Textual Patterns Research Articles

X-raying Human Rights Violations by Government Agencies in Nigeria's Democratic System of Government

Respect for human rights is diminishing within Nigeria's democracy, with the political elite often acting with impunity and ignoring the rule of law. Gender discrimination and exclusion of marginalized citizens persist in politics. This study investigated human rights violations by government agencies in Nigeria, with the aim of enhancing understanding of the situation and proposing policy recommendations for promoting human rights. The study employed a descriptive survey research design, with 150 individuals randomly selected from government agencies in Lagos state. Data was collected through in-depth interviews, with the analysis focusing on patterns in conversations, text, and activities. The findings reveal the emotional and systemic impacts of government agencies' human rights violations, calling for sweeping reforms and enhanced accountability. Respondents also stress the complexity of accountability challenges and advocate for a collaborative approach. They offer valuable strategies, including government reforms, civil society involvement, education and awareness, learning from successful examples, and using media and digital platforms to promote human rights and accountability. The study concluded that the findings underscore the urgent need for comprehensive and collaborative efforts to address the multifaceted challenges of human rights violations in Nigeria. This requires the involvement of all stakeholders, including the government, civil society organizations, and the media. Sweeping reforms and enhanced accountability mechanisms are essential to ensure justice and the protection of human rights for all Nigerians. This study provides a valuable overview of human rights violations by government agencies in Nigeria's democratic system of government. The findings highlight the need for urgent action to address the root causes of these violations and to ensure accountability for perpetrators. The study's recommendations offer a roadmap for promoting human rights and strengthening democracy in Nigeria.

Gender characterization in Lady Windermere’s Fan and its Chinese translations: A corpus stylistic approach

This study examines gender representation in Oscar Wilde’s comedy and satire, Lady Windermere’s Fan (1892), using corpus stylistic analysis. Specifically, it analyzes gender characterization patterns in the original drama and explores how these patterns shift in two Chinese translations: Shen Xingren’s translation in 1918 and Hong Shen’s translation in 1923. By analyzing keyword patterns, collocational patterns, and characterization cues, the study reveals the intricate nature of gender characterization in the source text. Subsequently, a comparison is made between the textual patterns of the source text and their manifestations in the translations. The findings indicate that while Shen Xingren’s translation quite faithfully (re)represents the gender images and relations of the source text, Hong Shen’s selective appropriation of women and men characterization in his translation not only suppresses the source text’s potential to challenge moral absolutism towards women but also undermines the voices of women present in the original text. The article suggests that the re-representation or shifts in gender characterization observed between the source and target texts can be attributed to the translator’s ideology and adherence to particular poetics.

Pragmatically oriented linguistic units in advertising of religious tours

INTRODUCTION. The relevance of the study is related to the study of the features of the implementation of tourist advertising in such a dynamically developing real segment as religion. The role of tourist advertising is increasing, and it is becoming especially important to study specific pragmatic oriented language units devoted to religious discourse. The purpose of the study is to identify lexical, morphological units and stylistic devices used in tourist religious discourse.MATERIALS AND METHODS. The research material was advertising texts promoting religious tours on the Internet. The collection and writing of factual material was carried out using conceptual analysis, interpretive analysis was used to determine and explain the linguistic features of religious advertising text.RESULTS AND DISCUSSION. The appeal to advertising texts promoting religious tours allowed to identify pragmatically oriented linguistic units. Religious discourse, oriented towards introducing a person to faith within the framework of a certain confession, contributes to the implementation of religious tours (pilgrimage, educational and pilgrimageeducational). Religious advertising discourse in the field of tourism can be oriented towards the discourses of consumer society, but at the same time give them new, religious and sacred outlines.CONCLUSION. The identified lexical units and stylistic means in tourist advertisements on the topic of religious tours allow us to consider that religious tourist advertisement has a number of characteristics inherent only to it and occupies its own special place among currently existing types of discourse. These advertisements can be considered special speech patterns of text aimed at implementing the pragmatic function of advertising messages.

Anthropocentrism in Contemporary Chinese Dictionary: A Synergic Perspective of Critical Lexicographical Discourse Studies and Systemic Functional Linguistics

Abstract Critical Lexicographical Discourse Studies (CLDS) view the dictionary as a discourse and emphasize an ideological, ethico-political dimension to the lexicographical process. Combining CLDS with Systemic Functional Linguistics, this paper looks into how anthropocentrism is manifested linguistically in Contemporary Chinese Dictionary (Seventh Edition), the most influential authoritative general-purpose Chinese dictionary, by examining the transitivity and appraisal patterns in the definition texts concerning animals and plants. The analysis shows that animals and plants are seldom assigned agency in their own right and that the animals and plants are evaluated either positively or negatively according to whether they are useful to the human beings while their intrinsic value is ignored. In particular, animals are highlighted for their agricultural and medicinal use and plants for their medicinal and ornamental value. However, compared with its previous editions, the dictionary has shown an enhanced awareness of environmental protection by deleting the encyclopaedic information that describes the use of some rare animals’ meat, bones and fur. It is argued that, although modern general-purpose dictionaries are increasingly corpus-based and descriptive, their role in shaping social values and stances cannot be disregarded. Dictionary makers need to balance what words mean with what they can be said to mean or be allowed to mean (Moon, 2014). The study contributes to unmasking and denaturalizing the hidden ideology in colony texts such as the dictionary, enhances a critical use of dictionaries and helps develop the theory of critical lexicography and emerging ecolexicography.

Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study.

Suicide stands as a global public health concern with a pronounced impact, especially in low- and middle-income countries, where it remains largely unnoticed as a significant health concern, leading to delays in diagnosis and intervention. South Asia, in particular, has seen limited development in this area of research, and applying existing models from other regions is challenging due to cost constraints and the region's distinct linguistics and behavior. Social media analysis, notably on platforms such as Facebook (Meta Platforms Inc), offers the potential for detecting major depressive disorder and aiding individuals at risk of suicidal ideation. This study primarily focuses on India and Bangladesh, both South Asian countries. It aims to construct a predictive model for suicidal ideation by incorporating unique, unexplored features along with masked content from both public and private Facebook profiles. Moreover, the research aims to fill the existing research gap by addressing the distinct challenges posed by South Asia's unique behavioral patterns, socioeconomic conditions, and linguistic nuances. Ultimately, this research strives to enhance suicide prevention efforts in the region by offering a cost-effective solution. This quantitative research study will gather data through a web-based platform. Initially, participants will be asked a few demographic questions and to complete the 9-item Patient Health Questionnaire assessment. Eligible participants who provide consent will receive an email requesting them to upload a ZIP file of their Facebook data. The study will begin by determining whether Facebook is the primary application for the participants based on their active hours and Facebook use duration. Subsequently, the predictive model will incorporate a wide range of previously unexplored variables, including anonymous postings, and textual analysis features, such as captions, biographic information, group membership, preferred pages, interactions with advertisement content, and search history. The model will also analyze the use of emojis and the types of games participants engage with on Facebook. The study obtained approval from the scientific review committee on October 2, 2023, and subsequently received institutional review committee ethical clearance on December 8, 2023. Our system is anticipated to automatically detect posts related to depression by analyzing the text and use pattern of the individual with the best accuracy possible. Ultimately, our research aims to have practical utility in identifying individuals who may be at risk of depression or in need of mental health support. This initiative aims to enhance engagement in suicidal ideation medical care in South Asia to improve health outcomes. It is set to be the first study to consider predicting participants' primary social application use before analyzing their content to forecast behavior and mental states. The study holds the potential to revolutionize strategies and offer insights for scalable, accessible interventions while maintaining quality through comprehensive Facebook feature analysis. DERR1-10.2196/55511.

A comparative study of thematic choices and thematic progression patterns in human-written and AI-generated texts

By adopting systemic functional grammar as the theoretical framework, the present research investigates the choices of the textual, the interpersonal, the topical Themes, and thematic progression patterns in human-written and AI-generated argumentative texts to examine how the human author and the robot organize the clause and text. The findings suggest that human-written and AI-generated texts differ significantly in using the subtypes of textual, interpersonal, and marked topical Themes. In terms of textual Themes, the machine tends to use more concession signals to repeat information that has been stated earlier, adopt fewer condition signals since the robot may be less likely to imagine possible or impossible situations, and repetitively rely on clause-initial conjunctive adjuncts of addition to extend the clause to the previous text. As for interpersonal Themes, the robot seldom adopts modal adjuncts or modal verbal operators as interpersonal Themes, which may suggest its lack of awareness of interaction with the reader and its avoidance of expressing its viewpoints in a typical or congruent way. Regarding topical Themes, the less use of marked themes in AI-generated texts demonstrates that AI may less carefully plan the development of the text to foreground the setting or construct coherent text. Considering thematic progression patterns, the frequent use of the constant pattern in AI-generated texts prevents the text from development and makes the text redundant and simplistic like a list of ideas.

ANALISIS POLA KALIMAT PADA DONGENG MUNI PENIUP TEROMPET KARYA ENDANG FIRDAUS

A sentence is the smallest unit of language, in oral or written form, and capable of expressing a complete thought. Fairy tales are fictional stories that not only contain moral values, but are also made to entertain. This research was conducted with the aim of analyzing the sentence patterns in the fairy tale “Muni Peniup Terompet” by Endang Firdaus using descriptive qualitative research methods. In this research, secondary data was used and data collection was carried out using observation and analysis methods, then carrying out reading and note-taking techniques. The results of this research contain sentence patterns in the fairy tale texts being analyzed. The sentence patterns found are: 1) S and P; 2) S-P and O; 3) S-P-O and K; 4) K-S-P and O; and 5) K-S-P and K. Keywords: Sentence, Sentence Patterns, Fairy Tale

Transformer-Based Regression Models for Assessing Reading Passage Complexity: A Deep Learning Approach in Natural Language Processing

Natural Language Processing (NLP) is a vital area in deep learning, widely applied in tasks like text classification, virtual assistants, speech recognition, and autocorrect features in digital devices. It allows machines to understand and generate human language, enhancing user interactions with software. This paper presents a deep learning model using the Transformer architecture for a regression task to predict the complexity of reading passages based on text excerpts. By leveraging the Transformer’s capability to identify complex patterns in text, the model achieves a relative error rate of about 10%. The paper also examines how different architectural choices influence model performance, focusing on one-hot encoding and embeddings. While one-hot encoding provides a simple text representation, embeddings offer a richer, more nuanced understanding of word relationships. The findings highlight the significance of model design and data representation in optimizing NLP tasks, providing insights for future advancements in the field.

Analyzing deep textual facial patterns for human pain sentiment recognition system in smart healthcare framework

BACKGROUND: Patient sentiment analysis aids in identifying issue areas, timely remediation, and improved patient care by the healthcare professional. The relationship between pain management and patient sentiment analysis is crucial to providing patients with high-quality medical care. Therefore, a self-reported pain level assessment is required for the smart healthcare framework to determine the best course of treatment. OBJECTIVE: An efficient method for a pain sentiment recognition system has been proposed based on the analysis of human facial emotion patterns of patients in the smart healthcare framework. METHODS: The proposed system has been implemented in four phases: (i) in the first phase, the facial regions of the observation patient have been detected using the computer vision-based face detection technique; (ii) in the second phase, the extracted facial regions are analyzed using deep learning based feature representation techniques to extract discriminant and crucial facial features to analyze the level of pain emotion of patient; (iii) the level of pain emotions belongs from macro to micro facial expressions, so, some advanced feature tunning and representation techniques are built along with deep learning based features such as to distinguish low to high pain emotions among the patients in the third phase of the implementation, (iv) finally, the performance of the proposed system is enhanced using the score fusion techniques applied on the obtained deep pain recognition models for the smart healthcare framework. RESULTS: The performance of the proposed system has been tested using two standard facial pain benchmark databases, the UNBC-McMaster shoulder pain expression archive dataset and the BioVid Heat Pain Dataset, and the results are compared with some existing state-of-the-art methods employed in this research area. CONCLUSIONS: From extensive experiments and comparative studies, it has been concluded that the proposed pain sentiment recognition system performs remarkably well compared to the other pain recognition systems for the smart healthcare framework.

Figurative and Motivational Situations of the «Saryanov» Corpus of Russian Poetry

Figurative-motivic situations combine the consideration of frequency named entities with the motives assigned to them, which are repeated by different authors. This is how the patterns of the local text are formed. In this context, the figurative-motivic situations associated with the name of the artist Martiros Saryan are considered as one of the patterns of the Armenian text of Russian poetry. In the «Saryan» corpus collected by the authors of the article, two main figurative situations were identified: 1) epiphany, comprehension. It is implemented in two versions: a) the lyrical subject comprehends Saryan's work after seeing Armenia; b) the lyrical subject comprehends Armenia after getting acquainted with Saryan's work. This situation of comprehension is semantically central in the poems, because it illustrates the inner transformation of the lyrical subject, his transformation; 2) aging. It is realized simultaneously in two versions, projecting on life and on art: a) Saryan person grows old, he is mortal; 2) Saryan-creator, his art are timeless, immortal. This is a universal lyrical situation that develops eternal themes of life, death and art. Only the first situation refers to the formation of the Armenian text, emphasizing the necessary participation of Saryan in the comprehension of Armenia by a lyrical subject.

Contrasting Linguistic Patterns in Human and LLM-Generated News Text

We conduct a quantitative analysis contrasting human-written English news text with comparable large language model (LLM) output from six different LLMs that cover three different families and four sizes in total. Our analysis spans several measurable linguistic dimensions, including morphological, syntactic, psychometric, and sociolinguistic aspects. The results reveal various measurable differences between human and AI-generated texts. Human texts exhibit more scattered sentence length distributions, more variety of vocabulary, a distinct use of dependency and constituent types, shorter constituents, and more optimized dependency distances. Humans tend to exhibit stronger negative emotions (such as fear and disgust) and less joy compared to text generated by LLMs, with the toxicity of these models increasing as their size grows. LLM outputs use more numbers, symbols and auxiliaries (suggesting objective language) than human texts, as well as more pronouns. The sexist bias prevalent in human text is also expressed by LLMs, and even magnified in all of them but one. Differences between LLMs and humans are larger than between LLMs.

Utilizing language models for advanced electrocardiogram analysis

Electrocardiography (ECG) signals are often referred to as the language of the heart and have been widely utilized for diagnosing various heart ailments, particularly arrhythmias. Consequently, numerous machine learning models have been employed to automatically detect heart disorders using ECG signals. In this research, the primary objective is to detect arrhythmias using a center-symmetric self-organized textual pattern. A novel feature engineering model has been introduced, which includes the following components: (i) multilevel feature extraction using the proposed center-symmetric self-organized textual pattern (CSSOTP), (ii) iterative neighborhood component analysis (INCA), and (iii) classification using k-nearest neighbors (kNN) with 10-fold cross-validation. During the feature extraction phase, a multilevel feature extraction model incorporating maximum absolute pooling (MAP) and the proposed CSSOTP was applied. The CSSOTP was designed to select the most appropriate pattern for the given data block. A large language model (LLM) was leveraged to generate text for creating patterns, and ChatGPT was used to assist in text generation. To identify the most informative features, the INCA feature selector was employed, and the selected features were subsequently classified using the kNN classifier. An impressive 96.20 % classification accuracy was achieved by the CSSOTP-based feature engineering model when tested on an ECG dataset containing 17 classes. Furthermore, comparisons with state-of-the-art feature engineering models were conducted, demonstrating that the proposed model has superior classification capabilities.

Textsortenspezifische Abhängigkeit der Schreibflüssigkeit bei Grundschulkindern

Abstract An essential prerequisite for a fully developed writing ability is the effortless mastery of lower-level writing skills. These include, in particular, writing fluency, since fluent writing frees up sufficient cognitive capacity for other demanding higher-order processes of text production, such as planning and revising a text or tailoring it to a particular audience. Despite its relevance, writing fluency – in contrast to reading fluency – has so far only played a minor role in both writing research and classroom practice. This article first defines the construct of writing fluency and then elaborates on its two components: transcription fluency and text generation fluency. While transcription fluency is fairly well defined in the existing literature, text generation fluency remains underspecified, particularly from a linguistic point of view. How ideas are fluently translated into language is not sufficiently addressed by cognitive models of writing processes. It remains unclear which linguistic knowledge is most relevant for writing processes; what is known is that part of it is knowledge of genre-specific text patterns. Within the framework of an explorative empirical study, the influence of different text genres on the text generation fluency of primary school students was investigated to shed more light on this component. We found that text genre has an influence on text generation fluency, in particular in more competent writers, whereas there appears to be no such influence in weaker writers. The latter can be explained by the fact that in texts of weaker writers, the content appears to have been written more associatively and that there are hardly any genre-specific patterns and linguistic elements irrespective of the text genre they were asked to produce.

Interdisciplinary Applications of AI: Dermatology and Psychology

Artificial Intelligence (AI) is profoundly transforming interdisciplinary domains like dermatology and psychology, providing revolutionary possibilities in the areas of diagnosis, treatment, and patient care. AI-driven solutions in dermatology are improving diagnostic accuracy by analyzing medical photos to identify skin disorders such as melanoma with a level of precision comparable to that of professional dermatologists. These technologies utilize machine learning algorithms that have been trained on extensive datasets to detect minor patterns and anomalies in skin lesions. This enables the early detection of skin conditions and the creation of personalized treatment strategies. Moreover, artificial intelligence (AI) assists in optimizing the efficiency of workflows, minimizing diagnostic inaccuracies, and enhancing the availability of dermatological treatment, particularly in places with limited access to healthcare services. Within the field of psychology, artificial intelligence (AI) has a significant influence. It is utilized to assess individuals' mental well-being by analyzing their text and speech patterns, employing techniques such as natural language processing and sentiment analysis. Artificial intelligence algorithms have the capability to observe and analyze patient interactions, offering immediate and accurate observations regarding emotional states and possible psychological illnesses. The incorporation of AI enables more precise evaluations and prompt actions, enhancing conventional therapeutic methods. In addition, chatbots and virtual therapists that utilise artificial intelligence provide instant assistance and ongoing supervision, thereby extending the accessibility of mental health care. The integration of AI with dermatology and psychology not only improves diagnostic skills and treatment results but also highlights the significance of interdisciplinary collaboration in advancing healthcare. By using artificial intelligence (AI), these industries can utilize data-driven analysis to provide more accurate, tailored, and easily accessible healthcare, thereby enhancing patient results and quality of life.

FORMICARY SWARM OPTIMIZED DEEP CNN FOR FACIAL EMOTION RECOGNITION FROM HUMAN FACIAL EXPRESSIONS

Facial emotion recognition (FER) is a dominant research area that captures the biological facial features and matches the facial data with existing databases to analyze the individual’s emotional state. Numerous techniques have been formulated for attaining effective FER. However, the occlusions, different head positions, deformed faces, motion blur under unrestricted settings, and complicated backgrounds make it complex to analyze the facial images. In this paper, the formicary swarm optimization-based deep convolutional neural network (FSO-opt DCNN) model is utilized for facial emotion detection for which JAFFE and the RAVDESS facial expression datasets are used. DCNNs are proficient with built-in feature extraction strategies from images to map the various facial expressions to the corresponding emotional states adopted for effective FER. In addition, the intensity, directional, and edge patterns as well as the correlation features extracted utilizing the hybrid textual pattern, RESNET 101 and VGG 16-based correlation modules assist the DCNN to attain the informative feature from the high-resolution images. Further, the formicary swarm optimization (FSO) is incorporated that effectively tunes the DCNN to capture the complex relationships between the learned features that excel the FER capability. Evaluating the metrics, the face recognition model using the RAVDESS dataset achieves notable efficiencies during a training percentage (TP) of 90%, with values of 97.51%, 95.48%, 99.55%, 95.48%, 97.48%, 96.47%, and a minimum loss of 2.49%. Simultaneously, the emotion detection model demonstrates robust efficiencies with metric values of 96.75%, 98.49%, 95.01%, 98.49%, 96.72%, 97.59%, and a minimum loss of 3.25%. Finally, the obtained results reveal the efficacy of the FSO-opt DCNN, particularly in face recognition tasks, as it outperforms the existing models across various datasets, showcasing its versatility and potential in facial analysis applications.

Textual Patterns and Virality in X: An Analysis of Engagement in Telenovela Posts

X, previously known as Twitter, boasts 556 million active users and is widely used by businesses to engage with their audiences. In our study, we focused on TV Globo's telenovela "Terra e Paixão" broadcast in 2023, to analyze the impact of textual patterns on post virality using natural language processing techniques. Techniques like sentiment analysis, Part-Of-Speech Tagging, reinforcement scoring, TF-IDF, semantic similarity, and cosine similarity were utilized to identify attributes that contribute to a post's success, aiming to enhance marketing strategies. We employed language models like BERT, RoBERTa, and e5 in our analysis. Our findings indicate that while various metrics affect post engagement, the challenge remains complex. Textual characteristics, although essential, do not fully explain a publication's popularity, underscoring the need for a multifaceted approach to understanding social media dynamics.

A Knowledge-Driven Approach for Automatic Semantic Aspect Term Extraction Using the Semantic Power of Linked Open Data

Aspect-Based Sentiment Analysis (ABSA) is a crucial process for assessing customer feedback and gauging satisfaction with products or services. It typically consists of three stages: Aspect Term Extraction (ATE), Aspect Categorization Extraction (ACE), and Sentiment Analysis (SA). Various techniques have been proposed for ATE, including unsupervised, supervised, and hybrid methods. However, many studies face challenges in detecting aspect terms due to reliance on training data, which may not cover all multiple aspect terms and relate semantic aspect terms effectively. This study presents a knowledge-driven approach to automatic semantic aspect term extraction from customer feedback using Linked Open Data (LOD) to enrich aspect extraction outcomes in the training dataset. Additionally, it utilizes the N-gram model to capture complex text patterns and relationships, facilitating accurate classification and analysis of multiple-word terms for each aspect. To assess the effectiveness of the proposed model, experiments were conducted on three benchmark datasets: SemEval 2014, 2015, and 2016. Comparative evaluations with contemporary unsupervised, supervised, and hybrid methods on these datasets yielded F-measures of 0.80, 0.76, and 0.77, respectively.

Language Choices and Digital Identity of High School Student Text Messages in the New Capital City of Indonesia: Implication for Language Education

This research investigates the language choices and digital identities represented in text messages exchanged by high school students in the New Capital City of Indonesia (Ibu Kota Nusantara/IKN) and the implications of these linguistic practices for language teaching. Using a corpus of messages transmitted via WhatsApp collected from 100 high school students, this study produced 2.1 million tokens of 83,414 word types. This study uses corpus linguistic analysis to investigate the distinctive features of language usage, lexical changes, and communication trends in the digital discourse of IKN high school students. Data analysis utilizes features from AntConc corpus tools, including word lists, collocations, concordances, and N-grams. Concerning the features uncovered the subtle nuances that contribute to the unique linguistic identity of the student authors. This research highlights how digital language patterns attribute individual identity within the context of IKN high school students. The data analysis discovers three linguistic patterns in the electronic texts produced by IKN High School students, reflecting their general digital identity as language users in IKN, namely (1) lexical choice, (2) orthographic selection, and (3) lexical bundles. Furthermore, the study reveals the complex construction of digital identity through language, with students negotiating social positions, connections, and personal identities via their linguistic choices in electronic texts. The research findings contribute to the implications of language teaching in the IKN area and a deeper understanding of how students within the IKN area express themselves through language in the virtual realm, thus shaping their digital identities.

Elements of Timed Pattern Matching

The rise of machine learning and cloud technologies has led to a remarkable influx of data within modern cyber-physical systems. However, extracting meaningful information from this data has become a significant challenge due to its volume and complexity. Timed pattern matching has emerged as a powerful specification-based runtime verification and temporal data analysis technique to address this challenge. In this paper, we provide a comprehensive tutorial on timed pattern matching that ranges from the underlying algebra and pattern specification languages to performance analyses and practical case studies. Analogous to textual pattern matching, timed pattern matching is the task of finding all time periods within temporal behaviors of cyber-physical systems that match a predefined pattern. Originally we introduced and solved several variants of the problem using the name of match sets, which has evolved into the concept of timed relations over the past decade. Here we first formalize and present the algebra of timed relations as a standalone mathematical tool to solve the pattern matching problem of timed pattern specifications. In particular, we show how to use the algebra of timed relations to solve the pattern matching problem for timed regular expressions and metric compass logic in a unified manner. We experimentally demonstrate that our timed pattern matching approach performs and scales well in practice. We further provide in-depth insights into the similarities and fundamental differences between monitoring and matching problems as well as regular expressions and temporal logic formulas. Finally, we illustrate the practical application of timed pattern matching through two case studies, which show how to extract structured information from temporal datasets obtained via simulations or real-world observations. These results and examples show that timed pattern matching is a rigorous and efficient technique in developing and analyzing cyber-physical systems.

Conceptual Analysis through Corpus Text Patterns: Researching W.E.B. Du Bois’ Concept of Democratic Despotism via Regular Expressions

Conceptual Analysis through Corpus Text Patterns: Researching W.E.B. Du Bois’ Concept of Democratic Despotism via Regular Expressions