Reviewerly/exHarmony huggingface dataset:
Enhancing cervical cancer diagnosis with graph convolution network: AI-powered segmentation, feature analysis, and classification for early detection

Authors:
raw_author_name: Nur Mohammad Fahad
raw_affiliation_string: Department of CSE, United International University, Dhaka, 1212, Bangladesh
raw_author_name: Sami Azam
raw_affiliation_string: Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
raw_author_name: Sidratul Montaha
raw_affiliation_string: Department of Computer Science, University of Calgary, Calgary, AB, Canada
raw_author_name: Md. Saddam Hossain Mukta
raw_affiliation_string: Department of CSE, United International University, Dhaka, 1212, Bangladesh

Abstract: Abstract Cervical cancer is a prevalent disease affecting the cervix cells in women and is one of the leading causes of mortality for women globally. The Pap smear test determines the risk of cervical cancer by detecting abnormal cervix cells. Early detection and diagnosis of this cancer can effectively increase the patient’s survival rate. The advent of artificial intelligence facilitates the development of automated computer-assisted cervical cancer diagnostic systems, which are widely used to enhance cancer screening. This study emphasizes the segmentation and classification of various cervical cancer cell types. An intuitive but effective segmentation technique is used to segment the nucleus and cytoplasm from histopathological cell images. Additionally, handcrafted features include different properties of the cells generated from the distinct cervical cytoplasm and nucleus area. Two feature rankings techniques are conducted to evaluate this study’s significant feature set. Feature analysis identifies the critical pathological properties of cervical cells and then divides them into 30, 40, and 50 sets of diagnostic features. Furthermore, a graph dataset is constructed using the strongest correlated features, prioritizes the relationship between the features, and a robust graph convolution network (GCN) is introduced to efficiently predict the cervical cell types. The proposed model obtains a sublime accuracy of 99.11% for the 40-feature set of the SipakMed dataset. This study outperforms the existing study, performing both segmentation and classification simultaneously, conducting an in-depth feature analysis, attaining maximum accuracy efficiently, and ensuring the interpretability of the proposed model. To validate the model’s outcome, we tested it on the Herlev dataset and highlighted its robustness by attaining an accuracy of 98.18%. The results of this proposed methodology demonstrate the dependability of this study effectively, detecting cervical cancer in its early stages and upholding the significance of the lives of women.
Motion-aware object tracking for aerial images with deep features and discriminative correlation filter

Authors:
raw_author_name: İbrahim Delibaşoğlu
raw_affiliation_string: Department of Software Engineering, Faculty of Computer and Information Sciences, Sakarya University, Esentepe, Sakarya, 54050, Turkey

Abstract: Abstract Object tracking is a challenging task which is required for different problems such as surveillance, traffic analysis and human-computer interaction. The problem of tracking an object can be considered in different categories such as single object tracking, multiple object tracking, short-term tracking, long-term tracking, tracking by detection and detection-free tracking. This study focuses on detection-free tracking for ground targets on aerial images. The studies in the literature show that correlation filter and deep learning based object trackers perform well recently. This paper proposes a new correlation filter-based tracker containing a strategy for re-detection issue. We improve the performance of correlation filter-based tracker by adding a lightweight re-detection ability to the correlation filter tracker in case of a long occlusion or complete loss of target. We use deep features to train Discriminative Correlation Filter(DCF) by integrating sub-networks from pre-trained ResNet and SAND models. The experimental results on the popular UAV123L dataset show that the proposed method(MADCF) improves the performance of DCF tracker and have a reasonable performance for long-term tracking problem. Moreover, we prepare a new tracking dataset (PESMOD tracking) consisting of UAV images, and we evaluate the proposed method and state-of-the-art method in this dataset. We observed that the proposed method performs much better in ground target tracking from VIVID and PESMOD aerial datasets. The proposed MADCF tracker performs better for small targets tracked by UAVs compared to the deep learning-based trackers. The source code and prepared dataset are available at http://github.com/mribrahim/MADCF
Estimation and interpretation problems and solutions when using proportion covariates in linear regression models

Authors:
raw_author_name: Denis Valle
raw_affiliation_string: School of Forest, Fisheries, and Geomatics Sciences University of Florida Gainesville Florida USA

Abstract: Abstract Proportion variables, also known as compositional data, are very common in ecology. Unfortunately, few scientists are aware of how compositional data, when used as covariates, can adversely impact statistical analysis. We describe here how proportion covariates result in multicollinearity and parameter identifiability problems. Using simulated data on bird species richness as a function of land use, we show how these problems manifest when fitting a wide range of models in R, both in a frequentist and Bayesian framework. In particular, we show that similar models can often generate substantially different parameter estimates, leading to very different conclusions. Dropping a covariate or the intercept from the model can solve the multicollinearity and parameter identifiability problems. Unfortunately, these solutions do not fix the inherent challenges associated with interpreting parameter estimates. To this end, we propose focusing the interpretation on the difference of slope parameters to avoid the inherent unidentifiability of individual parameters. We also propose conditional plots with two x ‐axes and marginal plots as visualization techniques that can help users better interpret their modeling results. We illustrate these problems and proposed solutions using empirical data from the North American Breeding Bird Survey. The practical and straightforward approaches suggested in this article will help the fitting of linear models and interpretation of its results when some of the covariates are proportions.
Resonant entanglement of photon beams by a magnetic field

Authors:
raw_author_name: Alexander I. Breev
raw_affiliation_string: Department of Physics, Tomsk State University, Lenin Ave. 36, Tomsk, 634050, Russia
raw_author_name: Dmitry M. Gitman
raw_affiliation_string: Department of Physics, Tomsk State University, Lenin Ave. 36, Tomsk, 634050, Russia

Abstract: Abstract In spite of the fact that photons do not interact with an external magnetic field unless nonlinearity of QED is taken into account, the latter field may indirectly affect photons in the presence of a charged environment. This opens up an interesting possibility to continuously control the entanglement of photon beams without using any crystalline devices. We study this possibility in the framework of an adequate QED model. In an approximation it was discovered that such entanglement has a resonant nature, namely, a peak behavior at certain magnetic field strengths, depending on characteristics of photon beams direction of the magnetic field and parameters of the charged medium. Numerical calculations illustrating the above-mentioned resonant behavior of the entanglement measure and some concluding remarks are presented.
On the Role of Informal vs. Formal Context of Language Experience in Italian–German Primary School Children

Authors:
raw_author_name: Maria Francesca Ferin
raw_affiliation_string: Department of Linguistics, University of Konstanz, 78464 Konstanz, Germany
raw_author_name: Tanja Kupisch
raw_affiliation_string: Department of Linguistics, University of Konstanz, 78464 Konstanz, Germany

Abstract: This study focuses on the contexts of language experience in relation to language dominance in eighty-seven Italian–German primary school children in Germany using the MAIN narrative task. We compare current language experience in the heritage language (Italian) and the majority language (German) in both formal and informal settings, and we examine the respective impact on micro- and macrostructure measures, including different language domains. Some previous findings emphasized the importance of language experience in formal contexts. By contrast, our results suggest that, in particular, language experience in informal contexts determines vocabulary and fluency in the heritage and majority language, while there are no effects of exposure on syntactic complexity. Furthermore, while the younger children are relatively balanced, the older children are more dominant in the societal language. Our findings imply that the use of the minority language in informal contexts should be encouraged to promote its development and maintenance.
Structural and semantic features of adjectives across languages and registers

Authors:
raw_author_name: Signe Oksefjell Ebeling
raw_affiliation_string: University of OsloNorway

Abstract: Abstract This study focuses on the seemingly simple grammatical category of adjectives. However, the framework adopted for analysing adjectives from both a cross-linguistic and a cross-register perspective highlights features of grammatical complexity within phrases as well as contrastive complexity within and across languages and registers. Using English-Norwegian comparable data from the fictional sub-registers of narrative and dialogue, the analysis uncovers both similarities and differences in the structural and semantic features of adjectives. Most notable is perhaps the impact of register at all levels of analysis, including adjectival function (attributive vs. predicative), the degree of phrase complexity and semantic class. The contrastive analysis proper, i.e. comparing adjective use across languages, reveals that English and Norwegian have more in common within each sub-register than across them. For example, the lexico-grammatical behaviour of adjectives in English narrative has more in common with Norwegian narrative than it has with English dialogue. To the extent that there are clear general differences between English and Norwegian, these are mainly found in the proportions and preferences of a few specific features.
Reflections about Blockchain in Health Data Sharing: Navigating a Disruptive Technology

Authors:
raw_author_name: Ana Corte-Real
raw_affiliation_string: Clinical and Academic Centre of Coimbra, 3004-531 Coimbra, Portugal; Faculty of Medicine, University of Coimbra, 3000-370 Coimbra, Portugal
raw_author_name: Tiago Nunes
raw_affiliation_string: Clinical and Academic Centre of Coimbra, 3004-531 Coimbra, Portugal; Faculty of Medicine, University of Coimbra, 3000-370 Coimbra, Portugal
raw_author_name: Paulo Rupino da Cunha
raw_affiliation_string: Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, 3030-790 Coimbra, Portugal

Abstract: A comprehensive analysis was performed, considering blockchain technology (BT) properties in digital health, addressing medicolegal, privacy, and regulatory considerations. Adherence to personal data protection and healthcare regulatory guidelines were analyzed and compared for GDPR (Europe), HIPAA (United States), CCPA (California), PIPEDA (Canada), the Privacy Act of 1988 (Australia), APPI (Japan), and LGPD (Brazil). Issues such as health systems, strengthening and aligning policy orientations and initiatives, and emphasizing the role of data analysis in shaping health policies were explored. The study addressed conflicts between the legal frameworks and blockchain, comparing and suggesting solutions like the revision of laws and the integration of compliance mechanisms. Additionally, it sought to enhance IT-health literacy by integrating the healthcare and legal domains. Ongoing collaboration between legal, health, and IT experts is essential for designing systems that effectively balance privacy rights and data protection while maximizing the benefits of disruptive technologies like blockchain.
Unlocking Language Competence: Challenges and Strategies of English Language Educators in the Post-Pandemic Landscape

Authors:
raw_author_name: Edgar R. Eslit, PhD

Abstract: This descriptive-qualitative study explores the challenges and strategies of English language educators, specifically Senior High School (SHS) teachers, in Iligan City in adapting to the post-pandemic educational landscape. This research employs a phenomenological approach, informed by the works of Merleau-Ponty and Van Manen, to delve into the lived experiences of educators and their efforts to enhance language competence in the context of English language instruction. Data were gathered through in-depth literature reviews, informal interviews, observations, and thematic analysis offering valuable insights into the challenges faced and methods employed to overcome them. The study reveals that educators in the Schools Division of Iligan City have shown resilience and adaptability by integrating technology, adopting flexible instructional approaches, and embracing professional development. These strategies have not only enhanced their language competence but also improved their interactions with students. Moreover, the research underscores the pivotal role of language competence in English language instruction and emphasizes the need for educators to embrace new pedagogical approaches. The findings contribute to the field of English language education, providing a nuanced understanding of post-pandemic teaching methods and highlighting the significance of language competence in education. The study calls for a continued focus on professional language development and adaptability to promote linguistic proficiency, cultural awareness, and effective communication in the post-pandemic era, underlining the dedication and resilience of educators in the field of English language instruction.
Determining dense velocity fields for fluid images based on affine motion

Authors:
raw_author_name: Zili Zhang
raw_affiliation_string: Department of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China
raw_author_name: Yan Li
raw_affiliation_string: Shijiazhuang Campus, Army Engineering University, Shijiazhuang, China
raw_author_name: Kaiquan Xiang
raw_affiliation_string: Shijiazhuang Campus, Army Engineering University, Shijiazhuang, China
raw_author_name: Jinghong Wang
raw_affiliation_string: Department of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China

Abstract: In this article, we address the problem of estimating fluid flows between two adjacent images containing fluid and non-fluid objects. Typically, traditional optical flow estimation methods lack accuracy, because of the highly deformable nature of fluid, the lack of definitive features, and the motion differences between fluid and non-fluid objects. Our approach captures fluid motions using an affine motion model for each small patch of an image. To obtain robust patch matches, we propose a best-buddies similarity-based method to address the lack of definitive features but many similar features in fluid phenomena. A dense set of affine motion models was then obtained by performing nearest-neighbor interpolation. Finally, dense fluid flow was recovered by applying the affine transformation to each patch and was improved by minimizing a variational energy function. Our method was validated using different types of fluid images. Experimental results show that the proposed method achieves the best performance.
Integrated geochemical and magnetic potentially toxic elements assessment: a statistical solution discriminating anthropogenic and lithogenic magnetic signals in a complex area of the southeast Nile Delta

Authors:
raw_author_name: Mohamed Abdelwahed Mohamed
raw_affiliation_string: Geology Department, Faculty of Sciences, Cairo University, Giza, Egypt

Abstract: Magnetic proxy approaches proved to be efficient for potentially toxic elements (PTEs) pollution assessment when targeting forests or areas with a homogenous background where anthropogenic magnetic signals could be easily distinguished. Here, we present a multidisciplinary approach for magnetic susceptibility ([Formula: see text]) and HM assessment in a complex area in the Nile Delta, where geogenic input, land use, and various industries with different fly ash and surface water emissions interfere. Statistical analysis discriminates between the effects of lithologic elements and the concentrations of toxic anthropogenic elements. The studied elements are classified into lithogenic and anthropogenic-related (HMs, Au industry, and fertilizers industry) groups with maximum contamination levels of eight anthropogenic-related and highly toxic PTEs (Cu, Zn, Mo, Cd, Sb, Pb, Hg, and As) in the Akrasha industrial area (pollution load index = 15.84). Considering the whole data set, the numerical correlation of [Formula: see text] with most PTE concentrations and the pollution load index (PLI) is weak, while it is moderate to strong with lithogenic elements. However, a comparison of lithogenic elements and PTE concentrations along with x-values in two separate clusters supports the correspondence of lithology with elevated x-values in silt and clay-rich soil samples as well as HM concentration in industrial sandy soils. Correspondence between magnetic maps and chemistry data with land use reflects the potential of magnetic proxy methods for qualitative PTE pollution pre-delineation of the polluted spots, provided that lithological conditions are carefully considered.
Cooperative task allocation method for multi‐unmanned aerial vehicles based on the modified genetic algorithm

Authors:
raw_author_name: Chao Zhou
raw_affiliation_string: Aviation Engineering Institute Civil Aviation Flight University of China Guanghan Sichuan P. R. China
raw_author_name: Feng Qian
raw_affiliation_string: Aviation Engineering Institute Civil Aviation Flight University of China Guanghan Sichuan P. R. China

Abstract: Abstract Unmanned aerial vehicles (UAVs) play a crucial role in various domains such as military, civil, industrial, and so on. However, the coordination and task allocation of multiple UAVs in engineering practices face numerous challenges. As the scale of battlefields expands, and the diversity of UAV missions and constraints increases, the existing task allocation methods suffer from issues such as a mismatch between theoretical models and real‐world applications, low task execution efficiency, and poor responsiveness in dynamic environments. To address these challenges, this paper proposes an improved genetic algorithm (GA)‐based approach for multi‐UAV cooperative task allocation. By collecting battlefield information, decomposing tasks, and considering UAV resource types, an optimization model for multi‐UAV cooperative task allocation is constructed. The proposed method, using an improved GA, generates a set of Pareto‐optimal task solutions for decision‐makers. Case studies demonstrate that this approach effectively enhances task execution efficiency and reduces the total flight distance cost of UAVs.
Cross-linguistic Dependency Length Minimization in scientific language

Authors:
raw_author_name: Marie-Pauline Krielke
raw_affiliation_string: Saarland UniversityGermany

Abstract: Abstract We use Universal Dependencies (UD) for the study of cross-linguistic diachronic syntactic complexity reduction. Specifically, we look at whether and how scientific English and German minimize the length of syntactic dependency relations in the Late Modern period (ca. 1650–1900). Our linguistic analysis follows the assumption that over time, scientific discourse cross-linguistically develops towards an increasingly efficient syntactic code by minimizing Dependency Length (DL) as a factor of syntactic complexity. For each language, we analyse a large UD-annotated scientific and general language corpus for comparison. While on a macro level, our analysis suggests that there is an overall diachronic cross-linguistic and cross-register reduction in Average Dependency Length (ADL), on the micro level we find that only scientific language shows a sentence length independent reduction of ADL, while general language shows an overall decrease of ADL due to sentence length reduction. We further analyse the syntactic constructions responsible for this reduction in both languages, showing that both scientific English and German increasingly make use of short, intra-phrasal dependency relations while long dependency relations such as clausal embeddings become rather disfavoured over time.
Relational sensitivity in participatory design

Authors:
raw_author_name: Laura Gottlieb
raw_affiliation_string: Department of Innovation, Design and Engineering, Mälardalen University, Eskilstuna, Sweden

Abstract: Relational sensitivity involves attuning to subtle and complex collaborative dynamics and plays an important role in participatory design (PD) processes. PD practitioners, including researchers, designers, and facilitators, need abilities to navigate group dynamics while responsibly considering their own impact on the process. This article focuses on the term ‘relational sensitivity’ to develop perspectives and a vocabulary for discussing the competence and readiness needed in PD. This knowledge can contribute to design research by addressing the lack of understanding regarding how practitioners are trained in design facilitation and sensitivity to relationships. The article articulates relational sensitivity through the perspectives: sensitivity to self, to intersubjectivity, and to materiality and process. It makes a valuable contribution to the PD field by bringing together and exemplifying concepts and aspects connected to relational sensitivity, as well as applying Wilber’s four-quadrant model and Ofman’s adapted model in a new research context.
Consumer attitude toward using artificial intelligence (AI) devices in hospitality services

Authors:
raw_author_name: Israt Jahan

Abstract: Purpose The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions. Design/methodology/approach This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses. Findings The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices. Practical implications This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article. Originality/value This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.