Reviewerly/exHarmony huggingface dataset:
Case study of virtual reality sepsis management- instructional design and ITEM outcomes

This case study focuses on the instructional design and outcomes of a virtual reality (VR) application for sepsis management in healthcare education. The instructional design of the VR sepsis application follows five principles adapted from Merrill’s instructional design theory and Bloom’s taxonomy. The VR simulation is structured to provide a coherent and realistic experience, with instructional materials and feedback incorporated to guide and support the learners. A pilot study was conducted with medical students on clinical placement. Participants experienced the VR sepsis simulation and completed a questionnaire using the Immersive Technology Evaluation Measure (ITEM) to assess their immersion, intrinsic motivation, cognitive load, system usability, and debrief feedback. Descriptive analysis of the data showed median scores indicating high immersion and presence, intrinsic motivation, and perceived learning. However, participants reported a moderately high cognitive load. Comparison with a neutral response to ITEM suggested that users had a significantly higher user experience (p < 0.05) in all domains. This case study highlights the potential of VR in healthcare education and its application in sepsis management. The findings suggest that the instructional design principles used in the VR application can effectively engage learners and provide a realistic learning experience. Further research and evaluation are necessary to assess the impact of VR on learning outcomes and its integration into healthcare education settings.
Improving students' cognitive processes to enhance the quality of education

This article discusses the problem of improving the quality of students' knowledge by improving their cognitive processes in the educational environment. The purpose and content of the modern educational paradigm are to focus on the free and comprehensive development of future generations, self-education, competitiveness, successful life as a business person, the education of students, their quality of education, and the education of a responsible, humane citizen. Much research is being carried out in this direction. However, the problems of improving students' cognitive processes and improving their knowledge quality require particular study. There are contradictions between the definition of pedagogical conditions for the development of cognitive processes of students and the need to determine their effective ways. The authors found that the concept of cognitive has different meanings. Improving students' cognitive processes at an early age can significantly improve the quality of knowledge. Various research methods were used, including a theoretical analysis of literature, an express questionnaire, and a question-and-answer questionnaire "designed to improve the cognitive processes of students." The studied questions in this article will be helpful for those who are engaged in the education and upbringing of students, including parents and teachers, as well as psychologists and sociologists.
The Effect of Financial Services on Youth Involvement in Agribusiness Entrepreneurship in Fako Division, Cameroon

The main objective of this article was to examine the role of financial services on youth involvement in agribusiness entrepreneurship activities in the Fako division of the South West Region of Cameroon. Primary data were obtained with the use of a self-administer questionnaire. Which were administered using a stratified sampling technique. The target population was youths between the age of 18 to 35 years involved in any agribusiness-related activity for commercial purposes. Questionnaires were administered to a sample of 500 youths but only 451 of the questionnaires were returned without any error. We adopted a binary logistic estimation technique to analyze our model. Empirical analysis from a binary logistic estimation using marginal effect coefficients for the overall results revealed that; youth who have personal saving increases their likelihood of involving in agribusiness entrepreneurship activities in Fako at a very high level (main occupation) by 0.0986. Meanwhile, government financial services for youth involved in agribusiness has a positive significant effect on youth involvement in agribusiness with a likelihood of 0.6428. Furthermore, youths who are part of a Tontine increase their likelihood of involving in agribusiness entrepreneurship as a main occupation by 0.0233. Also, youths using bank financial services such as bank loan has a likelihood of reducing youths involvement in agribusiness as a main occupation by 0.0520 while those using microfinance services have a likelihood of 0.0354 of engaging youths in agribusiness as a primary activity. We recommend that the government of Cameroon should develop innovative financial services specifically for youths involved in agribusiness-related activities.
AMFF-Net: An Effective 3D Object Detector Based on Attention and Multi-Scale Feature Fusion

With the advent of autonomous vehicle applications, the importance of LiDAR point cloud 3D object detection cannot be overstated. Recent studies have demonstrated that methods for aggregating features from voxels can accurately and efficiently detect objects in large, complex 3D detection scenes. Nevertheless, most of these methods do not filter background points well and have inferior detection performance for small objects. To ameliorate this issue, this paper proposes an Attention-based and Multiscale Feature Fusion Network (AMFF-Net), which utilizes a Dual-Attention Voxel Feature Extractor (DA-VFE) and a Multi-scale Feature Fusion (MFF) Module to improve the precision and efficiency of 3D object detection. The DA-VFE considers pointwise and channelwise attention and integrates them into the Voxel Feature Extractor (VFE) to enhance key point cloud information in voxels and refine more-representative voxel features. The MFF Module consists of self-calibrated convolutions, a residual structure, and a coordinate attention mechanism, which acts as a 2D Backbone to expand the receptive domain and capture more contextual information, thus better capturing small object locations, enhancing the feature-extraction capability of the network and reducing the computational overhead. We performed evaluations of the proposed model on the nuScenes dataset with a large number of driving scenarios. The experimental results showed that the AMFF-Net achieved 62.8% in the mAP, which significantly boosted the performance of small object detection compared to the baseline network and significantly reduced the computational overhead, while the inference speed remained essentially the same. AMFF-Net also achieved advanced performance on the KITTI dataset.
Design of the self-optimal tuning fuzzy inference system for transformer network-based traveling salesman problem in tidying up objects

This research focuses on optimizing the path for service robots to tidying up objects in real-world environments. The study involves converting the physical field containing objects requiring the robot’s attention into virtual data. Multiple layers of environmental information are derived from this virtual data to analyze and optimize the path for robot-assisted tidying. The Mountain Gazelle Optimizer is employed to enhance the fuzzy inference system’s rule base, resulting in improved performance. Comparative analyses with alternative metaheuristic algorithms confirm the effectiveness of the Mountain Gazelle Optimizer in enhancing the fuzzy inference system. Additionally, a Transformer Network is utilized to identify the optimal path for tidying objects. This study significantly enhances the tidying capabilities and efficiency of service robots, contributing to real-world applications.
Ont4Sys: Ontology-based tool of Semantic Representation and Verification for Traceability Models

Some examples of systems and their organizations that have ignored or violated human values have caused very devastating and widespread damage. To prevent these incidents, operationalizing human values in systems transforms the values into concrete concepts such that they can be validated. There are several challenges such as a lack of techniques to integrate values, mechanisms to trace values, formalized perspective of values. To address these challenges, we propose Ont4Sys, an ontology-based tool of semantic representation and verification for traceability models with human value. Our research uses the formal theory Ontology to integrate value into the model and traces value under traceability’s guidance. The verification and labeling algorithm is provided to verify values and help with inspections. Two subject systems are selected for feasibility and accuracy evaluation. The experimental results show that our approach can effectively verify human values; moreover, based on traceability, there is at least a 50% reduction rate in model size to help with inspections. The labeling algorithm ensures high recall while minimizing the "noise" that is detrimental to the user’s understanding of the system.
A Case of Pure Red Cell Aplasia as a Possible Complication of Pulmonary Tuberculosis Treatment

Pure red cell aplasia (PRCA) is a rare disorder caused by failure or abnormalities in erythropoiesis only, among all cell lines of the bone marrow. Patients with PRCA usually present with no specific clinical presentation, thus its recognition depends on a clinician’s high index of suspicion, to facilitate early diagnosis and treatment to provide a better prognosis. Tuberculosis (TB) is the infectious disease with the highest casualty rate worldwide and is still a problem in many countries, including Indonesia. This study is a case report of a pulmonary TB patient with PRCA as a complication. PRCA was confirmed by the bone marrow puncture (BMP) examination as the gold standard. In this case, PRCA was inferred to occur due to the use of Isoniazid in the TB treatment regimen. The discontinuation of Isoniazid administration subsequently results in the normalization of Hb levels. In this paper we would highlight PRCA as a differential diagnosis that should be taken into consideration in symptomatic anemia in patients with tuberculosis under standard treatment.
Website User Interface Design Using Data Mining Task Centered System Design Method At National Private Humanitarian Institutions

Humanitarian organizations that support social communities by providing work opportunities for employees. In actuality, a website is required to serve as a channel for contacting new contributors and publicizing the organization. In order to create websites for humanitarian organizations using the task-centered system design methodology, research was done based on these issues. Identification, requirements, design as a scenario, and walkthrough evaluation are the four stages of this process. The PACT framework is used to identify users and necessary tasks during the identification phase. The tasks that are actually required are then chosen at the requirements stage. The task-based design is then completed using the Figma program during the design as scenario stage. The workflow and usability of the website, which was developed utilizing cognitive walkthrough and SUS, are also evaluated at this point. Based on the findings of the assessment, it can be said that cognitive walkthrough testing can be used to assess the components of an interface that are easy to learn, effective, and efficient, and that SUS can be used to assess the usability of the design outcomes. Based on the findings of the cognitive testing, a learnability and effectiveness score of 95% with the predicate "very good" and an average efficiency value of 0.1 goals/second with the predicate "very fast" were obtained. The SUS test then yielded an acceptable predicate and a SUS rating of 83
GraSS: Contrastive Learning With Gradient-Guided Sampling Strategy for Remote Sensing Image Semantic Segmentation

Self-supervised contrastive learning (SSCL) has achieved significant milestones in remote sensing image (RSI) understanding. Its essence lies in designing an unsupervised instance discrimination pretext task to extract image features from a large number of unlabeled images that are beneficial for downstream tasks. However, existing instance discrimination based SSCL suffers from two limitations when applied to the RSI semantic segmentation task: 1) Positive sample confounding issue, SSCL treats different augmentations of the same RSI as positive samples, but the richness, complexity, and imbalance of RSI ground objects lead to the model actually pulling a variety of different ground objects closer while pulling positive samples closer, which confuse the feature of different ground objects. 2) Feature adaptation bias, SSCL treats RSI patches containing various ground objects as individual instances for discrimination and obtains instance-level features, which are not fully adapted to pixel-level or object-level semantic segmentation tasks. To address the above limitations, we consider constructing samples containing single ground objects to alleviate positive sample confounding issue, and make the model obtain object-level features from the contrastive between single ground objects. Meanwhile, we observed that the discrimination information can be mapped to specific regions in RSI through the gradient of unsupervised contrastive loss, these specific regions tend to contain single ground objects. Based on this, we propose contrastive learning with Gradient guided Sampling Strategy (GraSS) for RSI semantic segmentation. GraSS consists of two stages: 1) the instance discrimination warm-up stage to provide initial discrimination information to the contrastive loss gradients, 2) the gradient guided sampling contrastive training stage to adaptively construct samples containing more singular ground objects using the discrimination information. Experimental results on three open datasets demonstrate that GraSS effectively enhances the performance of SSCL in high-resolution RSI semantic segmentation. Compared to eight baseline methods from six different types of SSCL, GraSS achieves an average improvement of 1.57% and a maximum improvement of 3.58% in terms of mean intersection over the union. Additionally, we discovered that the unsupervised contrastive loss gradients contain rich feature information, which inspires us to utilize gradient information more extensively during model training to attain additional model capacity. The source code is available at https://github.com/GeoX-Lab/GraSS.
Poze with Vogue

We present a poze and attire recommendation system that guides a user in how to best pose while taking a picture and provides the best alternatives for their attire as well as for their poses. Given an input image the system provides most attractive poses and trending fashion attire. Recommendation results in the system are influenced by the appearance and initial pose of the input image. Additionally, the user study indicates that the performance of the recommendation system is moderately correlated with the degree of conformity among the recommendations provided by different photographers. This suggests that the system takes into account factors such as image appearance and pose to generate tailored recommendations. It likely analyzes these aspects to determine the most suitable suggestions for a given image. In this project we have used human pose estimation model that provides thirty-three body point coordinates of an individual in a particular image. We have used a dataset containing insights from images using the above stated model. We have used both supervised and unsupervised learning for getting the best suggestions according to the user. At present, there are many image editing tools that provides some filters, and image enhancement techniques that can be used to beautify the already clicked image but there is no such application that provides posture suggestions while capturing the image. Therefore, this application is the first one in the field.
AMRDB: Design of an automatic analytical model for density-based clustering using augmented rule mining

Density based clustering (DBC) is an unsupervised learning method which is capable of identifying distinct groups of information. It works on the principle of instance-based variance maximization between distinct groups, while minimizing variance between similar groups of data. DBC models are capable of detecting regions of similar concentrated instances, and discard sparse instances based on pre-set distance ratios. Thus, selection of this distance ratio must be done intelligently in order to perform effective density-based clustering. A wide range of models are proposed for this purpose, but most of them work in a context-dependent manner, thereby limiting their scalability. In order to remove this drawback, an augmented rule mining approach that utilizes a combination of Apriori and frequent pattern growth (FPGrowth) is proposed in this text. The proposed approach initially evaluates minimum support values for Apriori & FPGrowth models via variance-based analytical modelling. Rules for these models are combined using instance-based matching, and are used for parametric tuning of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model. Due to a combination of Apriori & FPGrowth a large number of rules are extracted in a dataset-independent manner, which assist in optimization of DBSCAN’s inter-class and intra-class variance metrics. The results are compared with various state-of-the-art clustering models, and cluster metrics are evaluated. It is observed that the proposed model outperforms DBSCAN, and other density-based clustering techniques in terms of cluster formation efficiency, scalability and computational complexity on multiple datasets.
Barriers to blockchain adoption in the seaport industry: A fuzzy DEMATEL analysis

&lt;abstract&gt; &lt;p&gt;Blockchain technology, marked as a disruptive force across various sectors, including seaport logistics, faces challenges and obstacles that impede its effective adoption. We aim to empirically identify the significant barriers impeding blockchain adoption in the seaport industry and elucidate the interconnected relationships between these impediments. Utilizing the Fuzzy Decision-Making Trial and Evaluation Laboratory Analysis (Fuzzy DEMATEL) technique, we quantify the cause-and-effect relationships between various barriers to blockchain adoption. Structured interviews involving 18 experts were conducted, collecting both qualitative interview data and quantitative data. The nature of ports and the maritime industry did not seem to be accurately reflected in the literature about blockchain adoption, presenting several new findings in this study. Four primary obstacles were identified: 1) Lack of management support and commitment. 2) Issues in supply chain collaboration, communication and coordination. 3) Resistance from and lack of involvement of external stakeholders. 4) The high cost. Furthermore, cost was reaffirmed as a significant factor influencing blockchain adoption. We enhance existing literature by revealing the interdependencies among identified barriers and offers insights for policymakers and industry practitioners. We aim to foster successful blockchain integration in the seaport industry, improving its sustainability performance. During this research, it has been acknowledged by the business sector that the effective employment of business process re-engineering (BPR) and the strategic implementation of blockchain technology are crucial strategies to surmount the obstacles that have impeded the extensive integration of blockchain within port operations.&lt;/p&gt; &lt;/abstract&gt;
Automatic Dance Pose Estimation from the Hand Signs using Deep Learning

Abstract: Human activity is the sequential change of the human body. The collection and assessment of multimedia content connected to dance will be beneficial for the preservation of cultural heritage, the creation of video recommendation systems, and the assistance of students through tutoring systems. Indian classical dance (ICD) classification is still a fascinating subject of research because of its intricate hand gestures. Changes in learning habits make automated teaching solutions unavoidable in many fields, from traditional to internet forums. ICD also becomes an essential part of a vibrant legacy and culture that needs to be updated and maintained at all costs. Complex poses including full-body rotation and self-hands-occlusion are part of the dance. The primary goal of this research is to create a framework for Bharatanatyam dancing.
Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells

Several hematologic diseases, including malaria, diabetes, and sickle cell anemia, result in a reduced red blood cell deformability. This deformability can be measured using a microfluidic device with channels of varying width. Nevertheless, it is challenging to algorithmically recognize large numbers of red blood cells and quantify their deformability from image data. Deep learning has become the method of choice to handle noisy and complex image data. However, it requires a significant amount of labeled data to train the neural networks. By creating images of cells and mimicking noise and plasticity in those images, we generate synthetic data to train a network to detect and segment red blood cells from video-recordings, without the need for manually annotated labels. Using this new method, we uncover significant differences between the deformability of RBCs infected with different strains of Plasmodium falciparum, providing clues to the variation in virulence of these strains.
A Structural Analysis on early childhood teacher’s creative teaching behavior, reflective thinking and play sensitivity

Objectives The purpose df this study is to analyze the structural relationship between perceived creative teaching behavior by early childhood teacher, their reflective thinking, and play sensitivity.&#x0D; Methods For this purpose, a survey was administered to 456 teachers of kindergartens and daycare centers. The collected data were analyzed using SPSS and AMOS program. We analyzed the structural relationship between creative teaching behavior, reflective thinking, and play sensitivity by applying a structural equation model. The bootstrapping was conducted to verify the significance of the mediating effect.&#x0D; Results The research results were as follows; first, there were significant correlations among early childhood teacher’s creative teaching behavior, reflective thinking, and play sensitivity. Second, early childhood teacher’s creative teaching behavior directly affected on play sensitivity and indirectly gave an influence on play sensitivity by a mediation of reflective thinking. In accordance, there was a significant partial mediating effects of early child-hood teacher’s reflective thinking on the relationship between their creative teaching behavior and play sensitivity.&#x0D; Conclusions These results revealed that creative teaching behavior and reflective thinking would be the variables closely related with early childhood teacher’s play sensitivity. This implied that efforts would be urgently needed to in crease early childhood teacher’s creative teaching behavior and reflective thinking as well as to promote play sensitivity.