Conceptual Representation Research Articles

Reflections on Melbourne Suburbs: streetscapes, workplaces (homes) and subjectivities

The Melbourne Suburbs part of the Immigrant Networks exhibition delves into the spatialised experiences of mostly Southeast Asian-Australian communities, whose local origins were as refugees in the 1970s and 1980s, exploring their engagement with space and the negotiation of identities through labour and inhabitation of the Melbourne suburbs of Footscray and Springvale. Framed by Henri Lefebvre’s trialectics of spatial production this paper discusses how the conceptions of representation and the immigrant histories and geographies as an exhibition in a museum intersect with immigrants’ own narratives and spatialised histories, and more broadly how academic, architectonic and curatorial approaches intersect with immigrant subjectivities.

Grounded cognition and the representation of momentum: abstract concepts modulate mislocalization

Literature on grounded cognition argues that mental representations of concepts, even abstract concepts, involve modal simulations. These modalities are typically assumed to reside within the body, such as in the sensorimotor system. A recent proposal argues that physical invariants, such as momentum or gravity, can also be substrates in which concepts can be grounded, expanding the assumed limits of grounding beyond the body. We here experimentally assessed this proposal by exploiting the representational momentum effect and the abstract concept of success. If success is grounded in the physical invariant momentum, the representational momentum effect should be larger for successful targets. We tested this hypothesis across four experiments (three pre-registered). In a surprising finding, we find hints that large trial numbers may hinder being able to find a representational momentum effect, which should be further investigated in future research. Regarding the central hypothesis, although only one experiment found statistically significant support, the effect tended toward the same direction in the three others as well. In order to draw robust conclusions about the results, we performed a mini meta, which aggregates the effects and inference statistics across the N = 271 participants. Across the four experiments, this effect was statistically significant, suggesting evidence in favor of the central hypothesis. These results should be interpreted with caution as there was inconsistency across experiments, suggesting the magnitude of the effect is small, and when asked who they believe moved faster, participants did not reliably indicate the successful target.

Hyperlocal marketing: conceptual representation status, technological foundations and directions of development.

Hyperlocal marketing is a modern format of interacting with customers in the offline segment of retail and services, aimed at increasing sales by optimising strategies for promoting specific products. In conditions of market saturation, traditional marketing methods become less effective, so the use of new approaches allows companies to attract customers on a point-to-point basis and adapt their offers to specific locations and audience needs. Over the past decade, interest in hyperlocal marketing as a business development tool has grown in Russia, and at the same time it has become almost entirely expressed exclusively in practical terms. Its scientific implementation has not yet been recorded. The purpose of this study is to systematise the existing concepts and technologies in the field of hyperlocal marketing in order to identify its current state and prospects for development. Within the framework of the defined vector, both the technological prerequisites and requirements of this tool for ensuring the basic process in the field of offline retailing are considered, and an empirical basis for the inductive-deductive analysis of the subject field for the formation of patterns of consumer behaviour management and optimisation of the marketing policy of business agents is formed. In the course of the work, it was found that hyperlocal marketing in retail is highly dependent on the development of the Internet of Things (IoT). The use of such technologies can improve customer interaction, increase service levels and predict consumer behaviour patterns. Based on the considered instrumental and technological solutions discussed above, companies are able to create personalised marketing strategies and optimise business processes to maximise profits.

One‐Fit‐All Transformer for Multimodal Geophysical Inversion: Method and Application

AbstractThe increasing variety of geophysical data sets enhances inversion constraints but also poses significant challenges for conventional methods in terms of precision and efficiency due to factors like non‐linearity and limited observation coverage. While deep learning has the potential to address these challenges, issues such as data representation and domain transformation limit the development of universally applicable deep learning‐based inversion methods across various geophysical scenarios. To address these issues, we propose a novel Transformer‐based inversion framework named G(eophysics)‐Query for multimodal geophysical data by expanding the vector representation concept and incorporating query adjusting in the attention mechanism. It can adapt to diverse inversion settings—across different observation systems, targets, and prior information—within a single network, and thus eliminates the need for specific design adjustments and repeated training. Additionally, the framework incorporates a novel semi‐supervised training strategy to explore empirical relationships for prediction uncertainty estimation, ultimately providing more comprehensive inversion results. The framework is applied in the joint inversion of surface waves and receiver functions and successfully obtains the lithospheric structure of the conterminous U.S., demonstrating high precision, efficiency, and universal capability.

DOME: Directional medical embedding vectors from Electronic Health Records.

The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHR-embedding.

Path-based Design Model for Constructing and Exploring Alternative Visualisations.

We present a path-based design model and system for designing and creating visualisations. Our model represents a systematic approach to constructing visual representations of data or concepts following a predefined sequence of steps. The initial step involves outlining the overall appearance of the visualisation by creating a skeleton structure, referred to as a flowpath. Subsequently, we specify objects, visual marks, properties, and appearance, storing them in a gene. Lastly, we map data onto the flowpath, ensuring suitable morphisms. Alternative designs are created by exchanging values in the gene. For example, designs that share similar traits, are created by making small incremental changes to the gene. Our design methodology fosters the generation of diverse creative concepts, space-filling visualisations, and traditional formats like bar charts, circular plots and pie charts. Through our implementation we showcase the model in action. As an example application, we integrate the output visualisations onto a smartwatch and visualisation dashboards. In this article we (1) introduce, define and explain the path model and discuss possibilities for its use, (2) present our implementation, results, and evaluation, and (3) demonstrate and evaluate an application of its use on a mobile watch.

Representation of an aggrieved legal entity that has suffered damage as a result of a criminal offence at the stage of pre-trial investigation

The article defines the concept and essence of representation of a legal entity in respect of which a criminal offence or a socially dangerous act has been committed. It is proposed that representation of a legal entity in respect of which a criminal offence or a socially dangerous act has been committed should be understood as a type of procedural activity of a legally authorized entity whose powers are duly certified and consist in ensuring the exercise of rights and obligations of this legal entity. It is determined that the essence of the activities of a legal entity's representative corresponds to the activities of a victim in criminal proceedings, since the involved representative enjoys the rights and obligations guaranteed by law to the victim. That is, at the stage of pre-trial investigation, he or she is authorised to file statements and complaints, submit evidence, participate in investigative (search) and procedural actions, receive copies of criminal proceedings and procedural decisions, etc. This article establishes that representation of legal entities in criminal proceedings requires two grounds: factual, which implies that a criminal offence has been committed against a legal entity which caused property damage, and legal, which implies the existence of a relevant document provided for in Part 3 of Article 58 of the CPC of Ukraine, by which a specific person is authorised to take actions aimed at exercising the rights and legitimate interests of a legal entity. The textual analysis of the provisions of Part 1 of Art. 303 of the CPC of Ukraine provided the basis for stating that the wording used by the legislator in paragraph 1, in particular, regarding the possibility of appealing against the inaction of an investigator, detective, or prosecutor, which consists in the failure to perform other procedural actions which he or she is obliged to perform within the time limit specified by the CPC of Ukraine, is incorrect. The emphasis is placed on the fact that, given the laws of formal logic, it may be subject to an extended interpretation, and therefore is the basis for the formation of ambiguous case law. In addition, the typical mistakes made by persons involved in representation of a legal entity are summarised.

Citizens’ Preferences for Multidimensional Representation

How do citizens want to be represented in politics? We investigate citizens’ multidimensional preferences regarding six conceptions of representation that are derived from political theory. Using original item batteries and a conjoint experiment, we elicit the relative importance of the dimensions and the types of representation people prefer on each dimension. Our results from surveys fielded in the United States, the United Kingdom, and Germany show that 1) descriptive representation has comparatively limited appeal for citizens at large, but is more important for historically marginalized groups; 2) citizens do not focus on local politicians when thinking about who represents them, but also seek representation from politicians in other districts; 3) while citizens strongly value substantive representation, they are largely indifferent as to whether their representatives are responsive to electoral sanctions. Our findings have important implications for how political scientists study democratic representation.

Understanding Knowledge Representation Concepts in Machine Translation: A Review

Understanding Knowledge Representation Concepts in Machine Translation: A Review

A Constituição de 1824

Brazilian constitutionalism was similar in many aspects, in the diferent projects presented in the country, to European and North America ones. In this way, there were many symmetries, from the philosophical and scientific background of a world governed by natural laws, to the idea of a social contract, the affirmation of rights, the abolition of the Societé des Ordres and the organization of the State. There were, however, singularities or Brazilian adaptations. Such as the concept of political representation shared between the Nation and the Emperor and the proposal of a “Poder Moderador”. In the Constitution of 1824, a model of moderate monarchy prevailed, without republicanism nor a return to the Old Regime. It was a compromisse solution that considered three of the challenges faced at the time of Independence: slavery, large estates and foreign dependance.

Luang Prabang Mural Paintings: Social and Cultural Reflection, Lao PDR

The study entitled Luang Prabang Mural Painting: Social and Cultural Reflection, Lao PDR is the study of the mural paintings in Luang Prabang and the analysis of the social and cultural reflection from the mural paintings in Luang Prabang, Lao PDR. The research adopts both qualitative and quantitative methods. Studying the mural paintings in Luang Prabang, created during the Lao Lan Xang era under Siamese rule up to the present day (1779 to the present), covering eight temples. The target group consisted of the informants in Luang Prabang City, and the research tools consisted of surveys, observation, and interviews. The data was analyzed through the representation concept, structural-functionalism theory, and cultural diffusion theory, presented through descriptive analysis. The study found that the characteristic of the mural paintings in Luang Prabang, consisting of the form, colors, techniques, and composition, was artistically influenced by the surrounding kingdoms. Starting from the Lan Xang kingdom, until the era Laos was under Siam, where the Rattakosin artistic influence came to play a significant role in the development of arts and the integration of the traditional Lao art, creating an identity of Luang Prabang arts. As for the social reflection in the mural paintings, according to the Lao historical context from Lan Xang to the present, there are four significant social aspects: social kinship system, political relationships, transnational economic trades, and cultural tourism as a world heritage site. There are four aspects to the cultural reflection of the mural paintings in Luang Prabang: cultural livelihood and way of life system, cultural consciousness or social system, cultural intelligence and thought systems, and arts and culture or aesthetic system.

Integrating Citizen Participation in the Development of New ICT Services for Smart Cities

The transition of cities towards a smarter approach significantly benefits from citizen participation in the development and implementation of innovative information and communication technology (ICT) products and services. Despite the emergence of various initiatives in recent years aimed at guiding the development of smart cities, there is still a lack of effective strategies to actively engage citizens, businesses, and educational institutions during the creation of these products and services. This study describes a set of practices that includes four co-creation techniques to facilitate the effort of software system development in collaboration with citizens and other stakeholders. The SEMAT standard is used to create and represent a method in which these practices are distributed across four stages: focus, definition, development, and validation. In each stage, a practice is proposed that incorporates a co-creation technique and complementary activities from various software engineering disciplines to promote active citizen participation; stimulate idea generation; and facilitate the creation of necessary documents and components for the development of the desired software system, including design systems, code files, conceptual representations, and technical diagrams, among others. Finally, the applicability and completeness of the method are validated through expert consultation in the fields of software engineering and smart cities. Recognized procedures are followed to obtain qualitative and quantitative results, such as improvement actions (addition or removal of elements), levels of consensus or acceptance, and opportunities for future work.

The Effect of Human Presence on the Representations of Children 4-6 Years Old in the Case of Air Within Vases

A distinctive direction within the framework of Early Childhood Science Education research is the study of young children's mental representations of natural world phenomena and concepts in science. The study of representations offers the possibility of developing activities that address the real difficulties of children and lead to the construction of new representations compatible with school science knowledge. The current research presents a study on the potential effect of the presence of humans on the representations of children aged 4-6 years regarding the case of air within vases. 41 children attending a kindergarten in Patras (Greece) participated in the study. During individual semi-structured interviews, children were presented with 3 different images displaying a vase with people standing next to it in different settings and were asked to indicate whether there is air in the vase and whether this is related to that people. The results showed that the presence of air in the containers was best identified in the outdoor environment, while few children attributed the air to human presence, especially when the vases were placed indoors. Based on the frequency table, it seems that regarding the outdoor environment, there are more adequate responses, as the presence of human elements influences less the participants, while in indoor settings there are more intermediate and inadequate responses, suggesting that the presence of the human element has a greater impact in participant's responses.

Graph analysis of guilt processing network highlights links with subclinical anxiety and self-blame.

Maladaptive forms of guilt, such as excessive self-blame, are common characteristics of anxiety and depressive disorders. The underlying network consists of multiple associative areas, including the superior anterior temporal lobe (sATL), underlying the conceptual representations of social meaning, and fronto-subcortical areas involved in the affective dimension of guilt. Nevertheless, despite understanding the circuitry's anatomy, network-level changes related to subclinical anxiety and self-blaming behaviour have not been depicted. To fill this gap, we used graph theory analyses on a resting-state functional and diffusion-weighted magnetic resonance imaging dataset of 78 healthy adults (20 females, 20-35 years old). Within the guilt network, we found increased functional contributions of the left sATL for individuals with higher self-blaming, while functional isolation of the left pars opercularis and insula was related to higher trait anxiety. Trait anxiety was also linked to the structural network's mean clustering coefficient, with the circuitry's architecture favouring increased local information processing in individuals with increased anxiety levels, however, only when a highly specific subset of connections was considered. Previous research suggests that aberrant interactions between conceptual (sATL) and affective (fronto-limbic) regions underlie maladaptive guilt, and the current results align and expand on this theory by detailing network changes associated with self-blame and trait anxiety.

Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations

Abstract Current open-domain neural semantics parsers show impressive performance. However, closer inspection of the symbolic meaning representations they produce reveals significant weaknesses: Sometimes they tend to merely copy character sequences from the source text to form symbolic concepts, defaulting to the most frequent word sense based in the training distribution. By leveraging the hierarchical structure of a lexical ontology, we introduce a novel compositional symbolic representation for concepts based on their position in the taxonomical hierarchy. This representation provides richer semantic information and enhances interpretability. We introduce a neural “taxonomical” semantic parser to utilize this new representation system of predicates, and compare it with a standard neural semantic parser trained on the traditional meaning representation format, employing a novel challenge set and evaluation metric for evaluation. Our experimental findings demonstrate that the taxonomical model, trained on much richer and complex meaning representations, is slightly subordinate in performance to the traditional model using the standard metrics for evaluation, but outperforms it when dealing with out-of-vocabulary concepts. We further show through neural model probing that training on a taxonomic representation enhances the model’s ability to learn the taxonomical hierarchy. This finding is encouraging for research in computational semantics that aims to combine data-driven distributional meanings with knowledge-based symbolic representations.

Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules.

Drug-induced liver injury, or DILI, affects numerous patients and also presents significant challenges in drug development. It has been attempted to predict DILI of a chemical by in silico approaches, including data-driven machine learning models. Herein, we report a recent DILI deep-learning effort that utilized our molecular representation concept by manifold embedding electronic attributes on a molecular surface. Local electronic attributes on a molecular surface were mapped to a lower-dimensional embedding of the surface manifold. Such an embedding was featurized in a matrix form and used in a deep-learning model as molecular input. The model was trained by a well-curated dataset and tested through cross-validations. Our DILI prediction yielded superior results to the literature-reported efforts, suggesting that manifold embedding of electronic quantities on a molecular surface enables machine learning of molecular properties, including DILI. The concept encodes the quantum information of a molecule that governs intermolecular interactions, potentially facilitating the deep-learning model development and training.

Skinfolk, but Not Kinfolk? Paradoxical Representation Among Ethnic Minority Conservative Political Elites in the UK

As the number of ethnic minority politicians increase across countries like the United Kingdom and the United States, so too have instances in which these officeholders act against the communities they descriptively represent. In this contribution, we introduce the concept of paradoxical representation which we argue functions through neoliberal, post-racial scripts of color-blindness and meritocracy. Similar to research on gender representation which calls into question assumptions that substantive representation will follow unproblematically from women’s descriptive representation (Celis and Childs 2012), we argue that ethnic minority representatives can act as “post-racial gatekeepers.” This means paradoxically working against rather than for marginalized ethnic minority groups (Saini, Bankole, and Begum 2023). Through political discourse and policymaking, these representatives construct and “gatekeep” hegemonic ideas around race, racism, gender, and migration.

DEVELOPMENT OF TEACHING MATERIALS TO SUPPORT THE MERDEKA CURRICULUM IN REDOX REACTIONS AND ELECTROCHEMISTRY FOR PHASE F SENIOR HIGH SCHOOL

AbstractThe research aims to evaluate the development of teaching materials that incorporate multiple representations of chemical concepts, specifically on the topic of redox and electrochemistry, to support the Merdeka Curriculum and enhance student’s mental models in Phase F of senior high school. Educational Design Research (EDR) was the chosen methodology and the study involved three university chemistry lecturers, two chemistry teachers, and nine students. Data collection was carried out using validity and practicality questionnaires. The results revealed that the average construct validity score of 0.87 and the average content validity score of 0,86 classified the materials as valid. Furthermore, the practicality assessment showed that this teaching material received a high practicality score, with a score of 97% from teachers and 92% from students indicating ease of application in learning. In conclusion, the teaching materials developed for redox and electrochemistry lessons, incorporating multiple chemical representations, were proven to be both valid and highly practical, supporting the goals of the Merdeka Curriculum in fostering deeper student understanding.

Representation of Religiously Marked Allusion as a Literary Concept in the English Literary Text

The article examines the literary concept, its characteristics and the embodiment of the peculiar features of literary concepts in religiously marked allusions in the literary text. In particular, the author clarifies the theoretical foundations of the literary concept, which is of huge importance in cognitive linguistics, text linguistics and literary studies and is at target of many scientific researches. The aim of the research is to justify that literary concept as a complex mental unit embraces a set of specificities all of which are transmitted to the semantic construct of religiously marked allusion - the verbal explication of literary concept in the literary text. The topicality and novelty of the research lies in the fact that the author scrutinizes the notion of concept from the perspectives of cognitive and literary approaches, generalizing all of the peculiarities of literary concept based on the review of the prior linguistic investigations. In addition, the author studies religiously marked allusions as the linguistic units that externalize literary concepts in the literary text, and assures that these linguistic units manifest all of the features inherent in the literary concept. To reach the research aim, the representation of literary concepts via religiously marked allusions is investigated on the basis of the analysis of a literary text. In the process of analysis, the researcher widely uses the method of conceptual analysis. According to the results of the analysis, religiously marked allusions representing literary concepts explicate the author’s individual conceptual world picture and his/her modality in the literary text.

AI-Powered VR for Enhanced Learning Compared to Traditional Methods

This paper evaluates a VR (Virtual Reality) application aimed at enhancing the learning of Python collection data types and structures for electrical and electronic engineering students. By incorporating gamification and personalisation features, the application provides an immersive environment where students can interact with virtual representations of complex programming concepts. To further enhance interactivity and engagement, the application integrates a virtual assistant and example generator, developed using Meta Voice SDK (Software Development Kit) and wit.ai. These AI (Artificial Intelligence)-NLP (Natural Language Processing) tools create personalised learning paths and generate dynamic examples based on individual learning progress. A user study was conducted with a total of 48 participants. During the user study, participants were divided into two equal groups of 24, both wearing EEG (Electroencephalography) headsets: one group engaged with the VR application, while the other read the traditional booklet, allowing for the recording and analysis of attention and engagement levels. These measures of engagement and attention were then compared to those extracted from a benchmark cohort of students whose learning experience was through more traditional booklets. The results indicated a statistically significant improvement in understanding Python collections among VR users compared to their baseline scores, highlighting the benefits of interactive and tailored learning environments. Additionally, EEG data analysis showed that VR users exhibited higher average levels of attention and engagement compared to those using the paper-based method, demonstrating the effectiveness of immersive technologies in sustaining learner interest and focus, particularly in enhancing learning for software development.