Traditional Interpretation Research Articles

Редукция трансцендентного как основание имагинативного поворота в неопрагматизме

<span lang="EN-US">The article is devoted to the analysis of the conceptual and methodological grounds of the imaginative turn made within the framework of neopragmatist discourse. The specific works of R. Rorty, H. Putnam, and M. Johnson are used as examples to present the features of the interpretation of ethics, moral law, and rationalist tradition. The article emphasizes the process of metaphorization of key concepts of Kant's ethics by the neopragmatists. It is argued that for Putnam Kant's ethics has value first of all for its figurative component, the presence of colorful metaphors based on the core concepts of the Judeo-Christian tradition. In adding, it is suggested to critically evaluate the position of Johnson, who deals in detail with Kant's main moral law, the meaning of the categorical imperative. It is established on the basis of the fact that neopragmatists have reduced the transcendental dimension that the crucial role begins to be taken by the sensuality of the empirical subject and imagination as a moral moderator. The author refers to this phenomenon by the term "imaginative turn". Citing the works of Russian researcher E.N. Ustyugova the author makes a conclusion that for neopragmatists the human being turns out to be an empirical natural phenomenon, and aesthetics is the only fundament for moral judgments.</span>

Continuity and dialogue in the constitutional and legal development of Russia: reflections in the context of the scientific heritage of E. I. Kozlova

The article is an experience of reflection on the meaning and nature of continuity in the constitutional development of the institutions of Russian statehood in the context of the ideological and scientific heritage of Professor E. I. Kozlova. Defending the position according to which continuity is not an imperative-determinant, but a dialogic (discursive) characteristic of constitutional and legal development, involving the identification and interpretation of political and legal traditions, ideals, values in their refraction through the prism of specific historical conditions and perceived hopes, aspirations for the future, the author sees in continuity a source and institution of sovereign self-organization of the people. Following these methodological approaches, which are being confirmed, substantiation in the works of Professor E. I. Kozlova, who managed to remain modern and timely in different historical eras, the author reveals the actual significance of a number of ideas of Professor E. I. Kozlova in relation to understanding the essence of the Constitution, constitutional law in order to gain a confident constitutional future of Russia in legal science, education, regulation and legal practice.

‘The Trees are the Clothes of the Church’: Ethiopian Orthodox Tewahedo Church-Forests as Spiritual Spaces of Resilience against Climate Change?

In times of ecological crises, Ethiopian Orthodox Tewahedo church-forests are, as many suggest, sacred spaces of resilience and restoration. The need for community forest management and sustainable use of natural resources is said to be based on an ‘eco-theology of Edenic forests’ so, we ask, What can be said about conceptually defined entanglements between the theological perspective on so-called forest-churches and the ecologically oriented perspective that applies to so-called church-forests? What is the history and the present self-understanding of this phenomenon? Does it have a global dimension? How does a sacred text like Śənä Fəṭrät, with its theocentric theology and its reference to biblical testimony, create ‘sacred spaces’ in nature? The socio-ethical dimension of these church-forests is of high concern. For this, with an interdisciplinary focus, I refer to results given by existing research on the interrelations of oriental-orthodox theology, including its miaphysite Christology. Such an understanding includes reference to church architecture, biology, sociology, and on forestry in those areas of forest-churches. So we want to understand the specific religious and cultural significance of Ethiopian Orthodox Tewahedo church-forests. The relevance of these unique sacred groves cannot easily be compared to examples in other religions. But we may at least draw conclusions from other forms of Christianity and religiosity and their interpretation of common biblical and dogmatic traditions referring to the ‘creation of God’.

Application of the dynamic transformer model with well logging data for formation porosity prediction

Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, the Dynamic Transformer model in machine learning was introduced in this study, aiming to improve the accuracy and generalization ability of logging porosity prediction. Dynamic Transformer is a deep learning model based on the self-attention mechanism. Compared with traditional sequence models, Dynamic Transformer has a better ability to process time series data and is able to focus on different parts of the input sequence in different locations, so as to better capture global information and long-term dependencies. This is a significant advantage for logging tasks with complex geological structures and time series data. In addition, the model introduces Dynamic Convolution Kernels to increase the model coupling, so that the model can better understand the dependencies between different positions in the input sequence. The introduction of this module aims to enhance the model's ability to model long-distance dependence in sequences, thereby improving its performance. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the Dynamic Transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the Dynamic Transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience.

Hegemon or South-South partner? The ambiguity of Chinese foreign direct investment in Peru

PurposeThe purpose of this paper is to propose a framework for evaluating the relationship between China and Peru, drawing on dependency theory, against the backdrop of China’s explicit policies towards foreign direct investment. It seeks to transcend traditional interpretations of this relationship in the literature that focuses on China as either hegemon or a South–South partner to Latin American countries to highlight a more nuanced relationship.Design/methodology/approachThe paper adopts a case study approach, focusing on China in Peru. The authors examine three areas of traditional, strategic and emerging industries drawing from Chinese national policies, reviewing these against characteristics of dependency: control of production, heterogeneity of actors, transfer of knowledge and delinking.FindingsThe authors find that Chinese foreign direct investment (FDI) in Peru demonstrates mixed motives and collectively operates as an ambiguous player. Chinese firms appear to be willing to work with various actors, but this engagement does not translate into a decolonial development alternative in the absence of a Peruvian political will to delink and Chinese willingness to actively transfer control of production and knowledge.Originality/valueThis paper contributes to existing literature on China in Latin America by evaluating Chinese outward FDI in Peru against China’s strategic aims in terms of a re-evaluation of dependency theory.

Archimedes optimisation algorithm quantum dilated convolutional neural network for road extraction in remote sensing images

Roads are closely intertwined with human existence, and the process of extracting road networks has emerged as the most prominent task in remote sensing (RS). The automated road interpretation process of remote sensing images (RSI) efficiently acquires road network data at a reduced expense in comparison to the traditional visual interpretation of RSI. However the manifestation of RSI is completely distinct because of the great difference in length, width, material, and shape of road networks in dissimilar areas. Thus, the extraction of road network data in RSI is still a complex issue. In recent times, DL-based approaches have projected a famous development in image segmentation outcomes, but a lot of them still could not retain boundary data and attain high-resolution road segmentation maps while processing the RSI. Traditional convolutional neural networks (CNNs) demonstrate impressive performance in road extract tasks; however, they frequently encounter difficulties in capturing intricate details and contextual information. The study introduces a novel method, named Archimedes Optimisation Algorithm, Quantum Dilated Convolutional Neural Network for Road Extraction (AOA-QDCNNRE), to tackle the challenges encountered in remote sensing images. The AOA-QDCNNRE technique aims to generate a high-resolution road segmentation map using DL with a hyperparameter tuning process. The AOA-QDCNNRE technique primarily relies on the QDCNN model, which integrates quantum technology (QC) with dilated convolutions to augment the network's capacity to capture local as well as global contextual information. In addition, the incorporation of the dilated convolutional technique effectively enhances the receptive field without sacrificing spatial resolution, enabling the extraction of precise road features. To develop the road extraction outcomes of the QDCNN approach, the AOA-based hyperparameter tuning process can be exploited. The AOA-QDCNNRE system's simulation results can be tested on benchmark databases, and the results indicate that the AOA-QDCNNRE method surpasses recent algorithms.

Rethinking Islamic Feminist Thought on Reinterpreting the Qur'an: An Analysis of the Thoughts of Aminah Wadud, Fatima Mernissi, Asma Barlas, and Riffat Hassan

This article aims to revisit Islamic feminist thought on the reinterpretation of the Qur'an by analyzing the views of four major figures in the movement: Aminah Wadud, Fatima Mernissi, Asma Barlas, and Riffat Hassan. This study uses a critical text analysis approach to understand the arguments and interpretations they put forward in interpreting Qur'anic verses related to gender. Through an in-depth analysis of their work in articles, books, and papers of their views. In addition, the article explores the ways in which these Islamic feminists reconstruct an understanding of sacred texts, highlighting differences in approach and common themes in their interpretations. In addition, it evaluates the impact of their reinterpretations of the Qur'an on traditional views in Muslim societies and their implications in the context of social change and gender equality. When examining Islamic feminist views on the Qur'anic reinterpretation, it is important to thoroughly understand the cultural and linguistic context of Arabic. Misconceptions regarding the interpretations of classical Muslim intellectuals, which are often considered discriminatory against women, are often due to a lack of understanding of the cultural and linguistic context of Arabic. Therefore, it is important to pay attention to linguistic and cultural contexts when interpreting sacred texts. On the other hand, feminists should also note that interpretations of the Qur'an do not always result in interpretations that are in line with their principles of gender equality. The theoretical implication is that, in an effort to understand the verses of the Qur'an related to gender issues, it is important to consider the cultural context, language, and comprehensive interpretation. There is room for dialog and deeper understanding between feminist views and traditional interpretations, by taking an approach that combines an understanding of classical Arabic culture with the values of gender equality championed by feminists.

A neurophysiological basis for aperiodic EEG and the background spectral trend

Electroencephalograms (EEGs) display a mixture of rhythmic and broadband fluctuations, the latter manifesting as an apparent 1/f spectral trend. While network oscillations are known to generate rhythmic EEG, the neural basis of broadband EEG remains unexplained. Here, we use biophysical modelling to show that aperiodic neural activity can generate detectable scalp potentials and shape broadband EEG features, but that these aperiodic signals do not significantly perturb brain rhythm quantification. Further model analysis demonstrated that rhythmic EEG signals are profoundly corrupted by shifts in synapse properties. To examine this scenario, we recorded EEGs of human subjects being administered propofol, a general anesthetic and GABA receptor agonist. Drug administration caused broadband EEG changes that quantitatively matched propofol’s known effects on GABA receptors. We used our model to correct for these confounding broadband changes, which revealed that delta power, uniquely, increased within seconds of individuals losing consciousness. Altogether, this work details how EEG signals are shaped by neurophysiological factors other than brain rhythms and elucidates how these signals can undermine traditional EEG interpretation.

Pre-Service Teachers’ Perceptions on the Use of Heritage in Secondary Education and Their Reception of Educational Materials from the Heritage and Museum Sector: A Case Study in Flanders (Belgium)

There has been a growing policy interest in establishing connections between heritage and education. Nevertheless, there seems to be very little evaluation or critical reflection on what actually happens in practice, and it remains unclear how heritage education is employed in countries throughout Europe. The aim of this paper is to assess the current status of heritage education in Flanders (the Dutch-speaking northern part of Belgium) via a literature review and an exploratory case study. The perceptions and opinions of pre-service teachers (n = 17) were investigated using three instruments: a questionnaire, document analysis, and the think aloud method. The results show that the pre-service teachers had a traditional interpretation of heritage, mainly relying on well-known and monumental examples and following a rather historical–artistic conception. However, it was found that their opinions were positive towards the use of heritage as an educational resource, and their evaluations of educational materials from the heritage and museum sector provided detailed information concerning teachers’ desires and needs in this regard. The implications of this study should encourage initial teacher training in Flanders to further consider the epistemological and methodological challenges in the emerging field of heritage education.

The meaning of time: A digital, complex variable

This article explores the intricate concept of time, delving into its digital and complex nature. It presents a comprehensive theoretical analysis, revealing how time's multifaceted characteristics are essential for understanding various cosmic phenomena. The study extends to the implications of these properties in the realms of Cosmic Inflation and String Theory, particularly focusing on the accelerated expansion of space. This work aims to offer a deeper perspective on the complex nature of time, enriching the existing theories in Relativity and Cosmology, and providing a novel framework for viewing time beyond traditional interpretations.

Adalbero of Laon's Poem to King Robert (1023–1025/7): a discourse against Cluniac reform or a commentary on monastic hypocrisy?

The purpose of this paper is to nuance the traditional interpretation of Bishop Adalbero of Laon's satirical Carmen ad Rotbertum regem as a rebuttal of Cluniac reform and its disruptive effect on early eleventh‐century society. Study of the text's literary antecedents reveals that its criticism was rooted in a tradition of commentaries on the conduct and attitudes of a much larger monastic cohort. Furthermore, comparison of its argument with evidence about the context and with a number of polemical statements regarding the relations between bishops and monastic leaders since the 990s indicates that the author's focus was on cautioning against abbots’ hypocrisy rather than against a programmatic reform.

Deep attributes: innovative LSTM-based seismic attributes

SUMMARY Seismic attributes are derived measures from seismic data that help characterize subsurface geological features and enhance the interpretation of subsurface structures: we propose to exploit the hidden layers of Long–Short Time Memory neural network predictions as possible new reflection seismic attributes. The idea is based on the inference process of a neural network, which in its hidden layers stores information related to different features embedded in the input data and which usually are not considered. Neural network applications typically ignore such intermediate steps because the main interest lies in the final output, which is considered as the exclusive exploitable feature of the process. On the contrary, here we analyse the possibility to exploit the intermediate prediction steps, hereafter referred as ‘deep attributes’ because they are produced by a deep learning algorithm, to highlight features and emphasize characteristics embedded in the data but neither recognizable by traditional interpretation, nor evident with classical attributes or multi-attribute approaches. Nowadays, classical signal attributes are numerous and used for different purposes; we here propose an original strategy to calculate attributes previously never exploited, which are potentially complementary or a good alternative to the classical ones. We tested the proposed procedure on synthetic and field 2-D and 3-D reflection seismic data sets to test and demonstrate the stability, affordability and versatility of the entire approach. Furthermore, we evaluated the performance of deep attributes on a 4-D seismic data set to assess the applicability and effectiveness for time-monitoring purposes and comparing them with the sweetness attribute.

Design and Practice of Blended Teaching Mode of "MOOCs + Flipped Classroom"-- Taking "Basics of Interpretation" Course As An Example

Through investigation and observation, it is found that most English interpreting courses in China have single teaching methods, insufficient teaching means and classroom feedback. Meanwhile, interpretation teaching is greatly limited by teaching time and space. Reliance on the traditional paper textbook and teacher-centered teaching mode leads to a large number of students' lack of interest and participation, which affects students' professional development over time. Starting from the concept of flipped classroom and combining with the online MOOCs course "Basics of Interpretation" , this study aims to explore the role of blended teaching model in improving traditional interpreting teaching. The practice has proved that the blended teaching mode of "MOOCs + Flipped Classroom" has played a positive role in reforming and enriching traditional interpretation classes, improving teaching mode and content, expanding course duration, and providing effective feedback.

Porosity prediction through well logging data: A combined approach of convolutional neural network and transformer model (CNN-transformer)

Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, this study introduces a CNN (convolutional neural network)-transformer model, which aims to improve the accuracy and generalization ability of logging porosity prediction. CNNs have excellent spatial feature capture capabilities. The convolution operation of CNNs can effectively learn the mapping relationship of local features, so as to better capture the local correlation in the well log. Transformer models are able to effectively capture complex sequence relationships between different depths or time points. This enables the model to better integrate information from different depths or times, and improve the porosity prediction accuracy. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the CNN-transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the CNN-transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience.

Jerusalem’s Destruction and the Coming of the Son of Man: Assessing an Alleged Lukan Association

This study of a current current in biblical research surveys and appraises an interpretative trend that associates future-oriented Son of Man sayings in Luke’s Gospel with the destruction of Jerusalem in 70 ce. After contextualizing this interpretative tradition, the views of several recent advocates of this interpretative trend are surveyed and compared. Subsequently, a means of appraising this interpretative stance is proposed, which involves discussion of two sets of Lukan passages: first, four pericopes in which the destruction of Jerusalem is clearly referenced or implied (Lk. 13:31–35; 19:41–44; 21:20–24; and 23:26–31); and second, a series of passages containing future-oriented Son of Man sayings, with special reference to the key Son of Man saying in Lk. 21:27. The upshot of this survey and appraisal is that no future-oriented Son of Man saying in Luke’s Gospel plausibly relates to Jerusalem’s destruction.

Inner-witnessing as a spatial everyday practice in Ann Petry’s The Street

Some scholars approach Ann Petry’s portrayal of Lutie Johnson’s fate in the hostile background of The Street (1946) through a naturalistic and deterministic lens: the protagonist, a single black mother, dreams of improving her lifestyle by reproducing a white uppermiddle class model. Given Lutie’s gender and race her dreams are, following this critical method, condemned as impossible. This approach to Petry’s novel does not take into account the deep geographical analysis that the author provides of Lutie’s struggle. Employing a spatial paradigm, this essay aims to offer an interpretation of Petry’s novel that challenges traditional geographic patterns evident in the street’s urban description. Such opposition reflects Katherine McKittrick’s approach to the definition of ‘Black women’s geographies’ as sites of struggle. Analyzing the novel’s employment of everyday practices, such as communicative expressions, and Kelly Oliver’s notion of inner-witnessing as one of them, proves how Lutie Johnson’s subjective mapping constitutes an everyday practice within the novel. As such, Petry’s portrayal of Lutie’s creation of space through these everyday practices illustrates how in her interactions with the street’s dynamics Lutie provides a witnessing cartography of struggle that opposes traditional geographic interpretations.

Rethinking the balance-of-payments-constrained approach, in the light of the recent commodity boom

We develop a balance-of-payments-constrained growth (BPCG) model to explain the specific conditions of small commodity-dependent countries. Based on Thirlwall’s model and subsequent contributions, we elaborate a new model that assumes exogenous export prices, infinite price elasticity of export demand, and supply of commodities that react to prices with positive, but not infinite, elasticity. We contribute to explaining the central role of export prices and the relevance of price competitiveness in the growth dynamics. It is shown that a real devaluation and an improvement in terms of trade (TT) in a context of increasing international prices increase the equilibrium growth rate. However, export prices are more relevant than import prices in determining the growth of these countries, challenging the traditional interpretation of TT. Moreover, our model enriches the extensive literature which has corroborated that a development strategy based exclusively on increasing production capacity in commodity sectors is doomed to fail.

Voices from Transit Zones: A Study of Humanitarian Interpreting Challenges

This paper presents findings derived from interviews encompassing representatives of NGOs and public service institutions as well as trained and untrained interpreters within the ReTrans partner countries Austria, Slovenia, the Republic of North Macedonia and Greece. The study’s aim was to gain comprehensive insights into the specific requirements of humanitarian interpreters and service users. Additionally, it sought to illuminate the dilemmas they routinely face. The findings have shown that humanitarian interpreting frequently involves the participation of untrained interpreters, with the essential role played by cultural knowledge. Additionally, the use of a pivot language, thematic preparation, and engagement in additional tasks beyond traditional interpretation roles, such as gathering information about financial support or assisting in finding accommodation, emerge as crucial aspects of humanitarian interpreting. However, due to the scope of this paper, we will focus on specific elements within this broader context

Comparative study of time-frequency transformation methods for ECG signal classification

In this study, we highlighted the growing need for automated electrocardiogram (ECG) signal classification using deep learning to overcome the limitations of traditional ECG interpretation algorithms that can lead to misdiagnosis and inefficiency. Convolutional neural networks (CNN) application to ECG signals is gaining significant attention owing to their exceptional image-classification capabilities. However, we addressed the lack of standardized methods for converting 1D ECG signals into 2D-CNN-compatible input images by using time-frequency methods and selecting hyperparameters associated with these methods, particularly the choice of function. Furthermore, we investigated the effects of fine-tuned training, a technique where pre-trained weights are adapted to a specific dataset, on 2D-CNNs for ECG classification. We conducted the experiments using the MIT-BIH Arrhythmia Database, focusing on classifying premature ventricular contractions (PVCs) and abnormal heartbeats originating from ventricles. We employed several CNN architectures pre-trained on ImageNet and fine-tuned using the proposed ECG datasets. We found that using the Ricker Wavelet function outperformed other feature extraction methods with an accuracy of 96.17%. We provided crucial insights into CNNs for ECG classification, underscoring the significance of fine-tuning and hyperparameter selection in image transformation methods. The findings provide valuable guidance for researchers and practitioners, improving the accuracy and efficiency of ECG analysis using 2D-CNNs. Future research avenues may include advanced visualization techniques and extending CNNs to multiclass classification, expanding their utility in medical diagnosis.

Navigating the landscape of concept-supported XAI: Challenges, innovations, and future directions

This comprehensive review of concept-supported interpretation methods in Explainable Artificial Intelligence (XAI) navigates the multifaceted landscape. As machine learning models become more complex, there is a greater need for interpretation methods that deconstruct their decision-making processes. Traditional interpretation techniques frequently emphasise lower-level attributes, resulting in a schism between complex algorithms and human cognition. To bridge this gap, our research focuses on concept-supported XAI, a new line of research in XAI that emphasises higher-level attributes or 'concepts' that are more aligned with end-user understanding and needs. We provide a thorough examination of over twenty-five seminal works, highlighting their respective strengths and weaknesses. A comprehensive list of available concept datasets, as opposed to training datasets, is presented, along with a discussion of sufficiency metrics and the importance of robust evaluation methods. In addition, we identify six key factors that influence the efficacy of concept-supported interpretation: network architecture, network settings, training protocols, concept datasets, the presence of confounding attributes, and standardised evaluation methodology. We also investigate the robustness of these concept-supported methods, emphasising their potential to significantly advance the field by addressing issues like misgeneralization, information overload, trustworthiness, effective human-AI communication, and ethical concerns. The paper concludes with an exploration of open challenges such as the development of automatic concept discovery methods, strategies for expert-AI integration, optimising primary and concept model settings, managing confounding attributes, and designing efficient evaluation processes.