical complexity can refer to the range and frequency of the words used. The research used 100 essays written by final year high school students. Lexical complexity was analysed using the Lexical Complexity Analyzer (Ai & Lu 2010, Lu 2012), syntactic complexity using the L2 Syntactic Complexity Analyzer (Lu 2010, 2014) and Biber et al.’s (2011) hypothesised developmental stages for complexity framework. Despite a large number of measurements failing to produce any significant patterns, positive correlations were found between lexical diversity measures and vocabulary scores. Similarly, Mean Length of Clause (MLC) and Complex nominals per clause (CN/C) showed weak positive associations with grammar scores, as did Stage 5 of the developmental stages. The findings provide an insight into the kinds of complexity features that can be given more focus during instruction and underscore the potential of these measures as determinants of proficiency.

The antipassive in Kirundi

The Kirundi verbal suffix -an is notably polysemous with three possible meanings: reciprocal, associative, and antipassive. The contexts in which the antipassive is ambiguous or distinct raise questions for -an’s syntactic and semantic analysis. In this paper, I examine new data which demonstrate that -an’s primary function is to add a relation between the verb and an implicit argument. These data support an analysis where -an is a specialized high applicative head (ApplREL) which adds the semantics of such a relation. The distribution of the different meanings is then accounted for by differential semantic selection and binding. This analysis thereby simplifies -an to its relational role and accounts for the ambiguous and non-ambiguous contexts that characterize the suffix. Additionally, this paper addresses various questions about -an’s antipassive status and potential issues with the applicative approach.

Experimental investigation of the seismic performance of an L-shaped roof-sidewall joint with a cogging composite connection

The prefabricated connections with better mechanical properties and optimal layout are keys to guarantee good seismic and waterproof performances of assembled underground structures. In this study, a local prestressed cogging (high-strength bolt-grouted sleeve) composite connection (HSB-GS), an integral prestressed cogging (prestressed steel strand-grouted sleeve) composite connection (PS-GS), and a non-prestressed cogging (ultra-high performance concrete-grouted sleeve) composite connection (UHPC-GS) are proposed. Subsequently, three 1/4-scale prefabricated roof-sidewall specimens comprising the three proposed connections and an integral cast-in-place specimen were designed and fabricated. Finally, quasi-static tests were conducted on the specimens, and the results were verified through finite element analysis. The experimental and numerical results show that UHPC exhibits excellent bonding performance with ordinary concrete, and it can prevent and delay damage to the seam. Furthermore, cogging can limit crack development and improve the shear resistance of the connections to a certain extent. The HSBs can improve the self-resetting ability of the specimen and delay damage to the seam to some extent. Therefore, when HSB-GS and PS-GS are arranged in the roof plastic hinge and UHPC-GS is arranged in the roof-sidewall joint core, they can lead to the transfer of the roof plastic hinge. In addition, the HSB-GS and PS-GS arranged in the roof plastic hinge can easily cause shear damage to the joint core; while the UHPC-GS arranged in the joint core can decrease the relevant damage to the joint core.

Definiteness marking in American Norwegian: a unique pattern among the Scandinavian languages

This paper examines definiteness marking in American Norwegian (AmNo), a heritage variety of Norwegian spoken in the US. The description adds another language to the much-studied variation within Scandinavian nominal phrases. It builds on established syntactic analysis of Scandinavian and investigates aspects that are (un)like Norwegian spoken in the homeland. A central finding is that the core syntax of Norwegian noun phrases is retained in AmNo, while the morphophonological spell-out is sometimes different. Indefinite determiners, for example, are obligatory in AmNo, but some speakers produce them with non-homeland-like gender agreement. One systematic change is observed: double definiteness has been partially lost. The typical AmNo modified definite phrase lacks the prenominal determiner that is obligatory for varieties in Norway. I argue that this is a syntactic change which allows the realization of D to be optional. This is a pattern not found in the other Scandinavian languages. At the same time, this innovative structure in AmNo is not like English, the dominant language of the AmNo speakers. This demonstrates heritage language change that is distinct from both the homeland language and the dominant language.

Constructicon in progress

Abstract Over the last forty years, Construction Grammar has emerged as the leading syntactic theory in cognitive linguistics. Usage-based versions of Construction Grammar have successfully been used to explain language acquisition, change, and variation. How the theory can be applied in foreign language teaching, however, has only recently attracted scholarly attention (De Knop and Gilquin 2016; Boas 2022). As part of this enterprise, the Constructionist Approach to Syntactic Analysis (CASA; Herbst and Hoffmann 2018, 2024) offers a new model that enables language teachers and students to analyse the most complex naturally occurring sentences. In the present paper, I will provide a short introduction to the CASA model. In addition to this, I will illustrate how this approach not only allows for a descriptive analysis of sentences, but how it offers teachers and students important theoretic insights into the cognitive nature of language.

A reference constructicon as a database

Abstract This article explores the importance of database architecture in building a reference constructicon. Several reference constructicons have been implemented for various languages (FrameNet Constructicon, English Constructicon, Japanese FrameNet Constructicon, German Constructicon, Swedish Constructicon, FrameNet Brasil Constructicon, Russian Constructicon). However, most constructicon projects do not provide explicit information about their implementation or the underlying data model. A recent reference constructicon project dealing mainly with the English language is CASA (Construction Grammar Approach to Syntactic Analysis). In this paper, we present the data model underlying CASA in detail and its current implementation as a relational database and web interface. Relational databases are widely used for many different applications and allow large amounts of data to be stored and managed in a structured and efficient way.

Artificial Neural Network-Powered, Driverless Vehicle Concept Development

Autonomous cars are now possible due to significant advances in robotics and intelligent control systems. Before these vehicles can safely operate in traffic and other hostile environments, there are many navigation, vision, and control issues. We want techniques that are both cost-effective and efficient, so that the field of research and academia may fully embrace self-driving cars. Within this scenario, we need something that can convert people to autonomous automobiles and include existing vehicles so that academics and explorers can access them. This study proposes a flexible mechanical layout that can be assembled in a short time and installed in most modern automobiles; it can also be used as a stepping stone in the development of autonomous vehicles. Using various actuators, conventional automobiles can be converted into autonomous vehicles. In the context of motor vehicle automation, motors are often used as actuators. In addition to motors, a pneumatic system was developed to automate the predetermined steps. An autonomous vehicle's mechanical arrangement is crucial, and it must be regularly updated and built to be robust in the face of dynamic conditions. We re-implemented two additional convolutional neural networks in an effort to conduct an objective test of their proposed network and compare our system's structure, technical complexity, and performance test during autonomous driving with theirs. This predicted network is around 250 times larger than the Alex Net network and four times larger than Pilot Net after training. Although the complexity and measurement of the publication's system are lower than other models that contribute lower latency and greater speed throughout inference, the operation was claimed by our system, which achieved autonomous driving with an equivalent efficacy as that achieved with two other models. The projected deep neural system reduces the need to infer ultra-fast computational hardware. This is important for cost efficiency, scale, and cost.

Spending and wasting time: a semantic and syntactic analysis of time as a (metaphorical) resourc

Spending and wasting time: a semantic and syntactic analysis of time as a (metaphorical) resourc

A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation Tasks

Human annotations are vital to supervised learning, yet annotators often disagree on the correct label, especially as annotation tasks increase in complexity. A common strategy to improve label quality is to ask multiple annotators to label the same item and then aggregate their labels. To date, many aggregation models have been proposed for simple categorical or numerical annotation tasks, but far less work has considered more complex annotation tasks, such as those involving open-ended, multivariate, or structured responses. Similarly, while a variety of bespoke models have been proposed for specific tasks, our work is the first we are aware of to introduce aggregation methods that generalize across many, diverse complex tasks, including sequence labeling, translation, syntactic parsing, ranking, bounding boxes, and keypoints. This generality is achieved by applying readily available task-specific distance functions, then devising a task-agnostic method to model these distances between labels, rather than the labels themselves. This article presents a unified treatment of our prior work on complex annotation modeling and extends that work with investigation of three new research questions. First, how do complex annotation task and dataset properties impact aggregation accuracy? Second, how should a task owner navigate the many modeling choices in order to maximize aggregation accuracy? Finally, what tests and diagnoses can verify that aggregation models are specified correctly for the given data? To understand how various factors impact accuracy and to inform model selection, we conduct large-scale simulation studies and broad experiments on real, complex datasets. Regarding testing, we introduce the concept of unit tests for aggregation models and present a suite of such tests to ensure that a given model is not mis-specified and exhibits expected behavior. Beyond investigating these research questions above, we discuss the foundational concept and nature of annotation complexity, present a new aggregation model as a conceptual bridge between traditional models and our own, and contribute a new general semisupervised learning method for complex label aggregation that outperforms prior work.