Perspective Of Representation Research Articles

КОЛОНИАЛЬНЫЙ ДИСКУРС В ХУДОЖЕСТВЕННОЙ ПРОЗЕ ЮГРЫ

The article presents an outline of the gradual development of the literature of the Khanty-Mansiysk Autonomous Okrug — Yugra from the perspective of representation of colonial issues. It is noted that the literary reception of the processes of external and internal colonization of the Middle Ob region falls on the initial stage (1930s) of the formation of the regional fiction and expresses the idea of the need for the oncoming movement of different cultures as the most important condition for the prosperous of man’s existence on Yugra land. In the post-war period, the idea associated with the inevitability of a fundamental restructuring of the traditional consciousness of the indigenous population in new cultural and historical conditions is further developed and embodied in the image of a new man of the North, striving for intellectual and spiritual development, able to keep up with the times. The problem of a person’s choice of his life path in a rapidly changing reality becomes especially acute in the 1960s–1980s Yugra literature against the backdrop of social processes associated with the industrial development of the Tyumen North. At the turn of the 20th–21st centuries, the critical perspective of understanding the consequences of those processes that radically changed the life of the peoples of the Middle Ob region sharply increases and often actualizes apocalyptic motives in the structure of the Yugra writers’ literary narrative. In recent years, the colonial discourse in the works of the Yugra writers has shown a trend towards a decrease in socio-political severity and an increase in the role of the ontological and mythological context. Increasingly, the region is being positioned as part of the all-Russian and world space, where lifecreating energy is concentrated, capable of saving humanity from a global catastrophe.

GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning

Few-shot learning (FSL), purposing to resolve the problem of data-scarce, has attracted considerable attention in recent years. A popular FSL framework contains two phases: (i) the pre-train phase employs the base data to train a CNN-based feature extractor. (ii) the meta-test phase applies the frozen feature extractor to novel data (novel data has different categories from base data) and designs a classifier for recognition. To correct few-shot data distribution, researchers propose Semi-Supervised Few-Shot Learning (SSFSL) by introducing unlabeled data. Although SSFSL has been proved to achieve outstanding performances in the FSL community, there still exists a fundamental problem: the pre-trained feature extractor cannot adapt to the novel data flawlessly due to the cross-category setting. Usually, large amounts of noises are introduced to the novel feature. We dub it as Feature-Extractor-Maladaptive (FEM) problem. To tackle FEM, we make two efforts in this paper. First, we propose a novel label prediction method, Isolated Graph Learning (IGL). IGL introduces the Laplacian operator to encode the raw data to graph space, which helps reduce the dependence on features when classifying, and then project graph representation to label space for prediction. The key point is that: IGL can weaken the negative influence of noise from the feature representation perspective, and is also flexible to independently complete training and testing procedures, which is suitable for SSFSL. Second, we propose Graph Co-Training (GCT) to tackle this challenge from a multi-modal fusion perspective by extending the proposed IGL to the co-training framework. GCT is a semi-supervised method that exploits the unlabeled samples with two modal features to crossly strengthen the IGL classifier. We estimate our method on five benchmark few-shot learning datasets and achieve outstanding performances compared with other state-of-the-art methods. It demonstrates the effectiveness of our GCT.

Task switching involves working memory: Evidence from neural representation.

It is generally assumed that task switching involves working memory, yet some behavioral studies question the relationship between working memory and task switching ability. This debate can be resolved by directly comparing the brain activity pattern in task switching and working memory processes. If the task switching involves working memory, the neural activity patterns evoked by such two tasks would exhibit higher similarity. Here, we employed the task switching task and working memory to investigate the characteristic of the neural representation in such two cognitive processes. A conjunction analysis showed that the bilateral superior parietal lobule (SPL), bilateral insula, bilateral middle frontal gyrus (MFG), bilateral dorsal lateral prefrontal cortex (DLPFC) and pre-supplementary motor area (pre-SMA) were commonly and significantly activated in both task switching and working memory task. Critically, we found that task switching and working memory processing elicited similar activity patterns in bilateral SPL, right insula, left MFG, left DLPFC and pre-SMA, consistent with common neural processes for both tasks. These results not only suggest that the task switching process involves working memory from the perspective of neural representation, but also provide major new insights into the neurocognitive links between task switching and working memory.

Contextualizing Gender Disparity in Editorship in Psychological Science

Discourse on gender diversity tends to overlook differences across levels of hierarchy (e.g., students, faculty, and editors) and critical dimensions (e.g., subdisciplines and geographical locations). Further ignored is its intersection with global diversity—representation from different countries. Here we document and contextualize gender disparity from perspectives of equal versus expected representation in journal editorship, by analyzing 68 top psychology journals in 10 subdisciplines. First, relative to ratios as students and faculty, women are underrepresented as editorial-board members (41%) and—unlike previous results based on one subfield—as editors-in-chief (34%) as well. Second, female ratios in editorship vary substantially across subdisciplines, genres of scholarship (higher in empirical and review journals than in method journals), continents/countries/regions (e.g., higher in North America than in Europe), and journal countries of origin (e.g., higher in American journals than in European journals). Third, under female (vs. male) editors-in-chief, women are much better represented as editorial-board members (47% vs. 36%), but the geographical diversity of editorial-board members and authorship decreases. These results reveal new local and broad contexts of gender diversity in editorship in psychology, with policy implications. Our approach also offers a methodological guideline for similar disparity research in other fields.

An Outline of Certain Generic Values - Work Dimensions

Abstract Background/Purpose: Values-related issues have come into the focus of leadership thinking in the past few decades and it seems to be paradoxical why values work has not been more extensively used so far for defining and conceptualizing leadership. A reason for this can be that values-oriented research streams normally approach leadership from specific perspectives of values representation and transfer. Alternatively, this study examines values work from a generic perspective. Its goal is to suggest a generalized notion for values work and outline certain generic values-work dimensions. Methods: Analysis in this theoretical paper is primarily based on Institutional, New Leadership, and Values-Oriented theories. Methods include argumentation and analytical framework development. Results: This study presents a structured list of and an illustrative framework for some of the dimensions of values work as broadly defined and has research implications regarding issues of leadership influence and the demarcation of leadership from management. Conclusion: The importance of values representation in contrast to power/influence perspectives in leadership is underlined. Our study points to the necessity for more research on generic aspects of values work. The results can also be used for leadership practice, consulting, and development.

Hypergeometric functions over finite fields

Building on the developments of many people including Evans, Greene, Katz, McCarthy, Ono, Roberts, and Rodriguez-Villegas, we consider period functions for hypergeometric type algebraic varieties over finite fields and consequently study hypergeometric functions over finite fields in a manner that is parallel to that of the classical hypergeometric functions. Using a comparison between the classical gamma function and its finite field analogue the Gauss sum, we give a systematic way to obtain certain types of hypergeometric transformation and evaluation formulas over finite fields and interpret them geometrically using a Galois representation perspective. As an application, we obtain a few finite field analogues of algebraic hypergeometric identities, quadratic and higher transformation formulas, and evaluation formulas. We further apply these finite field formulas to compute the number of rational points of certain hypergeometric varieties.

A mathematical perspective of ultrasound image representations and image quality criteria

What is an ultrasound “image”? Where an engineer or physicist may see a quantitative map of physical properties, a physician may see qualitative anatomical features and pathologies. In the past, each image type was evaluated appropriately within its respective context: quantitative image quality was measured with criteria like the contrast-to-noise ratio (CNR), and qualitative images underwent dynamic range transformations to optimize human perception of contrast. Recent non-linear beamforming methods have blurred this line: the produced images are transformed like qualitative images but evaluated with quantitative criteria like CNR. Here, we identify the mathematical flaws in reasoning and suggest rigorous alternatives.An image assigns values to spatial coordinates. Let X denote the set of all possible image values. The mathematical structure endowed on X determines the set of equivalence-preserving transformations. For instance, qualitative images treat X as a general topological space, where images are equivalent under homeomorphisms of X (dynamic range transformations). Quantitative images treat X as a metric space and have fewer equivalence-preserving transformations. These structures determine when criteria like contrast, CNR, generalized CNR, and spatial resolution can be used. To extrapolate these criteria to other contexts, one must use relevant structure-preserving isomorphisms (e.g., histogram matching to preserve information content).

Similarity Calculation via Passage-Level Event Connection Graph

Recently, many information processing applications appear on the web on the demand of user requirement. Since text is one of the most popular data formats across the web, how to measure text similarity becomes the key challenge to many web applications. Web text is often used to record events, especially for news. One text often mentions multiple events, while only the core event decides its main topic. This core event should take the important position when measuring text similarity. For this reason, this paper constructs a passage-level event connection graph to model the relations among events mentioned in one text. This graph is composed of many subgraphs formed by triggers and arguments extracted sentence by sentence. The subgraphs are connected via the overlapping arguments. In term of centrality measurement, the core event can be revealed from the graph and utilized to measure text similarity. Moreover, two improvements based on vector tunning are provided to better model the relations among events. One is to find the triggers which are semantically similar. By linking them in the event connection graph, the graph can cover the relations among events more comprehensively. The other is to apply graph embedding to integrate the global information carried by the entire event connection graph into the core event to let text similarity be partially guided by the full-text content. As shown by experimental results, after measuring text similarity from a passage-level event representation perspective, our calculation acquires superior results than unsupervised methods and even comparable results with some supervised neuron-based methods. In addition, our calculation is unsupervised and can be applied in many domains free from the preparation of training data.

Embodied processing during social interactions: From a perspective of self-other shared representation

<p indent="0mm">In interpersonal situations, a person perceives the social scene not only from an egocentric (self-centered) perspective but also from an altercentric (other-centered) perspective. Research on embodied cognition indicates that one important way to achieve mutual understanding in social interactions is embodied processing, during which people read a wealth of bodily information from others, and make use of such information to effectively interpret their behaviors, emotions, and mental states. Although “embodied processing” has been widely implied in various social processes and is crucial to successful social cognition, there has been a lack of clarity regarding this concept, and a mechanical understanding of how bodily information is used in such processing remains unclear. In this paper, we reviewed relevant literature on embodied processing with an emphasis on analyzing its possible mechanisms. We first reviewed the concept of embodiment, based on which we highlighted a hierarchical understanding of embodied processing. At the neurophysiological level, existing theories and empirical studies indicate self-other shared representation as the foundation for “embodied” understanding of others and the Mirror Neuron system as the neural basis of such shared representation. Specifically, social interactions evoke the internal representations of the observer’s body states, which are associated with others’ expressions, actions, or mental states, as if the individual was going through the same psychological experience simultaneously. We then summarized the brain regions and neural connections of the shared representations. Instead of one unified system, there are two processing levels when it comes to representing self-other interactions. The lower level is activated in situations when we execute and imitate others’ actions, and the higher level is activated when reasoning and emotional components are involved, while the brain regions in charge of these levels are also highly interconnected. As the core neural foundation for embodied processing in social interactions, such evidence of shared self-other representation has been from or implied in social cognition research that focuses on three different aspects: behavior, emotion, and mentalization. Therefore, we reviewed research focusing on these aspects, specifically, behavioral imitation, empathy, and emotional mimicry, as well as perceptual perspective-taking, to scrutinize the understanding of embodied processing. In behavior imitation research, the observation of other people’s physical movements and postures is considered the basis of imitating actions, as such bodily information—Especially bodily representations—Is deeply engaged in the relevant social processing. Observation and imitation have also been considered as the stage in which embodied processing occurs. The embodied characteristic of behavioral imitation serves as a foundation of other important social cognitive functions. For example, the process of empathy is inseparable from the observation and identification of other people’s facial expressions and emotional physical states. Similarly, in visual perspective-taking, one may mentally project their bodily self to the agent’s physical location and seemingly experience direct visual input from the corresponding perspective, as the agent does. These reviewed studies jointly highlight that underlying shared self-other representation allows for approaching other minds in an embodied manner. It enables individuals to make full use of their own experience and knowledge, and achieve social understanding directly and effortlessly. However, behavioral mechanisms of embodied processing, for example, in empathy or perspective-taking, are largely understudied. Another potential limitation is that social interactions in research have often been simplified. The field could benefit from more ecological contexts in the research design. In conclusion, we combined different research and experimental evidence to bring out a general neural mechanism of embodied processing in social interactions. Our review helps to extend current knowledge of the nature of “understanding others” and offers insights as well as future directions on relevant social cognition research.

Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal

Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent label noise on a binary classification task: quality assessment for photoplethysmography (PPG). PPG signal is used to detect physiological changes and its quality can have a significant impact on the subsequent tasks, which makes PPG quality assessment a particularly good target for examining the impact of label noise in the field of biomedicine. Random and class-dependent label noise was introduced separately into the training set to emulate the errors associated with fatigue and bias in labeling data samples. We also tested different representations of the PPG, including features defined by domain experts, 1D raw signal and 2D image. Three different classifiers are tested on the noisy training data, including support vector machine (SVM), XGBoost, 1D Resnet and 2D Resnet, which handle three representations, respectively. The results showed that the two deep learning models were more robust than the two traditional machine learning models for both the random and class-dependent label noise. From the representation perspective, the 2D image shows better robustness compared to the 1D raw signal. The logits from three classifiers are also analyzed, the predicted probabilities intend to be more dispersed when more label noise is introduced. From this work, we investigated various factors related to label noise, including representations, label noise type, and data imbalance, which can be a good guidebook for designing more robust methods for label noise in future work.

Discovery of topic evolution path and semantic relationship based on patent entity representation

PurposeThis paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.Design/methodology/approachFirstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.FindingsIn the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.Originality/valueThis work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective

Big data applications are at the epicentre of recent breakthroughs in digital health. However, controversies over privacy, security, ethics, accountability, and data governance have tarnished stakeholder trust, leaving health-relevant big data projects under threat, delayed, or abandoned. Taking the notion of big data as social construction, this work explores the social representations of the big data concept from the perspective of stakeholders in Kenya’s digital health environment. Through analysing the similarities and differences in the way health professionals and information technology (IT) practitioners comprehend the idea of big data, we draw strategic implications for restoring confidence in big data initiatives. Respondents associated big data with a multiplicity of concepts and were conflicted in how they represented big data’s benefits and challenges. On this point, we argue that peculiarities and nuances in how diverse players view big data contribute to the erosion of trust and the need to revamp stakeholder engagement practices. Specifically, decision makers should complement generalised informational campaigns with targeted, differentiated messages designed to address data responsibility, access, control, security, or other issues relevant to a specialised but influential community.

Resident reactions to a pandemic: the impact on community-based tourism from social representation perspective

ABSTRACT As COVID-19 has spread throughout the world, it has caused unprecedented disruption to community-based tourism (CBT). The purpose of this study is to identify local reactions to COVID-19 in CBT destinations using social representation theory (SRT) as a framework for analysis. Furthermore, this paper uses Q methodology to shed light on residents’ reactions to COVID-19. A demonstration of this method is used to analyze a Q set of 36 statements and a P set of 30 respondents working in the CBT business in Seochon Village, Seoul, Korea from 10 May to 21 May in 2021. As a result of the analysis, three distinct clusters are identified: fear of stigmatization as a plague spreader, maladaptation to environmental change, and threats to livelihoods. Based on the findings, the theoretical and practical implications are discussed to aid the local community to build a more sustainable and safer CBT during and after the pandemic.

Crônicas de Milton Hatoum: dialogismo e emancipação na pósmodernidade

ABSTRACT This study analyzes Milton Hatoum’s narrative using the crônicas Um sonhador [A Dreamer] and Margens secas da cidade [Dry Riverbanks of the City] as corpus. It aims to ascertain the discursive and aesthetic characteristics of Hatoum’s literary creation from the dialogical perspective proposed by Mikhail Bakhtin, exploring the artistic-literary discourse of the Amazonian writer, the axiological positions refracted in his narrative. It also relates Boaventura Santos’ epistemological perspective about the waste of social experience in modernity/post-modernity, discussing the criticism of the paradigm of current rationality called by the author as indolent reason. Initial analysis based on these theoretical-methodological approaches show evidence in Hatoum’s cronistic narrative that brings it closer to a counter-hegemonic and responsive perspective of literary representation. Thus, with this inter-relationship among literature, dialogism and theory of knowledge, we intend to highlight the existing overlaps between the socio-historical and economic discourse produced by the critique of modernity / postmodernity and literary discourse of Milton Hatoum.

A semi-supervised label-driven auto-weighted strategy for multi-view data classification

Distinguishing the importance of views plays a key role in multi-view learning as each view often contributes differently to a specific task. However, existing strategies generally attach importance to each view blindly from a data representation perspective, which often fails to identify low-quality views then leading to poor performance. In this paper, by establishing a link between labeled data and the importance of different views, we propose a semi-supervised label-driven auto-weighted strategy that evaluates the importance of views from a labeling perspective to avoid the negative impact of unimportant or low-quality views. Based on this strategy, we propose a transductive semi-supervised auto-weighted multi-view classification model that learns the labels of multi-view data under the more reasonable weights of the views. The model is decoupled into three small-scale sub-problems that can efficiently be optimized. The experimental results on classification tasks show that our proposed method achieves very promising classification accuracy at a lower computational cost compared to other related methods, and the weight change experiments show that our proposed strategy can distinguish view importance more accurately than other related strategies especially when low-quality views are involved in multi-view learning.

Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits

A range of perceptual and cognitive processes have been characterized from the perspective of probabilistic representations and inference. To understand the neural circuit mechanism underlying these probabilistic computations, we develop a theory based on complex spatiotemporal dynamics of neural population activity. We first implement and explore this theory in a biophysically realistic, spiking neural circuit. Population activity patterns emerging from the circuit capture realistic variability or fluctuations of neural dynamics both in time and in space. These activity patterns implement a type of probabilistic computations that we name fractional neural sampling (FNS). We further develop a mathematical model to reveal the algorithmic nature of FNS and its computational advantages for representing multimodal distributions, a major challenge faced by existing theories. We demonstrate that FNS provides a unified account of a diversity of experimental observations of neural spatiotemporal dynamics and perceptual processes such as visual perception inference, and that FNS makes experimentally testable predictions.

The Representation of Fatherhood Identity on Netflix Cinema

This study aims to investigate how a man constructs his masculinity stereotype as a father and caregiver as represented in the original Netflix’s Fatherhood. This research is qualitative with Stuart Hall's Perspectives of Representation as an approach. This perspective is to find the meaning of American fatherhood as represented in the film and to explore how the meaning of fatherhood significantly affects the global meaning of transnational communication in media. Qualitative means that a film as well as text is read from its language, image, and other aspects such as color, lighting, composition, articulation details, and role positions are taken consideration in finding meaning. The results of the study show that the fatherhood concept symbolizes ideal masculinity, anti-thesis of traditional masculinity, develops hybrid masculinity, rejects femininity as well as hegemonic masculinity, and pro-gender equality. In conclusion, the fatherhood concept inspires non-America films such as Indonesian and Indian films. This research proves that a film is made not only for economic aspects or a visual product that is intended to entertain the audience, but a film is a text that can be read, evaluated, and analyzed in depth through various approaches since a film represents a reality.

Inviting the Spectral in Photos: A Poststructuralist Engagement With Visual Data in Qualitative Inquiry

In this article, we share some poststructuralist ideas for interacting and intra-acting with photos as visual data in qualitative inquiry. The discussion of photos as visual data has mostly drawn from representational perspectives. Furthermore, poststructuralist theorizing has been mostly utilized to discuss discursive data. Drawing from poststructuralist ideas, like Derrida’s discussion of the absent-presence, we offer a poststructuralist glance at photos as spectral visual data. We view such approach as liberating of visual texts and as denotative of the unfinalizability of meaning. To illustrate our point, we engage into a dual mode of narrative, of both words and images, that is photos. Following a brief overview situating the use of photos as data within a representational framework in qualitative research, we illustrate our poststructuralist glance inspired by a Derridean framework. As an epilogue, we share reflections in the form of questions aiming to challenge our narrative in this article.

Implicit Body Representation of the Hand Enlarged by Repetitive Peripheral Magnetic Stimulation within the Boundary of a Real Hand

Deafferentation induced by local anesthesia causes a larger perceived area than the real area of the mouth, which, in the perspective of body representation, belongs to implicit body representation. In this study, we applied repetitive peripheral magnetic stimulation (rPMS) on the motor branch of the radial nerve of participants’ non-dominant-side forearm to induce extension movements of wrist and fingers. This intervention was supposed to increase proprioception to the brain and had an enlargement effect on implicit body representation of the hand in our hypothesis. A total of 39 participants were randomly allocated to the real rPMS group (n = 19) or the sham rPMS group (n = 20). Implicit representation of the hand was measured by a simplified paradigm based on the proposal of Longo and Haggard that depicted perceived locations of fingertips and metacarpophalangeal joints of participants’ occluded hand, in which they showed that implicit body representation of the hand was smaller than the real hand. We compare the main effect of real rPMS vs. sham rPMS and its interaction effect with time by setting four timepoints—before stimulation, right after stimulation, 10 min after stimulation and 20 min after stimulation—to demonstrate the possible short-lasting effect. Results showed that real rPMS had a short-lasting enlargement effect on implicit representation of the hand in general, which was significant especially on the ulnar side of fingers. What is more, the enlarged implicit body representation of the hand was still within the boundary of a real hand, which might indicate the identification role of a real body part.

Assessing the Need for Semantic Data Integration for Surgical Biobanks-A Knowledge Representation Perspective.

To improve patient outcomes after trauma, the need to decrypt the post-traumatic immune response has been identified. One prerequisite to drive advancement in understanding that domain is the implementation of surgical biobanks. This paper focuses on the outcomes of patients with one of two diagnoses: post-traumatic arthritis and osteomyelitis. In creating surgical biobanks, currently, many obstacles must be overcome. Roadblocks exist around scoping of data that is to be collected, and the semantic integration of these data. In this paper, the generic component model and the Semantic Web technology stack are used to solve issues related to data integration. The results are twofold: (a) a scoping analysis of data and the ontologies required to harmonize and integrate it, and (b) resolution of common data integration issues in integrating data relevant to trauma surgery.