Traditional Representation Research Articles

Exploring How the Divergent becomes The Norm in Selected Works of 21st-Century Disability Fiction

Twenty-first century literature featuring disability has shown a marked flight from the traditional and conventional representations of disability as sites of pity, compassion or fear. Two novels that ostensibly delineate this shift in disability cultural studies are The Quarry (2013) by Iain Banks and A Room Called Earth (2020) by Madeleine Ryan. Although almost a decade apart, the two novels portray neurodivergent protagonists whose disability is neither a badge of shame nor a defining characteristic. This provided the perfect opportunity to step into the other side of the normality divide and experience the world from the point-of-view of intellectually divergent characters without any of the negative bias of an external, able-bodied narrator. The analysis revealed how in the subjective realities of these protagonists the traditional concepts of normal and divergent were completely inverted thereby solidifying the idea that disability is not an absolute condition but a relative and socially constructed one and similarly authentic representations can go a long way in displacing the stigmatization and misconceptions surrounding the disabled.

The Meaning of Sarong in Madurese Culture: A Symbolic Analysis of Films on YouTube Mata Pena

The purpose of this research is to examine the symbolic meaning of the Madurese sarong as a cultural symbol in short films featured on the YouTube channel Mata Pena. The background of this research highlights the significance of the sarong as a symbol of identity and a representation of cultural values in Madurese society. The sarong is not only viewed as an item of clothing but also as a marker of social status, tradition, and honor passed down through generations. This research employs a qualitative method with a semiotic analysis approach based on Charles Sanders Peirce's theory, focusing on three main elements: sign, object, and interpretant. This framework is used to understand how the meaning of the sarong is constructed within cultural and social contexts. The findings reveal that the Madurese sarong carries profound meaning, both as a symbol of social status and cultural identity and as a representation of tradition, politeness, religion and honor in Madurese society. Keyword: Sarong, Semiotics, Cultural Identity, Madura, Short Film

Fusion Text Representations to Enhance Contextual Meaning in Sentiment Classification

Sentiment classification plays a crucial role in evaluating user feedback. Today, online media users can freely provide their reviews with few restrictions. User reviews on social media are often disorganized and challenging to classify as positive or negative comments. This task becomes even more difficult when dealing with large amounts of data, making sentiment classification necessary. Automating sentiment classification involves text classification processes, commonly performed using deep learning methods. The classification process using deep learning models is closely tied to text representation. This step is critical as it affects the quality of the data being processed by the deep learning model. Traditional text representation methods often overlook the contextual meaning of sentences, leading to potential misclassification by the model. In this study, we propose a novel fusion text representation model, GloWord_biGRU, designed to enhance the contextual understanding of sentences for sentiment classification. Firstly, we combine the advantages of GloVe and Word2Vec to obtain richer and more meaningful word representations. GloVe provides word representations based on global frequency statistics within a large corpus, while Word2Vec generates word vectors that capture local contextual relationships. By integrating these two approaches, we enhance the quality of word representations used in our model. During the classification stage, we employ biGRU, considering the use of fewer parameters, which consequently reduces computational requirements. We evaluate the proposed model using the IMDB dataset. Several scenarios demonstrate that our proposed model achieves superior performance, with an F1 score of 90.21%.

Redefining Female Identity Through the Female Gaze: A Comparative Analysis of Gender Narratives in Feminist Cinema

Abstract: This paper examines how female directors convey feminist consciousness and challenge traditional gender roles through the female gaze in film. Through film analysis, theoretical research, and case studies, this paper examines how female directors have redefined female roles and narratives in contrast to traditional male perspectives. The conventional male perspective has traditionally objectified and marginalized women with the male gaze in visual expression. However, the female perspective portrays women as complex and multidimensional subjects, breaking the male-dominated narrative paradigm and offering new possibilities for reimagining the image of women on screen. Through a feminist lens, this paper further analyzes how the female perspective resists widespread misogyny and provides a counter-narrative to traditional patriarchal representations. In addition, this study examines how female perspectives from different cultural contexts have advanced feminist film theory globally. By highlighting their unique visual expressions, the study demonstrates how these directors advance feminist discourse through their filmmaking, challenging gender, and cultural norms.

Phoneme-by-Phoneme Speech Recognition as a Classification of Series on a Set of Sequences of Elements of Complex Objects Using an Improved Trie-Tree

Sequences, including vector sequences, are applicable in any subject domains. Sequences of scalar values or vectors (series) can be produced by higher-order sequences, for example: a series of states, or elements of complex objects. This academic paper is devoted to the application of an improved trie-tree in the classification of series on a set of sequences of elements of complex objects using the dynamic programming method. The implementation areas of dynamic programming have been considered. It has been shown that dynamic programming is adapted to multi-step operations of calculating additive (multiplicative) similarity/difference measures. It is argued that the improved trie-tree is applicable in the problem of classifying a series on a set of sequences of elements of complex objects using such similarity/difference measures. An analysis of hierarchical representations of sets of sequences has been performed. The advantages of the improved trie-tree over traditional representations of other highly branching trees have been described. A formal description of the improved trie-tree has been developed. An explanation has been given to the previously obtained data on a significant speed gain for operations of adding and deleting sequences in the improved trie-tree relative to the use of an array with an index table (24 and 380 times, respectively). The problem of phoneme-by-phoneme recognition of speech commands has been formulated as a problem of classifying series on a set of sequences of elements of complex objects and a method for its solving has been presented. A method for classifying a series on a set of sequences of elements of complex objects using the improved trie-tree is developed. The method has been studied using the example of phoneme-by-phoneme recognition with a hierarchical representation of the dictionary of speech command classes. In this method, recognition of speech commands is executed traversing the improved trie-tree that stores a set of transcriptions of speech commands – sequences of transcription symbols that denote classes of sounds. Numerical studies have shown that classifying a series as sequences of elements of complex objects increases the frequency of correct classification compared to classifying a series on a set of series, and using the improved trie-tree reduces the time spent on classification.

Anthropocenic Futures and Precarious Bodies. A Reading of "Mugre rosa" (2020) by Fernanda Trías

Mugre rosa (2020), by Uruguayan author Fernanda Trías, depicts a near-future Montevideo, where the population is confronted with climatic disaster and the propagation of a devastating disease. In this article, I explore the representation of bodily precarity in the context of environmental catastrophe, arguing that this vulnerability constitutes a point of resistance from which to rethink human identity. On the one hand, exposure to an anthropogenic environment is interpreted as instrumental to late capitalist biopolitics. This is revealed through the cartographies of precarity emerging from the novel, which reflect not only the chasm between Global North and Global South but also local inequalities. On the other hand, this corporeal exposure constitutes a challenge to traditional representations of the human. Through the analysis of the depiction of the illness, I discuss how this deconstruction of the body favours the emergence of a new, interconnected identity. Ultimately, I suggest that a destabilising age such as the Anthropocene constitutes not only a profound moment of crisis but also a privileged space to rethink human subjectivity and its modes of representation.

Intangible cultural heritage versus tourism: residents’ reactions to temporal overtourism during the Day of the Dead in Mixquic, Mexico

ABSTRACT Globalisation opens small communities and their traditions to temporal overtourism, causing adaptations in the local dynamics. This study investigates responses to overtourism during the Day of the Dead in Mixquic, a semi-rural community located in Mexico City. Employing rapid ethnography, semi-structured interviews, participant observation, and content analysis of social media, it delves into local perceptions and actions regarding temporary overtourism dynamics in a global patrimonialisation context. Results reveal a complex interplay between traditional practices, community resilience, and tourist dynamics. Despite challenges, residents show a positive attitude towards tourists, often incorporating them into ancient traditions while preserving cultural authenticity. Notably, symbolic representations of tradition persist amidst external pressures, showcasing the resilience of community identity. The study underscores the importance of embracing community-centric approaches – observing and considering the community – and innovative tourism strategies to mitigate negative impacts for a better preservation of cultural heritage. By prioritising resident values and needs, tourism can lead to practices that are beneficial to both communities and visitors. Furthermore, the research highlights the importance of observing and listening to local communities.

Advancements in Word Embeddings: A Comprehensive Survey and Analysis

In recent years, the field of Natural Language Processing (NLP) has seen significant growth in the study of word representation, with word embeddings proving valuable for various NLP tasks by providing representations that encapsulate prior knowledge. We reviewed word embedding models, their applications, cross-lingual embeddings, model analyses, and techniques for model compression. We offered insights into the evolving landscape of word representations in NLP, focusing on the models and algorithms used to estimate word embeddings and their analysis strategies. To address this, we conducted a detailed examination and categorization of these evaluations and models, highlighting their significant strengths and weaknesses. We discussed a prevalent method of representing text data to capture semantics, emphasizing how different techniques can be effectively applied to interpret text data. Unlike traditional word representations, such as Word to Vector (word2vec), newer contextual embeddings, like Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo), have pushed the boundaries by capturing the use of words through diverse contexts and encoding information transfer across different languages. These embeddings leverage context to represent words, leading to innovative applications in various NLP tasks.

Longitudinal Associations Between Scripted Attachment Representations in Late Adolescence and Depression in Adulthood in a Normative and a Higher-Risk Cohort

Attachment representations are increasingly operationalized as secure base script knowledge—one’s level of awareness of the temporal-causal schema that summarizes basic features of seeking and receiving effective support from caregivers when in distress. A method was recently developed to assess secure base script knowledge during the Adult Attachment Interview (AAIsbs), though its predictive validity for mental health outcomes is largely unknown. A series of pre-registered analyses, leveraging two large, longitudinal cohorts to assess the associations between AAIsbs and depression in emerging adults revealed that higher AAIsbs at age 18 years in the normative-risk sample, but not in a higher-risk cohort, predicted fewer depressive symptoms at age 30 years. In general, these associations were robust to concurrent depressive symptoms, sociodemographic and cognitive functioning covariates, and other traditional attachment representation measures. Findings support the importance of considering schematic attachment representations in developing interventions to improve young adults’ well-being.

The Role of Working Commission Transactions in Afghanistan's Legal System

A commission agent is an individual who conducts business transactions in their own name on behalf of the transaction owner, earning a fee for their services. This contractual arrangement between the transaction owner and the commission agent is referred to as a working commission. It is important to differentiate working commission from representation and advocacy; in traditional representation or advocacy, the representative or advocate acts on behalf of the principal or client, whereas a commission agent often conceals the identity of their principal. Consequently, the party involved in the transaction may only recognize the commission agent’s right and not the identity of the principal. This research highlights the significance of working commission transactions within the Afghan legal framework by thoroughly examining their legal implications. The importance of this discussion is paramount, as it addresses a critical issue impacting Afghan society. The objectives of this research can be viewed from both theoretical and practical perspectives. Theoretically, the primary aim is to expand the academic understanding of commission transactions. Practically, the research seeks to analyze the position of commission work within Afghanistan's legal system, which constitutes a fundamental legal concern. The investigation was conducted using library resources and existing legal texts from Afghanistan. Insights and definitions pertaining to this topic were derived from relevant books and legal documents. Through this research, I have concluded that a commission agent is a businessman who operates in their own name while acting on behalf of others. In this context, entities that establish and manage property dealer offices serve as pertinent examples of working commission practices.

Enhancing the variability of simulated random processes using the spectral representation method

In this paper, the traditional spectral representation method for simulating stochastic processes is revisited. The main goal is to introduce various approaches to enhance the variability of the generated sample functions to better reflect variabilities observed in field measurements and experimental data, while keeping the ensemble-averaged power spectra unchanged. The general idea, gradually developed over the last decade, is to randomize the power spectral density function of the uncertain physical quantities by modeling certain parameters as random variables calibrated from experimental data. The potential of such approaches has not been fully explored yet. This work explores the pros and cons of using power spectral density functions with random parameters in the simulation of highly variable, stationary and nonstationary, Gaussian stochastic processes. The main characteristics of a stochastic process, such as distribution of maxima and variability in time and frequency, are examined. The use of a pass-band Butterworth filter to control the variability of the Fourier spectrum of the simulated samples is studied. The Butterworth filter is selected so that the ensemble-averaged power spectral density matches the target power spectrum, but each individual generated sample function possesses different amplitude and frequency distributions. Comparisons between samples generated using the traditional spectral representation method and those generated with enhanced variability are made through a series of illustrative examples. Results show that with the proposed enhanced-variability spectral representation method, certain characteristics of the stochastic process, such as the distribution of maxima, will be significantly different (while the ensemble-averaged power spectra will remain the same). The implications in reliability studies are obvious.

War without Pain? Representing Death and Injury in the War of the Pacific (1879–1884) in Chilean Museums

This article examines how Chilean museums that exhibit war handles the bodily consequences of war and weapons upon the human body through an investigation of how death and injury in the War of the Pacific (1879–1884) is represented in a museal context. Are Chilean museums able to defetishise historical weapons and other forms of voyeurism? By analysing two permanent galleries and one temporary exhibit, this article argues that Chilean museums are unable to fully abandon traditional representations that treat the war as a romantic and heroic conflict. Death and injury are present in the museums but lack a reflexive and critical perspective.

EDMUND BLUNDEN’S WAR VERSES: NATURE AS THE UNSEEN VICTIM OF THE GREAT WAR

War leaves a lasting mark not only on the lives of those directly involved but also profoundly impacts the natural environment, an aspect frequently overshadowed by the stark human tragedies. Edmund Blunden, a World War I poet, focuses on the environmental destruction caused by war. This article aims to analyse Blunden’s poems “Preparations for Victory” and “Thiepval Wood,” exploring the resonant themes of desolation and the interconnected destinies of soldiers and nature through his poetic expression. These poems provide a compelling lens to address an often-neglected aspect of warfare: its effect on the natural world. Moving beyond traditional representations where nature is merely a setting for human struggles, Blunden portrays it as a primary victim, subjected to the harsh transformations and destructions of war. Accordingly, this study illuminates how Blunden articulates nature’s suffering and alteration as fundamentally intertwined with human strife, offering a critical voice in understanding the multifaceted impacts of military conflicts.

Space‐efficient data structures for the inference of subsumption and disjointness relations

AbstractConventional database systems function as static data repositories, storing vast amounts of facts and offering efficient query processing capabilities. The sheer volume of data these systems store has a direct impact on their scalability, both in terms of storage space and query processing time. Deductive database systems, on the other hand, require far less storage space since they derive new knowledge by applying inference rules. The challenge is how to efficiently obtain the required derivations, compared to having them in explicit form. In this study, we concentrate on a set of predefined inference rules for subsumption and disjointness relations, including their negations. We use compact data structures to store the facts and provide algorithms to support each type of relation, minimizing even further the storage space requirements. Our experimental findings demonstrate the feasibility of this approach, which not only saves space but is often faster than a baseline that uses well‐known graph traversal algorithms implemented on top of a traditional adjacency list representation to derive the relations.

A Ramayana of Her Own: Reading Chandrabati’s Ramayana through a Gendered Perspective

Composed in the late 16th century, the Bengali woman poet Chandrabati’s Ramayana retells the Ramkatha stories with a distinctive female voice that narrates the familiar story of women’s suffering rather than of masculine heroism. Belonging to the Bengali genre of folk narratives variously known as pala gaan, this version of the Ramayana stands in clear affinity with the several narratives about women’s tragic lives that comprise a distinct type of pala literature. It centralizes Sita’s agonizing experiences. Sita’s character becomes the primary narrative tool through which Chandrabati’s verse narrative subverts the traditional patriarchal representation of her character. She (Chandrabati) challenges the mythology from the point of view of the gendered subaltern and makes Sita occupy the centre stage pushing back Rama to the margin. Written with a unique narrative technique this verse narrative is told through a “baromasi” – a woman’s plaintive refrain about everyday sorrows that venture into mythical intertextualities, Chandrabati questions the woman-centric point of view to understand, analyze, and interpret religion and remains critical of the dominant male ideology. The narrative authority of the female voice becomes even more assertive with the addition of the poet’s own life story, which serves to set this revision of the customary Ramkatha within a discourse on women’s self-perception. The paper intends to explore how a woman’s narrative raises crucial questions regarding wifehood, motherhood, the female psyche, the various forms of oppression that women go through, and the overall position of women in a male-dominated society. It is a woman’s text, an atypical refashioning of the Ramayana story which starts with a river in Sita’s village and her miraculous birth. While maintaining some of the basic plot points, Chandrabati’s short, humble Ramayana in Bangla takes artistic liberties that few ‘Ramayanas’ of the time had taken.

Disrupting Dichotomies, Dismantling Hegemonies: Studying Caste Reality in Fiction Through Omprakash Valmiki’s Amma and Other Stories

In the Hindi belt, Dalit literature has taken full shape in the post-Ambedkarite and post-Mandal Commission moments, making this literature layered with the understanding of caste, caste politics and its subtleties, with the central question revolving around the issue of realism. The genre of Dalit fiction transcends the societal and literary boundaries that are guided by hegemonic forces and lifts the cloak of invisibility from the ‘other’. It has the potential to disrupt the dichotomy between the centre and the periphery. By undertaking a critical study of Omprakash Valmiki’s anthology Amma and Other Stories, published in 2008, this article is an attempt to locate the creative and liberating power of Dalit fiction that is seamlessly interwoven with reality. Primarily placed in the rural and urban areas of the state of Uttar Pradesh, Valmiki’s writing situates the Dalit reality directly in front of the readers, all the while challenging the representation and prominent discourse of the Hindi literary tradition. It is in the context of realism that this article focuses on the new kind of realism brought in by Dalit fiction and the revolutionary inversion undertaken by it, which subverts and dismantles the hegemonic traditions of society and literature.

Evaluating Interior Space Perception using Virtual Reality

This study evaluates the effectiveness of Virtual Reality (VR) in enhancing spatial perception compared to traditional 2D representations in the context of interior design education. Conducted with Interior Architecture students at the University of Buraimi, the research utilized the Oculus Quest 2 VR headset to create an immersive environment. Participants assessed various spatial characteristics, such as depth, scale, and spatial relationships, using both printed materials and VR immersion. Findings indicate that VR significantly improves the accuracy and realism of spatial perception, providing a more comprehensive understanding of interior spaces. This research contributes to the growing body of knowledge on VR applications in architectural education, highlighting its potential benefits and challenges.

Continuous Dictionary of Nodes Model and Bilinear-Diffusion Representation Learning for Brain Disease Analysis.

Brain networks based on functional magnetic resonance imaging (fMRI) provide a crucial perspective for diagnosing brain diseases. Representation learning has recently attracted tremendous attention due to its strong representation capability, which can be naturally applied to brain disease analysis. However, traditional representation learning only considers direct and local node interactions in original brain networks, posing challenges in constructing higher-order brain networks to represent indirect and extensive node interactions. To address this problem, we propose the Continuous Dictionary of Nodes model and Bilinear-Diffusion (CDON-BD) network for brain disease analysis. The CDON model is innovatively used to learn the original brain network, with its encoder weights directly regarded as latent features. To fully integrate latent features, we further utilize Bilinear Pooling to construct higher-order brain networks. The Diffusion Module is designed to capture extensive node interactions in higher-order brain networks. Compared to state-of-the-art methods, CDON-BD demonstrates competitive classification performance on two real datasets. Moreover, the higher-order representations learned by our method reveal brain regions relevant to the diseases, contributing to a better understanding of the pathology of brain diseases.

TransE-MTP: A New Representation Learning Method for Knowledge Graph Embedding with Multi-Translation Principles and TransE

The purpose of representation learning is to encode the entities and relations in a knowledge graph as low-dimensional and real-valued vectors through machine learning technology. Traditional representation learning methods like TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of a graph’s entities, are effective for learning the embeddings of knowledge bases, but struggle to effectively model complex relations like one-to-many, many-to-one, and many-to-many. To overcome the above issues, we introduce a new method for knowledge representation, reasoning, and completion based on multi-translation principles and TransE (TransE-MTP). By defining multiple translation principles (MTPs) for different relation types, such as one-to-one and complex relations like one-to-many, many-to-one, and many-to-many, and combining MTPs with a typical translating-based model for modeling multi-relational data (TransE), the proposed method, TransE-MTP, ensures that multiple optimization objectives can be targeted and optimized during training on complex relations, thereby providing superior prediction performance. We implement a prototype of TransE-MTP to demonstrate its effectiveness at link prediction and triplet classification on two prominent knowledge graph datasets: Freebase and Wordnet. Our experimental results show that the proposed method enhanced the performance of both TransE and knowledge graph embedding by translating on hyperplanes (TransH), which confirms its effectiveness and competitiveness.

Score Images as a Modality: Enhancing Symbolic Music Understanding through Large-Scale Multimodal Pre-Training.

Symbolic music understanding is a critical challenge in artificial intelligence. While traditional symbolic music representations like MIDI capture essential musical elements, they often lack the nuanced expression in music scores. Leveraging the advancements in multimodal pre-training, particularly in visual-language pre-training, we propose a groundbreaking approach: the Score Images as a Modality (SIM) model. This model integrates music score images alongside MIDI data for enhanced symbolic music understanding. We also introduce novel pre-training tasks, including masked bar-attribute modeling and score-MIDI matching. These tasks enable the SIM model to capture music structures and align visual and symbolic representations effectively. Additionally, we present a meticulously curated dataset of matched score images and MIDI representations optimized for training the SIM model. Through experimental validation, we demonstrate the efficacy of our approach in advancing symbolic music understanding.