Textual Expression Research Articles

#IndonesiaRepresent: Investigating Nation Branding at International Fashion Events through the Presence of Social Media Influencers

Abstract This study presents the complex tapestry of Indonesia’s nation branding, by investigating the activities of its influencers at international fashion events. The research questions focus on the influencers, the use of national symbols, and online textual expression, in which visual and internet lexicons converge. By simultaneously assessing the resonance for local and international spectators, we identify the dominant themes and narratives promoted by these influencers at international fashion events. We adopt a theoretical perspective focused on the employment of young influencers in nation branding, and our results reveal a striking dichotomy. Although these charismatic personalities can increase the resonance of nation branding, they frequently veer off course and are occasionally diverted by the potential for achieving fame. This divergence reveals a problematic issue: the use of influencers might unintentionally damage the national story that their employers want them to present.

Mutually-Guided Hierarchical Multi-Modal Feature Learning for Referring Image Segmentation

Referring image segmentation aims to locate and segment the target region based on a given textual expression query. The primary challenge is to understand semantics from visual and textual modalities and achieve alignment and matching. Prior works have attempted to address this challenge by leveraging separately pretrained unimodal models to extract global visual and textual features, and perform straightforwardly fusion to establish cross-modal semantic associations. However, these methods often concentrate solely on the global semantics, disregarding the hierarchical semantics of expression and image, and struggling with complex and open real scenarios, thus failing to capture critical cross-modal information. To address these limitations, this paper introduces an innovative mutually-guided hierarchical multi-modal feature learning scheme. By leveraging the guidance of global visual features, the model mines hierarchical text features from different stages of the text encoder. Simultaneously, the guidance of global textual features is leveraged to aggregate multi-scale visual features. This mutually guided hierarchical feature learning effectively addresses the semantically inaccurate cause by free-form text and naturally occurring scale variations. Furthermore, a Segment Detail Refinement (SDR) module is designed to enhance the model’s spatial detail awareness through attention mapping of low-level visual features and cross-modal features. To evaluate the effectiveness of the proposed approach, extensive experiments are conducted on three widely used referring image object segmentation datasets. The results demonstrate the superiority of the presented method in accurately locating and segmenting objects in images.

Enhancing Middle-School Students’ Textual Expression: The Complementary Role of Speech-to-Text Technology

Literature on the impact of speech-to-text (STT) technology on text production is limited, with a predominant focus on students with learning disabilities. The widespread availability of STT suggests its potential integration into regular teaching practices; however, this remains understudied. The present study explored two conditions of narrative text production—STT and keyboarding—using a counterbalanced within-group design to measure text productivity, vocabulary, and sentence construction. Eighty-one middle-school students (Grades 4 and 5), typically from regular classrooms, participated in the study. The results revealed that students produced longer texts and in less time with STT than with keyboarding, and they used more diverse vocabulary. Sentence structures revealed an increase in punctuation and sentence length with STT. The text-based measurements were positively correlated; for instance, if students produced longer texts using the keyboard, they did so similarly with STT. In conclusion, young students can benefit from STT as a complement to traditional transcription, developing their narrative textual expression and reflective skills in knowledge acquisition. The implications of these results are discussed.

Automatic compliance checking of BIM models against quality standards based on ontology technology

Many countries have issued standards to stipulate the quality of BIM models. However, due to the complexity of BIM models and the textual expression of the standards, checking the quality of BIM models against the standards manually is challenging and time-consuming. In order to solve the problem, this paper presents an ontology-based approach for the automatic compliance checking of BIM models against the standards. Taking the Chinese standard as an example, it is represented in OWL to obtain an ontology, and SWRL is used for representing the checking rules. Then, an inference process is established to fulfill the checking. Finally, a prototype system is developed to validate the approach. It is concluded that the approach can be applied to check the quality of BIM models in IFC format automatically. This research contributes to the development of automatic compliance checking systems for the quality of BIM models against real standards.

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining in Large Language Models (LLMS)

The conventional methodology for sentiment analysis within large language models (LLMs) has predominantly drawn upon human emotional frameworks, incorporating physiological cues that are inherently absent in text-only communication. This research proposes a paradigm shift towards an emotionallyagnostic approach to sentiment analysis in LLMs, which concentrates on purely textual expressions of sentiment, circumventing the confounding effects of human physiological responses. The aim is to refine sentiment analysis algorithms to discern and generate emotionally congruent responses strictly from text-based cues. This study presents a comprehensive framework for an emotionally-agnostic sentiment analysis model that systematically excludes physiological indicators whilst maintaining the analytical depth required for accurate emotion detection. A novel suite of metrics tailored to this approach is developed, facilitating a nuanced interpretation of sentiment within text data, which is paramount for enhancing user interaction across a spectrum of text mining applications, including recommendation systems and interactive AI characters. The research undertakes a critical comparative analysis, juxtaposing the newly proposed model with traditional sentiment analysis techniques, to evaluate efficacy enhancements and to substantiate its application potential. Further, the investigation delves into the short-term memory capabilities of LLMs, exploring the implications for character AI roleplaying interfaces and their ability to recall and respond to user input within a text-driven emotional framework. Findings indicate that this emotionally-agnostic sentiment analysis approach not only simplifies the sentiment assessment process within LLMs but also opens avenues for a more precise and contextually appropriate emotional response generation. This abstracted model, devoid of human physiological constraints, represents a significant advancement in text mining, fostering improved interactions and contributing to the evolution of LLMs as adept analytical tools in various domains where emotional intelligence is crucial.

USE OF CERTAIN TERMS IN THE SPHERE OF ENVIRONMENTAL PROTECTION

The publication is dedicated to the coverage of legislative and theoretical approaches to the use of the interpretation of certain terms and concepts in the field of environmental protection, based on which suggestions are made regarding their optimal use in scientific circulation and relevant regulatory and legal acts. It is emphasized that the stability of informational and legal provision, in particular in the environmental sphere, is possible under the condition of unification of legislative terms, clear regulatory consolidation of their content with the aim of uniform understanding and application in practical activities. It is possible to secure interests and implement strategic priorities in the field of environmental protection through the creation of effective legal regulation, which includes various legal means, in particular regulations. The important role of definitions in legal regulation, which contain definitions of concepts as integral elements of the legal basis for ensuring the environmental policy of the state, is pointed out. It is noted that the legal definitions of the concepts have a universally binding nature and contribute to the formation of a single legal space. An attempt was made to evaluate the conceptual and categorical apparatus in the analyzed field from the point of view of the universality of the relevant definitions, the completeness of their textual expression, as well as state policy. It is noted that the relevant legislation does not contain a legislative definition of key concepts in the field of environmental protection. It was concluded that the identification of the definitions “ecology” and “surrounding environment” is erroneous, because “ecology” is the part of it that refers to the natural component of the environment. In this regard, as well as in view of the need to bring national legislation to international standards in the researched area, it is more appropriate to use the term “surrounding environment”, which is synonymous with the concept of “environment”, which would cover not only the natural component, but also the environment the existence of humanity in general.

Regarding the ordering of individual definitions in the regulatory and legal regulation of economic security

The scientific article examines issues related to the determination of the essence, role and meaning of definitions in the regulatory and legal regulation of economic security. The uniqueness of the phenomenon of economic security is emphasized, which is the guarantor of ensuring the national interests of the state, society, and the individual in the economic sphere and the basis for the unhindered implementation of the state’s strategic national priorities. Ensuring these interests and implementing strategic priorities is possible through the creation of effective legal regulation, which includes various legal means, in particular, regulatory prescriptions. It is pointed out the important role of definitions in normative and legal regulation, which contain definitions of concepts as integral elements of the legal basis for ensuring economic security. It is noted that the legal definitions of the concepts have a universally binding nature and contribute to the formation of a single legal space. The functions of legal definitions in the normative and legal regulation of ensuring economic security are revealed. An attempt was made to evaluate the conceptual and categorical apparatus in the analyzed field from the point of view of the universality of the relevant definitions, the completeness of their textual expression, as well as state policy. It is noted that the current security legislation does not contain a legislative definition of key concepts in the field of ensuring economic security. In this regard, the problem of unification of the conceptual and categorical apparatus in the field of ensuring economic security by adopting the main strategic planning documents is raised. In the opinion of the author, taking into account the main approaches of the legislation to the definition of national interests, the legal concept of “economic security”, similar to the definition of “state security”, should reflect a certain state of the state, under which conditions are created to ensure the appropriate state of security of the economic system of Ukraine. The expediency of developing a new Strategy for the sustainable development of the economy of Ukraine for the post-war period was emphasized, which would not only determine the ways to achieve goals and implement priorities of national interests in the field of ensuring economic security and foresee the main challenges and threats, but also contain a legal definition of “economic security” and such its components, such as “financial security”, “foreign economic security”, “investment and innovation security”, etc.

Enhanced Fine-Grained Motion Diffusion for Text-Driven Human Motion Synthesis

The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently. However, in most cases, textual expressions only contain general and qualitative motion descriptions, while lack fine depiction and sufficient intensity, leading to the synthesized motions that either (a) semantically compliant but uncontrollable over specific pose details, or (b) even deviates from the provided descriptions, bringing animators with undesired cases. In this paper, we propose DiffKFC, a conditional diffusion model for text-driven motion synthesis with KeyFrames Collaborated, enabling realistic generation with collaborative and efficient dual-level control: coarse guidance at semantic level, with only few keyframes for direct and fine-grained depiction down to body posture level. Unlike existing inference-editing diffusion models that incorporate conditions without training, our conditional diffusion model is explicitly trained and can fully exploit correlations among texts, keyframes and the diffused target frames. To preserve the control capability of discrete and sparse keyframes, we customize dilated mask attention modules where only partial valid tokens participate in local-to-global attention, indicated by the dilated keyframe mask. Additionally, we develop a simple yet effective smoothness prior, which steers the generated frames towards seamless keyframe transitions at inference. Extensive experiments show that our model not only achieves state-of-the-art performance in terms of semantic fidelity, but more importantly, is able to satisfy animator requirements through fine-grained guidance without tedious labor.

Taking stock of far-right terrorism through manifestos: Glorification of identity

Abstract This research delves into the identity construction and violence justification within the context of far-right lone-actor terrorism, particularly motivated by white supremacist ideologies. Employing a qualitative analysis of manifestos compiled by five lone-actor terrorists, this study adopts a model to unveil the nuanced processes behind the justification of violence and glorification of collective identities. The model has been formed for the purpose of the study, drawing from social identity and identity fusion approaches, including steps such as group alignment, exclusion, threat, virtue, and celebration. The analysis of these manifestos illuminates a progression through each phase of the violent act, meticulously crafted through textual expression. Central to the terrorists’ objectives is the creation of a rhetorical platform aimed at fomenting violence against non-white, ethnic, and religious groups. Their motivation arises from the perceived threat of the ‘white race’ being supplanted by immigrant communities across various social, political, and economic domains. This justification of violence hinges on the portrayal of themselves as protectors of the majority society, pitted against these minority groups. Strikingly, the terrorists celebrate their actions by commemorating past white supremacists who employed violence against marginalised communities.

Cluster-based Visualization of human element interactions in marine accidents

This study addresses human elements in marine accidents that cause many deaths, environmental pollution, and economic losses. The aim of this study is to identify various human elements that cause marine accidents and to visualize their interaction with each other on a two-dimensional plane. For this purpose, a two-stage study was conducted: the first was a pilot study in which the performance of different dimensionality reduction and clustering algorithms was tested, and the second was the phase in which 2050 textual expressions corresponding to human elements in 871 marine accidents were analyzed with the algorithms featured in the pilot study. The textual expressions were converted into vectors with the BERT model, dimensionality reduction was performed with UMAP, and clustering was performed with the k-Means algorithm. The results showed that the most common type of error and the one that interacts the most with others are navigational errors. It has also been observed that a cause-effect relationship can be established between the clusters such as “fatigue”-“cognitive dysfunction” and “falseobservation”-“violation of navigation rules”. This study contributes to taking more specific safety measures as it directly reveals human elements specific to marine accidents rather than using generic human error classifications.

Unveiling the Construction of Overall Meaning: A Social Semiotic Analysis of the Anti-War Discourse in Lynn Nottage’s Ruined

Lynn Nottage’s drama, Ruined, depicts the profound and far-reaching consequences of warfare on both individuals and communities. With deliberate emphasis on the harrowing experiences of Congolese women, this play is concerned with the intricate web of human rights challenges that plague conflict-ridden regions. Halliday’s social semiotics understands and analyzes how meaning is constructed in various discourses, which can provide a framework for probing the relationships between language, communication, and society, and how these entities interplay to convey meaning and mediate social interactions. This paper delves into the discursive cues that construct the overall meaning of Ruined through the lens of Halliday’s social semiotics, and further explores the social-cultural contexts constructed by Nottage as well as the anti-war keynote embedded within her play. By examining the ideational, interpersonal, and textual expressions throughout the text, this analysis suggests that the adopted framework can provide valuable insights into understanding how discursive strategies serve character development, relational dynamics between characters, and the underlying message of the targeting play, which can also be taken as an attempt at multidisciplinary interpretations on literary works.

Towards Bridged Vision and Language: Learning Cross-Modal Knowledge Representation for Relation Extraction

In natural language processing, relation extraction (RE) is to detect and classify the semantic relationship of two given entities within a sentence. Previous RE methods consider only the textual contents and suffer performance decline in social media when texts lack contexts. Incorporating text-related visual information can supplement the missing semantics for relation extraction in social media posts. However, textual relations are usually abstract and of high-level semantics, which causes the semantic gap between visual contents and textual expressions. In this paper, we propose RECK - a neural network for relation extraction with cross-modal knowledge representations. Different from previous multimodal methods training a common subspace for all modalities, we bridge the semantic gaps by explicitly selecting knowledge paths from external knowledge through the cross-modal object-entity pairs. We further extend the paths into a knowledge graph, and adopt a graph attention network to capture the multi-grained relevant concepts which can provide higher level and key semantics information from external knowledge. Besides, we employ a cross-modal attention mechanism to align and fuse the multimodal information. Experimental results on a multimodal RE dataset show that our model achieves new state-of-the-art performance with knowledge evidence <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

The hybrid of BERT and deep learning models for Indonesian sentiment analysis

&lt;span&gt;Artificial intelligence (AI) is one example of how data science innovation has advanced quickly in recent years and has greatly improved human existence. Neural networks, which are a type of machine learning model, are a fundamental component of deep learning in the field of AI. Deep learning models can carry out feature extraction and classification tasks in a single design because of their numerous neural network layers. Modern machine learning algorithms have been shown to perform worse than this model on tasks including text classification, audio recognition, imaginary, and pattern recognition. Deep learning models have outperformed AI-based methods in sentiment analysis and other text categorization tasks. Text data can originate from a number of places, including social media. Sentiment analysis is the computational examination of textual expressions of ideas and feelings. This study employs the convolutional neural network (CNN), long-short term memory (LSTM), CNN-LSTM, and LSTM-CNN models in a deep learning framework using bidirectional encoder representations from transformers (BERT) data representation to assess the performance of machine learning. The implementation of the model utilises YouTube discussion data pertaining to political films associated with the Indonesian presidential election of 2024. Confusion metrics, including as accuracy, precision, and recall, are then used to analyse the model’s performance.&lt;/span&gt;

The Influence of Personality Structure on the Level of Professional Burnout

The influence of the level of professional (emotional) burnout on the quality of work of educational psychologists of municipal educational organizations has determined the relevance of this article, which analyzes the dependence of the personality structure presented in Transactional Analysis as the combination of three ego states (Parent, Child and Adult) on the coefficient of professional burnout. This coefficient is interpreted as an integral indicator of three factors: psycho-emotional exhaustion, depersonalization/dehumanization and reduction of professional achievements. Identification of the personality structure of the study participants, calculation of the coefficient of their professional (emotional) burnout, structuring the levels of severity of factors of professional (emotional) burnout, implemented as research objectives, created the possibility of achieving the research goal. The hypothesis was tested by means of mathematical statistics methods. The visibility and accessibility of conclusions about the correlations between the integral indicator of professional (emotional) burnout and the level of severity of ego states in the egogram are provided by their graphical and textual expression. The presented data on the absence of a critical level of professional (emotional) burnout of educational psychologists of municipal educational organizations and the presence of a correlation between the reduction of professional achievements and the Adult ego state substantiate the vector of further work to ensure conditions for maintaining the desired psycho-emotional state of specialists.

METAFORA LIMA BUAH SAJAK SAPARDI DJOKO DAMONO DAN PENGGUNAANNYA DALAM PEMBELAJARAN SASTRA

This study aims to review the semiotic technique of Sapardi's poetry in five selected poems from Sihir Hujan. This research is qualitative descriptive research. The analysis technique used is the semiotic theory with a heuristic-hermeneutic reading from Michel Riffaterre. Of the five poems that became the object of the study, it was found that almost all of them used implicit metaphors. Sapardi in Sihir Hujan relies on sound elements to form a rhythm, support the mood of the rhyme, and emphasize the meaning of the rhyme. Thus, it can be stated that the meaning of Sapardi's poem is something that is behind the textual expression of the poem. This study also came to the conclusion that the poem "Yang Fana Adalah Waktu" is the only poem out of the five selected poems that can be used as learning material for literature and figurative language for elementary, junior high, and high school education levels.values both in terms of religious values, moral values, social values, and aesthetic values.

Chinese Text De-Colloquialization Technique Based on Back-Translation Strategy and End-to-End Learning

With the development of the Internet, there has been a significant increase in various types of textual information. However, when people engage in the composition of formal texts, they often incorporate their colloquial habits, which can diminish the professionalism and formality of the text. Existing research on Chinese texts primarily focuses on correcting misspelt characters that are visually or phonetically similar, as well as obvious grammatical errors, such as redundancy, omissions, and incorrect word order. However, there is limited research addressing the correction of text that exhibits colloquial expressions without apparent grammatical errors or misspelt characters. This article proposes a novel technique that utilizes deep learning methods to directly transform colloquial textual expressions into formal written expressions. Firstly, a parallel corpus dataset of written and spoken language is constructed using a back-translation strategy. Then, an end-to-end learning mechanism based on neural machine translation is employed, with colloquial text as the source language and written text as the target language. This allows the model to directly transform the colloquial text into text with a formal style. Finally, an evaluation of the proposed approach is conducted using the bilingual evaluation understudy (BLEU) and manual assessment techniques. The experimental results demonstrate that the technology proposed in this paper performs well in the task of de-colloquialization in Chinese texts. The contribution of this paper lies in proposing an automated method for collecting a substitute for manually annotated parallel corpora of spoken and written language, which significantly saves time and reduces the manual cost of constructing the dataset. Furthermore, the application of end-to-end learning techniques from neural machine translation to the task of de-colloquialization allows the trained model to directly generate written language flexibly based on the input of spoken language. This presents a novel solution for the task of the de-colloquialization of Chinese text.

Critical Analysis on Multimodal Emotion Recognition in Meeting the Requirements for Next Generation Human Computer Interactions

Emotion recognition is the gap in today’s Human Computer Interaction (HCI). These systems lack the ability to effectively recognize, express and feel emotion limits in their human interaction. They still lack the better sensitivity to human emotions. Multi modal emotion recognition attempts to addresses this gap by measuring emotional state from gestures, facial expressions, acoustic characteristics, textual expressions. Multi modal data acquired from video, audio, sensors etc. are combined using various techniques to classify basis human emotions like happiness, joy, neutrality, surprise, sadness, disgust, fear, anger etc. This work presents a critical analysis of multi modal emotion recognition approaches in meeting the requirements of next generation human computer interactions. The study first explores and defines the requirements of next generation human computer interactions and critically analyzes the existing multi modal emotion recognition approaches in addressing those requirements.

Ausencias y susurros escatológicos en Fortunata y Jacinta

"Spanish nineteenth-century Realism is afflicted by a paradox concerning scatological matters that has been so far neglected. On the one hand, one of the key foundations of realist poetics is the will to give textual expression to the totality of life and language, including foul words and repugnant things and deeds, the miseries of the soul and the body, both of the individual person and of society. On the other hand, concrete realist novels incur in the notable absence of an excremental component, a lack that constitutes a noncompliance with that programmatic ambition of totality. In general, that is the situation in Fortunata y Jacinta by Benito Pérez Galdós. The present essay attempts a reflection on this excremental absence using as points of departure a theoretical argument and a set of textual evidences. First, psychoanalytical and Marxist theories predicate the idea that what is not said, what is muted in a discourse, could become the cardinal element of that discourse and its contradictions, its psychological and ideological key. Second, starting with the controversial scene of Fortunata ́s dream, the text occasionally breaks its scatological silence through a series of excremental whispers of sorts that are indexes of the importance and the potential significance of excrement in the novel. Keywords: Pérez Galdós; reality; realism; scatology; dreams"

Theory (And/Or) Fiction: Academic Writing, Inhuman Horror, and the New Weird in Reza Negarestani’s Cyclonopedia

ABSTRACT H.P Lovecraft’s inhuman horror represents a site of both creativity and controversy, complicating his innovative depictions of the unknown by his racially problematic politics. This article presents Reza Negarestani’s Cyclonopedia as a dramatic reappraisal of Lovecraftian horror from a twenty-first century non-Western perspective. Negarestani’s academic prose – specifically concepts and footnotes – centralizes the inhumanity of oil to dramatize the limits of subjectivity. Initially, oil provides a means of conceptualizing gaps within the text, adding a theoretical form of coherence to textual expressions of the unknown and the inhuman. The counterintuitive return to a subjectivity, relegated to the margins and footnotes of the text, recalibrates rather than rejects human form. This disruption of a human/inhuman binary challenges Lovecraft’s formulation of white Western subjectivity, while retaining a Lovecraftian horror aesthetic. Furthermore, by presenting academic prose as an intensifier of textual horror, its aesthetic function also challenges an implied literary/theory hierarchy, whereby literature serves the needs of theory. Cylonopedia’s theory-fictional form presents an intersection between the New Weird and the inhuman turn of contemporary theory; one which radically challenges humanist, anti-humanist, and posthumanist notions of subjectivity.

Towards Real-Time Panoptic Narrative Grounding by an End-to-End Grounding Network

Panoptic Narrative Grounding (PNG) is an emerging cross-modal grounding task, which locates the target regions of an image corresponding to the text description. Existing approaches for PNG are mainly based on a two-stage paradigm, which is computationally expensive. In this paper, we propose a one-stage network for real-time PNG, termed End-to-End Panoptic Narrative Grounding network (EPNG), which directly generates masks for referents. Specifically, we propose two innovative designs, i.e., Locality-Perceptive Attention (LPA) and a bidirectional Semantic Alignment Loss (SAL), to properly handle the many-to-many relationship between textual expressions and visual objects. LPA embeds the local spatial priors into attention modeling, i.e., a pixel may belong to multiple masks at different scales, thereby improving segmentation. To help understand the complex semantic relationships, SAL proposes a bidirectional contrastive objective to regularize the semantic consistency inter modalities. Extensive experiments on the PNG benchmark dataset demonstrate the effectiveness and efficiency of our method. Compared to the single-stage baseline, our method achieves a significant improvement of up to 9.4% accuracy. More importantly, our EPNG is 10 times faster than the two-stage model. Meanwhile, the generalization ability of EPNG is also validated by zero-shot experiments on other grounding tasks. The source codes and trained models for all our experiments are publicly available at https://github.com/Mr-Neko/EPNG.git.