Linguistic Rules Research Articles

Resisting Linguistic Rules in French-Australian Writing

Recent scholarship has posited the experience of migration as a source of creative, experimental possibilities that allow writers to contest fixed forms of identity; it has also questioned monolingual, monocultural understandings of national literatures that yoke one language to one nation. Building on such work, this article considers French migrant writing that breaks linguistic rules and challenges the norms of national literatures by analyzing various attitudes testifying to multilingualism and linguistic differences in the works of Paul Wenz, Didier Coste and Catherine Rey—authors who had embarked upon their writing careers before migrating, who have settled in Australia and who write from a position of stability and permanence. While travel writers use English to nuance their texts about journeys through Anglophone regions, they ultimately do not displace the primary importance of French in their texts. By contrast, the texts of the writers considered herein articulate both unique understandings of linguistic identity and resistance to linguistic fixity as well as innovative narrative strategies to communicate both.

Speaker Naming in Arabic TV Programs

Automatic speaker identification is the problem of identifying speakers by their real identities. Previous approaches use textual information as a source of naming, try to associate names to neighbouring speaker segments using linguistic rules. However, these approaches have a few limitations that hinder their application on spoken text. Deep learning approaches for natural language processing have recently reached state-of-the-art results. However, deep learning requires a lot of annotated data which is difficult to obtain in the case of speaker identification task. In this paper, we present two contributions towards integrating deep learning for identifying speakers in news broadcasts: first we realise a dataset in which the names of mentioned speakers are related to the previous, next, current or other speaker turns. Moreover, we present our approach to solve the problem of speaker identification using information obtained from the transcription. We use a Long-term Recurrent Convolutional Network for name assignment and integer linear programming for name propagation into the different segments. We evaluate our model on both assignment and propagation tasks on the test part of the Arabic multi-genre broadcast dataset which consists of 17 TV programs from Aljazeera. The performance is analysed using the evaluation metrics, such as Estimated Global Error Rate (EGER) and Diarization Error Rate (DER). The outcome of the proposed method ensures better performance by achieving the lower EGER of 32.3% and DER of 8.3%.

Implementation of a fuzzy controller to tune the response of a waveguide cavity filter

This paper demonstrates the utility of a modern technique to tune the frequency response of a microwave bandpass cavity filter as a complex nonlinear system with a new type of controller. The considered plant in the regulation process is a 4th order cavity filter with four resonators operating at a resonance frequency of 916 MHz, with a fixed return loss quantity of 21dB. Yet our method is based on the fuzzy logic principle, by retrieving and recalling the knowledge of microwave experts and duplicating it through linguistic rules to achieve the objective and optimize the reflection coefficient of our filter. The regulation process will be effectuated by using seven tuning screws, the insertion of these screws will create a discontinuity at the insertion position. Meanwhile, adjusting the discontinuities depth of penetration will lead to the variation of the frequency response of the considered structure. The fuzzy controlling algorithm will actuate on the screws to achieve the target.

AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text

Adversarial examples are vital to expose vulnerability of machine learning models. Despite the success of the most popular word-level substitution-based attacks which substitute some words in the original examples, only substitution is insufficient to uncover all robustness issues of models. In this paper, we focus on perturbations beyond word-level substitution, and present <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdvExpander</b> , a method that crafts new adversarial examples by expanding text. We first utilize linguistic rules to determine which constituents to expand and what types of modifiers to expand with. We then expand each constituent by inserting an adversarial modifier searched from a pre-trained CVAE-based generative model. To ensure that our adversarial examples are label-preserving for text matching, we also constrain the modifications with a heuristic rule. Experiments on three classification tasks verify the effectiveness of AdvExpander and the validity of our adversarial examples. AdvExpander is significantly more effective than sentence-level attack baselines and is complementary to previous word substitution-based attacks, thus promising to reveal new robustness issues.

Optimization of multipurpose reservoir system operation (Case study: Sefidrud and Shahryar reservoir dams)

Nowadays, effective water management becomes more vital all over the worlds. Due to the effect of climate change and population growth, reservoirs play a more important role in water resources management. Reservoirs can be used for multiple purposes such as irrigation, industrial water supply, hydropower generation, flood protection, water quality management, recreation and so on. In this paper, an optimized model have been considered and solved based on the goal programming method for the optimal operation of a multi-objective two reservoir systems in Sefidrud watershed. Release for irrigation demand and environment, flood controlling and recreation are represented as objectives. Then, to consider uncertainties and also for achieving the general method for reservoirs operation, because of the considerable advantages of linguistic rules in better inferring and interpretation of systems, an adaptive neuro based fuzzy inference system (ANFIS) approach is used to construct operation rules for these multipurpose reservoirs. The results of the ANFIS models show that they can be applied successfully to provide high accuracy for the management of the reservoirs system.

Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State Transducers

In this article we present the design and the development of a knowledge based computational linguistic tool, Mlphon [em.el.foːɳ] for Malayalam language. Mlphon computationally models linguistic rules using finite state transducers and performs multiple functions including grapheme to phoneme (g2p) and phoneme to grapheme (p2g) conversions, syllabification, phonetic feature analysis and script grammar check. This open source software tool, released under MIT license, is developed as a one-stop solution to handle different speech related text processing tasks for automatic speech recognition, text to speech synthesis and non-speech natural language processing tasks including syllable subword based language modeling, phoneme diversity analysis and text sanity check. The tool is evaluated on a manually crafted gold standard lexicon. Mlphon performs orthographic syllabification with 99% accuracy with a syllable error rate of 0.62% on the gold standard lexicon. For grapheme to phoneme conversion task, overall phoneme recognition accuracy of 99% with a phoneme error rate of 0.55% is obtained on gold standard lexicon. Additionally an extrinsic evaluation of Mlphon is performed by employing the pronunciation lexicon created using Mlphon, in Malayalam automatic speech recognition (ASR) task. Performance analysis in terms of the computation time of lexicon creation process and the word error rate (WER) on ASR task are presented along with a comparison over other automated tools for lexicon creation. Pronunciation lexicons with more than 100k commonly used Malayalam words in phonemised and syllabified forms is created and they are published as open language resources along with this work. We also demonstrate the usage of Mlphon on different natural language processing applications - syllable subword ASR, assisted pronunciation learning, phoneme diversity analysis and text sanity check. Being a knowledge based solution with open source code, Mlphon can be adapted to other languages of similar script nature.

Automatic Eyewitness Identification During Disasters by Forming a Feature-Word Dictionary

Social media provide digitally interactional technologies to facilitate information sharing and exchanging individuals. Precisely, in case of disasters, a massive corpus is placed on platforms such as Twitter. Eyewitness accounts can benefit humanitarian organizations and agencies, but identifying the eyewitness Tweets related to the disaster from millions of Tweets is difficult. Different approaches have been developed to address this kind of problem. The recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic rules. However, these approaches suffer from limitations as they are dataset-dependent and not scalable. In this paper, we proposed a method to identify eyewitnesses from Twitter. To experiment, we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the eyewitness. Considering each feature, a dictionary of words was created with the Word Dictionary Maker algorithm, which is the crucial contribution of this research. This algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words dictionary. Further, keyword matching for each feature in tweets is performed. If a feature exists in a tweet, it is termed as 1; otherwise, 0. Similarly, for 13 features, we created a file that reflects features in each tweet. To classify the tweets based on features, Naïve Bayes, Random Forest, and Neural Network were utilized. The approach was implemented on different disasters like earthquakes, floods, hurricanes, and Forest fires. The results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure values. At the same time, the proposed approach gained a 0.88 value of F-measure. The results were comparable as the proposed approach is not dataset-dependent. Therefore, it can be used for the identification of eyewitness accounts.

REGARDING THE ACTIVITY OF A NATIONAL PARK DIRECTOR IN ENSURING THE SAFETY OF TOURISTS VISITING NATIONAL PARK AREAS

Purpose. The aim of the article is to analyse a national park director's legal position, roles, tasks and legal forms of operations in ensuring the safety of tourists visiting a national park. The objective of this analysis is also to determine whether the legal position, competencies and legal forms of activity at the disposal of the national park director are sufficient to ensure the safety of tourists visiting the national park. Method. The theoretical nature of this article determines the choice of research methods and their application. A dogmatic method (analytical and dogmatic) involving legal exegesis using linguistic and non-linguistic rules of legal interpretation is the predominant method applied in the article. Findings. The national park director’s legal status (including his/her position in the system of administering entities) is not clearly defined by the legislator and therefore, raises doubts. The legislature has not explicitly included this entity into the local authorities of consolidated and non-consolidated government administration. The legislator defines a national park director as a national park authority and a nature protection authority, directly indicating that this authority performs the tasks of a regional director aimed at nature protection within the national park area. The director of a national park may be classified as an administering entity, or on account of his/her tasks and powers, a public administration authority in a functional sense. However, it is misleading to treat national park directors as public administration authorities sensu stricto, i.e. the authorities who are part of the state machinery (authorities acting directly on behalf of the state or local self-governments), whose basic and, in principle, sole purpose is to perform public administration tasks (e.g. minister, province administrator, commune head). However, some authors consider national park directors to be public administration bodies sensu stricto [Makuch 2020, p. 527]. It has been confirmed in research that there is great diversity concerning tasks, powers and legal forms of operations at the disposal of a national park director, which can be used to ensure the safety of tourists visiting national parks. These are legal and factual activities of regulatory and non-regulatory nature. The tasks and competencies of national park directors include, first of all, protecting national park resources (environmental protection), which is the essence of their existence, and also providing access to national parks so as to ensure the safety of people who visit them. Research and conclusions limitations. The author focuses on analysis of the national legal framework. The origin of institutions and comparative legal analyses have been omitted. Practical implications. In the research, the current legal status is shown, and this can be considered the basis for further legislative work. Originality. To date, research on the national park directors' tasks, roles and legal forms of operation in ensuring the safety of tourists visiting national parks has been very scarce. Most of such issues are raised while discussing wider problems related to nature protection as well as tourism, and are not subject to in-depth examination [Wolski 2010, pp. 75-83]. In this context, it is worth noting that not only the national park directors' tasks and legal forms of activity require detailed analysis and evaluation, but their status in the state system and position in the system of administering entities as well. The current findings in this field are not sufficiently comprehensive and require further clarification. Type of paper. The article presents some theoretical concepts. It is a general overview article.

Taḥlīl Nuṣūsh al-Qirāah Min Kitāb Ta`līm al-Lughah al-`Arabiyyah Li al-Ṣaf al-Ṡāliṡ al-Mutawassiṭah Min Ḥaiṡ al-Lughah wa al-Ṡaqāfah al-Islāmiyyah `Alā al-Manhaj al-Dirāsī 2013

Overall, the goal of teaching Arabic at an intermediate level about religious ministry is to strengthen reading skill and increase religious knowledge. This is reflected in the planning of the Arabic subject at the intermediate level, so the reading texts must contain the two things mentioned, whether the application of the linguistic rules that have been studied or include religious knowledge in order to achieve the goal of teaching the Arabic language. The teaching and learning process needs the appropriate educational resources for the current curriculum. One of the educational resources is the textbook. The researcher found a book for teaching Arabic for the third intermediate grade, and this book is a model and was not printed for sale, so it can be fixed with suggestions and better analysis. According to this, the researcher analyzed the content of the book. This thesis aimed to identify the compatibility of book reading texts with the rules of arrangement and composing good study materials in terms of language, and to identify the compatibility of book reading texts with Islamic culture. The research tools used to obtain the desired data are observation and files. As for the result of this message, they are: 1). The reading texts found on the aforementioned book did not include the linguistic structure and this was not taken into account in every topic, 2). Of the five existing texts, four are texts that correspond to the Islamic culture or the teachings and creed of Islam, but one text needs to reform the content and the subject to conform to the established planning.

The Analysis of Hoax News Content on Facebook Reviewed from Theory of Critical Discourse Analysis and Linguistic Rules

The threat of hoax news on social media makes a news reader more alert. This case encourages someone to increase personal security so that someone is not trapped by leading opinions that are not related to the actual facts. This study aims to describe the analysis of hoax news content on facebook social media in terms of theory of critical discourse analysis and linguistic rules. A qualitative approach with descriptive method was used in this study. The data collection technique used a documentation study on three hoax news content published on facebook. The data analysis technique used triangulation which consisted of data reduction, data presentation, and conclusion drawing and verification. Based on the theory of critical discourse analysis, the results of the study showed that in the representation aspect, hoax news content did not show an appropriate representation between the images presented and the text written on the news. On the aspect of relations, hoax news content did not show an appropriate relationship to the news content. On the aspect of identity, hoax news content does not have a complete identity. In addition, based on the linguistic aspect, in the aspect of word writing, the majority of hoax news content writing used non-standard language. In the element of using capital letters, in hoax news content, it was found that there was an inconsistent use of capital letters. Moreover, on the element of using punctuation, on hoax news content it was found that there were many errors in the use of punctuation. The findings of this study can be used as a reference for news readers to identify news that is classified as hoax or not.&#x0D; Keywords: hoax news, critical discourse analysis, linguistic rules&#x0D; &#x0D; &#x0D; Abstrak&#x0D; Ancaman berita hoaks pada media sosial menjadikan seorang pembaca berita menjadi lebih waspada. Hal ini mendorong seseorang untuk meningkatkan keamanan diri pribadi agar tidak terjebak dengan penggiringan opini yang tidak berkaitan dengan fakta yang sebenarnya. Penelitian ini bertujuan untuk mendeskripsikan analisis konten berita hoaks pada media sosial facebook ditinjau dari teori critical discourse analysis dan kaidah kebahasaan. Pendekatan kualitatif dengan metode deskriptif digunakan pada penelitian ini. Adapun teknik pengumpulan data menggunakan studi dokumentasi pada tiga buah konten berita hoaks yang dipublikasikan di facebook. Teknik analisis data menggunakan triangulasi yang terdiri dari tahap reduksi data, penyajian data, serta penarikan simpulan dan verifikasi. Ditinjau dari Critical Discourse Analysis, hasil penelitian menunjukkan pada aspek representasi, konten berita hoaks tidak menunjukkan representasi yang sesuai antara gambar yang disajikan dengan teks yang ditulis pada berita tersebut. Pada aspek relasi, konten berita hoaks tidak menunjukkan relasi yang sesuai pada isi berita. Adapun pada aspek identitas, konten berita hoaks tidak memiliki identitas yang lengkap. Selain itu, jika ditinjau dari aspek kebahasaan, pada aspek penulisan kata, penulisan konten berita hoaks mayoritas menggunakan bahasa yang tidak baku. Pada unsur penggunaan huruf kapital, pada konten berita hoaks ditemukan adanya pemakaian huruf kapital yang tidak konsisten. Adapun pada unsur penggunaan tanda baca, pada konten berita hoaks ditemukan terdapat banyak kesalahan pada pemakaian tanda baca. Temuan penelitian ini dapat menjadi rujukan bagi pembaca berita untuk mengidentifikasi sebuah berita yang termasuk ke dalam berita hoaks maupun bukan.&#x0D; &#x0D; Kata kunci: berita hoaks, critical discourse analysis, kaidah kebahasaan&#x0D;

Leader in law theory: in memory of Alexander Fedorovich Cherdantsev

The article is dedicated to the memory of Professor A.F. Cherdantsev, a well-known Russian legal theorist. In the focus are creative periods of the scientist's life. Particular attention is paid to his contribution to the development of legal science with the emphasis on law interpretation, scientists achievements in the field of investigating epistemological nature, values (principles) of interpretation of legal norms, formulation of linguistic, systemic and other rules. The author's contribution to the development of methodology of law and his criticism of the integrative approach in jurisprudence have also been illustrated. The article analyzes the scholars development of the legal norm doctrine, its structure, technical and legal regulations, etc. His contribution to the development of educational literature is separately considered and highly appreciated. The tribute is also given to his individual scientific papers. It is proposed to systematize and republish Professor Cherdantsevs works.

Using finite-state machines to automatically scan Ancient Greek hexameter

Abstract This article presents a fully automatic approach to the scansion of Ancient Greek hexameter verse. In particular, the article describes an algorithm that uses deterministic finite-state automata and local linguistic rules to implement a targeted search for valid spondeus patterns and, in addition, a weighted finite-state transducer to correct and complete partial analyses and to reject invalid candidates. The article also details the results of an empirical evaluation of the annotation quality resulting from this approach on hand-annotated data. It is shown that a finite-state approach provides quick and linguistically sound analyses of hexameter verses as well as an efficient formalization of linguistic knowledge. The project code is available online.1

Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention.

Discovering new materials better suited to specific purposes is an important issue in improving the quality of human life. Here, a neural network that creates molecules that meet some desired multiple target conditions based on a deep understanding of chemical language is proposed (generative chemical Transformer, GCT). The attention mechanism in GCT allows a deeper understanding of molecular structures beyond the limitations of chemical language itself which cause semantic discontinuity by paying attention to characters sparsely. The significance of language models for inverse molecular design problems is investigated by quantitatively evaluating the quality of the generated molecules. GCT generates highly realistic chemical strings that satisfy both chemical and linguistic grammar rules. Molecules parsed from the generated strings simultaneously satisfy the multiple target properties and vary for a single condition set. These advances will contribute to improving the quality of human life by accelerating the process of desired material discovery.

A novel fuzzy sliding mode control approach for chaotic systems

The purpose of this paper is to study the stabilization problem for a class of uncertain chaotic systems against unknown dynamics and disturbances, based on fuzzy sliding mode controller approaches. To fulfill this aim, the first- and the second-orders sliding mode controllers and an adaptive variable universe fuzzy sliding mode controller are combined to a set of linguistic rules, to design some novel approaches for improving the performance of the control action and eliminating the chattering issue. The stability analysis of the closed-loop system is proved via the Lyapunov stability theorem, and also the convergence of the tracking error to zero in finite-time is guaranteed. The new proposed control laws contribute the control actions to outperform the conventional one in terms of chattering reduction and elimination, along with lessening in the reaching time. Moreover, some numerical simulations are provided to depict that the proposed control laws are not only robust with respect to uncertainties and external disturbances, which lead the system to the desired state, but also can significantly eliminate the chattering effect.

An HMM-Based PoS Tagger for Old Church Slavonic

Abstract We present a hybrid HMM-based PoS tagger for Old Church Slavonic. The training corpus is a portion of one text, Codex Marianus (40k) annotated with the Universal Dependencies UPOS tags in the UD-PROIEL treebank. We perform a number of experiments in within-domain and out-of-domain settings, in which the remaining part of Codex Marianus serves as a within-domain test set, and Kiev Folia is used as an out-of-domain test set. Analysing by-PoS-class precision and sensitivity in each run, we combine a simple context-free n-gram-based approach and Hidden Markov method (HMM), and added linguistic rules for specific cases such as punctuation and digits. While the model achieves a rather non-impressive accuracy of 81% in in-domain settings, we observe an accuracy of 51% in out-of-domain evaluation, which is comparable to the results of large neural architectures based on pre-trained contextual embeddings.

Remaining Useful Life Estimation from Event Data

Abstract—Prognostics aims to predict the degradation of equipment by estimating their remaining useful life (RUL) and/or the failure probability within a specific time horizon. The high demand of equipment prognostics in the industry have propelled researchers to develop robust and efficient prognostics techniques. Among data driven techniques for prognostics, machine learning and deep learning (DL) based techniques, particularly Recurrent Neural Networks (RNNs) have gained significant attention due to their ability of effectively representing the degradation progress by employing dynamic temporal behaviors. RNNs are well known for handling sequential data, especially continuous time series sequential data where the data follows certain pattern. Such data is usually obtained from sensors attached to the equipment. However, in many scenarios sensor data is not readily available and often very tedious to acquire. Conversely, event data is more common and can easily be obtained from the error logs saved by the equipment and transmitted to a backend for further processing. Nevertheless, performing prognostics using event data is substantially more difficult than that of the sensor data due to the unique nature of event data. Though event data is sequential, it differs from other seminal sequential data such as time series and natural language in the following manner, i) unlike time series data, events may appear at any time, i.e., the appearance of events lacks periodicity; ii) unlike natural languages, event data do not follow any specific linguistic rule. Additionally, there may be a significant variability in the event types appearing within the same sequence. Therefore, this paper proposes an RUL estimation framework to effectively handle the intricate and novel event data. The proposed framework takes discrete events generated by an equipment (e.g., type, time, etc.) as input, and generates for each new event an estimate of the remaining operating cycles in the life of a given component. To evaluate the efficacy of our proposed method, we conduct extensive experiments using benchmark datasets such as the CMAPSS data after converting the time-series data in these datasets to sequential event data. The event data conversion is carried out by careful exploration and application of appropriate transformation techniques to the time series. To the best of our knowledge this is the first time such event-based RUL estimation problem is introduced to the community. Furthermore, we propose several deep learning and machine learning based solution for the event-based RUL estimation problem. Our results suggest that the deep learning models, 1D-CNN, LSTM, and multi-head attention show similar RMSE, MAE and Score performance. Foreseeably, the XGBoost model achieve lower performance compared to the deep learning models since the XGBoost model fails to capture ordering information from the sequence of events.

Large-Scale System for Social Media Data Warehousing

Social media data become an integral part in the business data and should be integrated into the decisional process for better decision making based on information which reflects better the true situation of business in any field. However, social media data are unstructured and generated in very high frequency which exceeds the capacity of the data warehouse. In this work, we propose to extend the data warehousing process with a staging area which heart is a large scale system implementing an information extraction process using Storm and Hadoop frameworks to better manage their volume and frequency. Concerning structured information extraction, mainly events, we combine a set of techniques from NLP, linguistic rules and machine learning to succeed the task. Finally, we propose the adequate data warehouse conceptual model for events modeling and integration with enterprise data warehouse using an intermediate table called Bridge table. For application and experiments, we focus on drug abuse events extraction from Twitter data and their modeling into the Event Data Warehouse.

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

Execution tracing is a tool used in the course of software development and software maintenance to identify the internal routes of execution and state changes while the software operates. Its quality has a high influence on the duration of the analysis required to locate software faults. Nevertheless, execution tracing quality has not been described by a quality model, which is an impediment while measuring software product quality. In addition, such a model needs to consider uncertainty, as the underlying factors involve human analysis and assessment. The goal of this study is to address both issues and to fill the gap by defining a quality model for execution tracing. The data collection was conducted on a defined study population with the inclusion of software professionals to consider their accumulated experiences; moreover, the data were processed by genetic algorithms to identify the linguistic rules of a fuzzy inference system. The linguistic rules constitute a human-interpretable rule set that offers further insights into the problem domain. The study found that the quality properties accuracy, design and implementation have the strongest impact on the quality of execution tracing, while the property legibility is necessary but not completely inevitable. Furthermore, the quality property security shows adverse effects on the quality of execution tracing, but its presence is required to some extent to avoid leaking information and to satisfy legal expectations. The created model is able to describe execution tracing quality appropriately. In future work, the researchers plan to link the constructed quality model to overall software product quality frameworks to consider execution tracing quality with regard to software product quality as a whole. In addition, the simplification of the mathematically complex model is also planned to ensure an easy-to-tailor approach to specific application domains.

Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey

The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.

The Existence of Culture in its Relevance to the Dynamics of Globalization: Bahasa Indonesia Case Study

Language is present as a form of crystallization of the values of civilization and mediating and directing the orientation of the movement of civilization. Therefore, language becomes an important and interesting variable to be studied and revised scientifically for the preservation and progress of civilization. The Republic of Indonesia has an official language used by its citizens, namely Bahasa Indonesia. This language aims to unite the Indonesian people who are multicultural or have ethnic and cultural diversity so that no distance separates fellow Indonesian citizens from hundreds of tribes, according to the 1928 Youth Pledge Convention. The thing that must be realized now is the use of the Indonesian language which is getting weaker. The weakness is not that it is not used at all but that foreign terms are included in public discussion, and it tends to be considered more prestigious to be used. This study uses a descriptive qualitative method that refers to the sources of literature and the results of previous studies. The existence of Indonesian can decline when most Indonesians are happy and proud to use a foreign language that is considered more respectable and classier. However, the current condition of the existence of the Indonesian language is still in a safe stage. Bahasa Indonesia can continue to exist while preserving it by following the prevailing context and linguistic rules and developing its construction according to the needs and desires of civilization.