Interaction of Knowledge Spirals to Create Ontologies for An Institutional Repository of Educational Innovation Best Practices

2017 ◽  
Vol 8 (2) ◽  
pp. 72-92 ◽  
Author(s):  
María Luisa Sein-Echaluce ◽  
Ana Rosa Abadía-Valle ◽  
Concepción Bueno-García ◽  
Ángel Fidalgo-Blanco
Keyword(s):  
Best Practices ◽  
Continuous Flow ◽  
Educational Innovation ◽  
Descriptive Study ◽  
Proposed Model ◽  
New Knowledge ◽  
Subsequent Application ◽  
Expert Team ◽  
Points Of View

Previous studies have shown the usefulness of ontologies in the creation, consolidation, distribution and combination of new knowledge in the field of educational innovation to obtain a continuous flow of knowledge between individuals and organizations. In this paper, some phases of Nonaka's epistemological and ontological spirals are modified, and a layer to interact between them is added to create an ontology for a specific organization of higher education. The proposed model allows the classification of educational innovation best practices and encourages their transference into the organization through a knowledge management system developed in previous works. The proposed ontology is validated through a descriptive study. This allows a comparison of the different points of view of the authors of the best practices and those of an expert team, all involved in the knowledge spirals. This paper offers an ontology to classify educational innovation best practices and facilitate the search for these and their subsequent application in other contexts.

CLINICAL AND PARA-CLINICAL CHARACTERISTICS OF SICK NEWBORN BABIES TREATED AT PEDIATRIC DEPARTMENT OF HUE UNIVERSITY HOSPITAL

2011 ◽  
pp. 112-117
Author(s):  
Thi Kieu Nhi Nguyen
Keyword(s):  
Respiratory Distress ◽  
Congenital Malformation ◽  
Clinical Characteristics ◽  
Descriptive Study ◽  
Neonatal Unit ◽  
University Hospital ◽  
Pediatric Department ◽  
Sick Newborn ◽  
Observational Descriptive Study

Objectives: 1. Describe neonatal classification of WHO. 2. Identify some principal clinical and paraclinical signs of term, preterm, post term babies. Patients and method: an observational descriptive study of 233 newborns hospitalized in neonatal unit at Hue university‘ s hospital was done during 12 months from 01/01/2009 to 31/12/2009 for describing neonatal classification and identifying principal clinical and paraclinical signs. Results: Premature (16.74%); Term babies (45.5%); Post term (37.76%); Premature: asphyxia (43.59%), hypothermia (25.64%), vomit (30.77%), jaundice (61.54%), congenital malformation (17.95%); CRP > 10mg/l (53.85%); anemia Hb < 15g/dl (12.82%). Term babies: poor feeding (21.7%); fever (24.53%); CRP > 10mg/l (53.77%); Hyperleucocytes/ Leucopenia (35.85%). Post term: respiratory distress (34%); lethargy (29.55%); vomit (26.14%); polycuthemia (1.14%); hypoglycemia (22.73%). Conclusion: each of neonatal type classified by WHO presente different clinical and paraclinical. Signs. The purpose of this research is to help to treat neonatal pathology more effectively.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

scholarly journals Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jianfang Cao ◽  
Minmin Yan ◽  
Yiming Jia ◽  
Xiaodong Tian ◽  
Zibang Zhang
Keyword(s):  
Transfer Learning ◽  
Adaptive Learning ◽  
Deep Level ◽  
Image Resolution ◽  
Learning Rate ◽  
Stochastic Gradient Descent ◽  
Natural Image ◽  
Historical Period ◽  
Proposed Model

AbstractIt is difficult to identify the historical period in which some ancient murals were created because of damage due to artificial and/or natural factors; similarities in content, style, and color among murals; low image resolution; and other reasons. This study proposed a transfer learning-fused Inception-v3 model for dynasty-based classification. First, the model adopted Inception-v3 with frozen fully connected and softmax layers for pretraining over ImageNet. Second, the model fused Inception-v3 with transfer learning for parameter readjustment over small datasets. Third, the corresponding bottleneck files of the mural images were generated, and the deep-level features of the images were extracted. Fourth, the cross-entropy loss function was employed to calculate the loss value at each step of the training, and an algorithm for the adaptive learning rate on the stochastic gradient descent was applied to unify the learning rate. Finally, the updated softmax classifier was utilized for the dynasty-based classification of the images. On the constructed small datasets, the accuracy rate, recall rate, and F1 value of the proposed model were 88.4%, 88.36%, and 88.32%, respectively, which exhibited noticeable increases compared with those of typical deep learning models and modified convolutional neural networks. Comparisons of the classification outcomes for the mural dataset with those for other painting datasets and natural image datasets showed that the proposed model achieved stable classification outcomes with a powerful generalization capacity. The training time of the proposed model was only 0.7 s, and overfitting seldom occurred.

Usefulness of Assessment Techniques in Assessing Classification for Emotional Disturbance and Generating Classroom Recommendations

2000 ◽  
Vol 90 (1) ◽  
pp. 250-252 ◽  
Author(s):  
Gail M. Cheramie ◽  
Krystina M. Griffin ◽  
Tina Morgan
Keyword(s):  
Best Practices ◽  
School Psychologists ◽  
Emotional Disturbance ◽  
Rating Scales ◽  
Perceived Usefulness ◽  
Direct Link ◽  
Projective Techniques ◽  
Assessment Techniques ◽  
Specialist School

A national survey of specialist school psychologists examined the perceived usefulness of assessment techniques in making decisions regarding eligibility for the educational classification of emotional disturbance and in generating classroom recommendations. Analysis showed measures rated as most useful were interviews with the parent, teacher, and student, observations of the student, and norm-referenced rating scales. Projective techniques were least useful. These findings are important in the context of “best practices” for the multidimensional assessment of emotional disturbance which promotes a more direct link between assessment and intervention.

Immune-Based Systems on Network Security Situation Awareness and Risk Prediction Model

2014 ◽  
Vol 643 ◽  
pp. 99-104
Author(s):  
Jin Yang ◽  
Yun Jie Li ◽  
Qin Li
Keyword(s):  
Situation Awareness ◽  
Artificial Immune Systems ◽  
Epidemic Spreading ◽  
State Spaces ◽  
Real Time Processing ◽  
Network Intrusion ◽  
Markov Chain Theory ◽  
Proposed Model ◽  
Network Risk

In this paper, the process of the developments and changes of the network intrusion behaviors were analyzed. An improved epidemic spreading model was proposed to study the mechanisms of aggressive behaviors spreading, to predict the future course of an outbreak and to evaluate strategies to control a network epidemic. Based on Artificial Immune Systems, the concepts and formal definitions of immune cells were given. And in this paper, the forecasting algorithm based on Markov chain theory was proposed to improve the precision of network risk forecasting. The data of the Memory cells were analyzed directly and kinds of state-spaces were formed, which can be used to predict the risk of network situation by analyzing the cells status and the classification of optimal state. Experimental results show that the proposed model has the features of real-time processing for network situation awareness.

PENYUSUNAN BUKU PANDUAN PRAKTIK WISATA YANG BERTANGGUNG JAWAB DALAM PENDAKIAN GUNUNG

2017 ◽  
Vol 11 (03) ◽  
pp. 53-64
Author(s):  
Jussac Maulana Masjhoer ◽  
Dwi Wibowo ◽  
Bijak Qoulan Sadida ◽  
Inosensius Tito Ogista
Keyword(s):  
Best Practices ◽  
Survey Research ◽  
Research Method ◽  
Mountain Climbing ◽  
Tourist Behavior ◽  
The Public ◽  
Lack Of Information ◽  
Strongly Agree ◽  
Responsible Tourism

The lack of information related to the best practices in responsible tourism is one of the causes to tourist behavior problems. This study aims to determine the behavior of tourists in hiking, the adoption of responsible tourism practices, and to compile a responsible tourism practices guidebook. The research method used is survey research by spreading the questionnaire. Based on Likert analysis, at the pre-ascending stage, the classification of attitudes indicated by the respondents for cost and transportation is quite agreeable, while for equipment, guide, and simaksi is agreed. The ascent stage, the indicator when going up the mountain is quite agree, camping is not agree, the cook is agree, and when down the mountain is strongly agree. The post-ascent stage shows quite agreeable attitude. The public test of the guidebook, for the aspect of size and language of submission shows an agreeable attitude, while for the design, thickness, and content of the book shows an agreeable attitude. The conclusion is that (1) Still found the behavior of tourists in mountain climbing that is not environmentally friendly, (2) The responsible tourism practices of mountaineering that includes pre-ascending, ascent, and post-ascent, not well implemented by tourists, and (3) The responsible tourism practices guidebook still lack both technical and substance. Keywords: responsible tourism, mountain hiking, guidebook, tourist behavior

Speech Influence Mechanisms and Models in Polycode and Polymodal Text (Using the Example of Regional Television Sports Coverage)

2021 ◽  
Vol 10 (1) ◽  
pp. 72-76
Author(s):  
Elina Nikitina
Keyword(s):  
Information Sharing ◽  
New Knowledge ◽  
Field Of Values ◽  
Sports Coverage ◽  
Points Of View

This article analyzes speech influence mechanisms and models in polycode and polymodal text. As an example, we took a sports coverages aired on regional television, since it is a polycode and polymodal composing. The publication presents speech influence mechanisms and models proposed by various researchers. Taking into consideration various points of view it can be assumed that speech influence in television sports coverage occurs through the information sharing on two levels proposed by A.A. Leontiev. This process is carried out either by introducing new knowledge about reality into the field of values of the recipient, on the basis of which he will change his behavior or his attitude to this reality, or by changing the field of values of the recipient without introducing new elements.

scholarly journals Correlation between Campanacci’s radiological classification of giant cell tumor of bone and expression of Cyclin D1 and PCNA

2017 ◽  
Vol 7 (1) ◽  
pp. 47
Author(s):  
Eréndira G. Estrada-Villaseñor ◽  
Hidalgo Bravo Alberto ◽  
C. Bandala ◽  
P. De la Garza-Montano ◽  
Reyes Medina Naxieli ◽  
...  
Keyword(s):  
Cyclin D1 ◽  
Giant Cell Tumor ◽  
Giant Cell ◽  
Descriptive Study ◽  
Cell Tumor ◽  
Giant Cell Tumors ◽  
Radiological Classification ◽  
Tumors Of Bone ◽  
Made In

Giant cell tumor of bone is considered by his behavior a benign but aggressive neoplasm. The objective of our study was to determine if there is a correlation between the Campanacci’s radiological classification of giant cell tumors of bone and the expression by immunohistochemistry of Cyclin D1 and proliferation cell nuclear antibody (PCNA). A retrospective and descriptive study was made. In total, there were 27 cases. All cases showed Cyclin D1 and PCNA positivity. Rho Spearman for Campanacci and Cyclin D1 expression was 0.06 and for Campanacci and PCNA was 0.418. We conclude that there is a positive correlation between PCNA expression in giant cell tumors of Bone and the Campanacci’s radiological classification II and III, butCyclin D1 expression was no related with radiologic features.

scholarly journals Modified Decision Tree Technique for Ransomware Detection at Runtime through API Calls

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Faizan Ullah ◽  
Qaisar Javaid ◽  
Abdu Salam ◽  
Masood Ahmad ◽  
Nadeem Sarwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Feature Vector ◽  
Machine Learning Algorithms ◽  
The Novel ◽  
Proposed Model ◽  
Testing Accuracy ◽  
Financial Losses

Ransomware (RW) is a distinctive variety of malware that encrypts the files or locks the user’s system by keeping and taking their files hostage, which leads to huge financial losses to users. In this article, we propose a new model that extracts the novel features from the RW dataset and performs classification of the RW and benign files. The proposed model can detect a large number of RW from various families at runtime and scan the network, registry activities, and file system throughout the execution. API-call series was reutilized to represent the behavior-based features of RW. The technique extracts fourteen-feature vector at runtime and analyzes it by applying online machine learning algorithms to predict the RW. To validate the effectiveness and scalability, we test 78550 recent malign and benign RW and compare with the random forest and AdaBoost, and the testing accuracy is extended at 99.56%.

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