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The argumentative talk on radio and TV has become a popular feature of media discourse in Kenya. Question-answer sequences as the talk unfolds through the joint participation of co-participants in the talk have emerged as a means to put argumentative talk into effect. Yet, the nature of questions and their categorization remain little understood. Given the recursive nature of question-answer sequences, this paper investigates question typology that sets apart argumentative talk shows from other types of talk. The data consists of transcripts from two Kenyan TV argumentative talk shows: Checkpoint on KTN and Opinion Court on Citizen TV. A question classification scheme by Schirm [2008] was used to discuss the incidence and usage of questions in argumentative talk shows. Findings revealed that clashing, rhetorical, classic clarifying, and opinion-eliciting questions were the most frequently used types in the data sets. It was also noted that different question types served unique rhetorical purposes leading to the conclusion that argumentative talk shows on TV exhibit recursive interactional resources qualifying it as a genre.

Deep learning approaches are generally complex, requiring extensive computational resources and having high time complexity. Transfer learning is a state-of-the-art approach to reducing the requirements of high computational resources by using pretrained models without compromising accuracy and performance. In conventional studies, pre-trained models are trained on datasets from different but similar domains with many domain-specific features. The computational requirements of transfer learning are directly dependent on the number of features that include the domain-specific and the generic features. This article investigates the prospects of reducing the computational requirements of the transfer learning models by discarding domain-specific features from a pre-trained model. The approach is applied to breast cancer detection using the dataset curated breast imaging subset of the digital database for screening mammography and various performance metrics such as precision, accuracy, recall, F1-score, and computational requirements. It is seen that discarding the domain-specific features to a specific limit provides significant performance improvements as well as minimizes the computational requirements in terms of training time (reduced by approx. 12%), processor utilization (reduced approx. 25%), and memory usage (reduced approx. 22%). The proposed transfer learning strategy increases accuracy (approx. 7%) and offloads computational complexity expeditiously. [ABSTRACT FROM AUTHOR]

Deep learning approaches are generally complex, requiring extensive computational resources and having high time complexity. Transfer learning is a state-of-the-art approach to reducing the requirements of high computational resources by using pre-trained models without compromising accuracy and performance. In conventional studies, pre-trained models are trained on datasets from different but similar domains with many domain-specific features. The computational requirements of transfer learning are directly dependent on the number of features that include the domain-specific and the generic features. This article investigates the prospects of reducing the computational requirements of the transfer learning models by discarding domain-specific features from a pre-trained model. The approach is applied to breast cancer detection using the dataset curated breast imaging subset of the digital database for screening mammography and various performance metrics such as precision, accuracy, recall, F1-score, and computational requirements. It is seen that discarding the domain-specific features to a specific limit provides significant performance improvements as well as minimizes the computational requirements in terms of training time (reduced by approx. 12%), processor utilization (reduced approx. 25%), and memory usage (reduced approx. 22%). The proposed transfer learning strategy increases accuracy (approx. 7%) and offloads computational complexity expeditiously.

Despite the important role of the genre awareness in facilitating the effective communication in the global business context and the need to teach and practice writing for various social purposes, there was scant classroombased research on effects of providing peer feedback on EFL learners' genre awareness in business writing for fulfilling the particular communicative purpose. This study examined whether student reviewers could improve their genre awareness in business letter writing by providing peer feedback to other students' writings at varying levels. Sixty business English majors, taking the business writing course for 1 year under the tutelage of the same instructor, participated in the current study. They were randomly assigned to the experimental group who reviewed the students' drafts at different levels (high, medium and low) and gave written comments and the control group who received no treatment but did self-revision. Both groups followed the genrespecific evaluation criteria during peer feedback and self-revision. Repeatedmeasures ANOVA on the two groups' writing performances at different timepoints (pretest, immediate posttest, transfer posttest and delayed transfer posttest) demonstrated that the participants in the experimental group had better performance than those in the control group at the transfer posttest as well as the delayed transfer posttest. Moreover, providing weakness comments on both the low-quality and the medium-quality drafts had significant effects on their own writing quality at the subsequent tests, whereas providing strength feedback on the high-quality drafts had statistically significant impacts on their own writing quality at the immediate posttest and the delayed transfer posttest. However, multiple linear regression analyses demonstrated that only offering weakness comments to the medium-quality drafts could effectively predict the reviewers' overall writing quality in the immediate posttest, the transfer posttest, and the delayed transfer posttest. Tentative research and pedagogical implications of the findings were discussed. [ABSTRACT FROM AUTHOR]

China-Africa corporate advertising is emerging as an example of the corporate advertising genre in response to the specific rhetorical context of China-Africa win-win cooperation. Drawing on Bhatia's Critical Genre Analysis (2017), this study explores the text-internal and text-external generic features of China-Africa corporate advertising by analyzing 50 collected samples. The analysis of text-internal prominence shows that China-Africa corporate advertising is unique in its frequent use of win-win-oriented and sector-dependent technical lexicons to indicate the promotional intent. It is also found that move structures vary across different sub-types of China-Africa corporate advertising. Text-externally, the findings reveal a mixture of different discourses within the discursive space of China-Africa corporate advertising. In addition, the results suggest that Chinese companies invest more rhetorical efforts in enhancing the China-Africa community than African counterparts who tend to perceive China-Africa cooperation as the main avenue to attract worldwide partnerships. The findings have some practical implications for discourse construction in the Belt and Road Initiative context and shed light on the evolving nature of advertising discourse, particularly in the China-Africa win-win business context. [ABSTRACT FROM AUTHOR]

Deep learning approaches are generally complex, requiring extensive computational resources and having high time complexity. Transfer learning is a state-of-the-art approach to reducing the requirements of high computational resources by using pre-trained models without compromising accuracy and performance. In conventional studies, pre-trained models are trained on datasets from different but similar domains with many domain-specific features. The computational requirements of transfer learning are directly dependent on the number of features that include the domain-specific and the generic features. This article investigates the prospects of reducing the computational requirements of the transfer learning models by discarding domain-specific features from a pre-trained model. The approach is applied to breast cancer detection using the dataset curated breast imaging subset of the digital database for screening mammography and various performance metrics such as precision, accuracy, recall, F1-score, and computational requirements. It is seen that discarding the domain-specific features to a specific limit provides significant performance improvements as well as minimizes the computational requirements in terms of training time (reduced by approx. 12%), processor utilization (reduced approx. 25%), and memory usage (reduced approx. 22%). The proposed transfer learning strategy increases accuracy (approx. 7%) and offloads computational complexity expeditiously., Competing Interests: The authors declare there are no competing interests., (©2024 Safdar Ali Khan et al.)

This study aims at investigating the Sudanese criminal and civil judicial precedents so as to gain a better understanding of the communicative purposes of each subgenre and the variation in demands across the two subgenres using genre analysis. It adopted the mixed approach, using Maxqda application, to investigate the generic features, the moves, the sub-moves; as well as how problems are solved and decisions arrived at. The investigation shows that the law practitioners should know the intrinsic nature of these judicial precedents by examining their generic features, concepts, functions and approaches. Also, the analysis of the typical generic moves and sub-moves clearly reveals that the judicial precedents across the two subgenres have some overlapping generic structures at all levels. Most of the judicial precedents in the corpus seem to have the typical generic move structures. The analysis of the specific generic move structures reveals slight generic differences of judicial precedents. Finally, there are still evidences of slight variation across the two subgenres at sub-move level. [ABSTRACT FROM AUTHOR]

In light of the understanding that the analysis of the generic features of different academic genres can enhance the ability of non-native members of academic discourse communities to understand, and where appropriate, to produce them, the present study aimed at investigating the dominant generic structure of research articles in mathematics. To start with a relatively narrow focus, a corpus of thirty mathematics research articles were randomly selected from The Bulletin of the Iranian Mathematical Society(BIMS), and were analyzed in terms of their macro-organizational structure. An eleven-move structure was identified in the analyzed corpus, while it was also found that the research articles in the corpus did not follow the conventional Introduction-Methods-Results-Discussion structure found in many other disciplines. Methods and Discussion sections which are frequently encountered in research articles of many other disciplines were missing in the analyzed articles. It is argued that the findings may have theoretical implications in revealing some discipline specific conventions of organizing macro structure. Since all the authors were Iranian, the emerging model should be treated cautiously because some aspects of the emerging macro structure may also be under the influence of culture specific conventions.

Current Machine learning algorithms are highly dependent on manually designing features and the Performance of such algo- rithms predominantly depend on how good our representations are. Manually we might never be able to produce best and diverse set of features that closely describe all the variations that occur in our data. Understanding this, vision community is moving towards learning the optimum features itself instead of learning from the features. Traditional hand engineered features lack in generalizing well to other domains/Problems, are time consuming, expensive, requires expert knowledge on the problem domain and doesn’t facilitate learning from previous learnings/Representations(Transfer learning). All these issues are resolved in learning deep representations. Since 2006 a wide range of representation learning algorithms has been proposed but by the recent success and breakthroughs of few deep learning models, the representation learning algorithms have gained the spotlight. This paper aims to give short overview of deep learning approaches available for vision tasks. We also discuss their applicability (With respect to their properties) in vision field. [ABSTRACT FROM AUTHOR]

Despite the important role of the genre awareness in facilitating the effective communication in the global business context and the need to teach and practice writing for various social purposes, there was scant classroom-based research on effects of providing peer feedback on EFL learners' genre awareness in business writing for fulfilling the particular communicative purpose. This study examined whether student reviewers could improve their genre awareness in business letter writing by providing peer feedback to other students' writings at varying levels. Sixty business English majors, taking the business writing course for 1 year under the tutelage of the same instructor, participated in the current study. They were randomly assigned to the experimental group who reviewed the students' drafts at different levels (high, medium and low) and gave written comments and the control group who received no treatment but did self-revision. Both groups followed the genre-specific evaluation criteria during peer feedback and self-revision. Repeated-measures ANOVA on the two groups' writing performances at different timepoints (pretest, immediate posttest, transfer posttest and delayed transfer posttest) demonstrated that the participants in the experimental group had better performance than those in the control group at the transfer posttest as well as the delayed transfer posttest. Moreover, providing weakness comments on both the low-quality and the medium-quality drafts had significant effects on their own writing quality at the subsequent tests, whereas providing strength feedback on the high-quality drafts had statistically significant impacts on their own writing quality at the immediate posttest and the delayed transfer posttest. However, multiple linear regression analyses demonstrated that only offering weakness comments to the medium-quality drafts could effectively predict the reviewers' overall writing quality in the immediate posttest, the transfer posttest, and the delayed transfer posttest. Tentative research and pedagogical implications of the findings were discussed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wu, Zhu and Yang.)

An analysis of visceral skull bones morphology of six recent genera ( Clupeonella, Sprattus, Clupea, Sardina, Sardinops, and Sardinella) and three fossil species ( Illusionella tsurevica, I. pshekhensis, and Clupea doljeana) of Clupeinae was carried out. Description of bones of maxillary and opercular regions was done for examined taxa subject to generic and infra- and interspecific variability. Due to ascertained features of viscerocranium morphology of Illusionella species, we can distinguish them from all of the recent European clupeins. Also there is a number of generic features in this article which give us a possibility to distinguish either recent or fossil forms, even in case of separate paleoichhthyological material. [ABSTRACT FROM AUTHOR]

The identification of differences between groups is often important in biomechanics. This paper presents group classification tasks using kinetic and kinematic data from a prospective running injury study. Groups composed of gender, of shod/barefoot running and of runners who developed patellofemoral pain syndrome (PFPS) during the study, and asymptotic runners were classified. The features computed from the biomechanical data were deliberately chosen to be generic. Therefore, they were suited for different biomechanical measurements and classification tasks without adaptation to the input signals. Feature ranking was applied to reveal the relevance of each feature to the classification task. Data from 80 runners were analysed for gender and shod/barefoot classification, while 12 runners were investigated in the injury classification task. Gender groups could be differentiated with 84.7%, shod/barefoot running with 98.3%, and PFPS with 100% classification rate. For the latter group, one single variable could be identified that alone allowed discrimination. [ABSTRACT FROM AUTHOR]