Semantic Mapping between LOM – SCORM Content Package and MPEG-7 Concepts

2010 ◽  
pp. 321-345
Author(s):  
Varvara Vagiati
Keyword(s):  
Current Status ◽  
Multimedia Content ◽  
Semantic Mapping ◽  
Proposed Model ◽  
Mapping Process ◽  
Mapping Techniques ◽  
Metadata Interoperability

This chapter presents the current status of the efforts to harmonize MPEG-7 and SCORM Content Package (including the LOM description metadata, part of SCORM). In particular a model for the interoperability between these standards is developed. The MPEG-7 provides a standardized set of technologies for describing multimedia content, while SCORM is a collection of specifications for developing, organizing and delivering instructional content.The proposed model concerns the semantic mapping between the different elements of these standards, which are created to satisfy the specific needs of different communities. The followed approach is based on the main principles and procedures for metadata interoperability, such as on the crosswalking and mapping techniques. Moreover some empirical remarks conclude the mapping process.

scholarly journals CLASSROOM ACTION RESEARCH: Using Semantic Mapping in Teaching Descriptive Paragraph

ELT-Lectura ◽  
2015 ◽  
Vol 2 (2) ◽  
Author(s):  
Dahler Dahler
Keyword(s):  
Action Research ◽  
Semantic Mapping ◽  
The Third ◽  
Mapping Strategy ◽  
Class D ◽  
Writing Skill ◽  
Mapping Process ◽  
Field Notes ◽  
Classroom Action Research ◽  
Writing Tests

This classroom action research conducted to 38 participants at the seventh grade class D of SMPN 29 in2011/2012 academic years. It tries to improve their writing skill by applying semantic mapping strategy. Theresearcher collecting writing tests, observations, field notes, and interview as the instrument. The date reveals that improvement exists after the treatment on the students' writing skill. The data indicates that some factors influenced their improvement. The first was brainstorming process that led them easy to convey and think about the ideas. The second was the categorization process that made them easy to determine kinds of the idea. The third was the mapping process made them easy to write a good descriptive paragraph. The last was the teacher’s roles facilitated them to have an effective class.

Multimedia Social Network Modeling using Hypergraphs

2016 ◽  
Vol 7 (3) ◽  
pp. 53-77 ◽  
Author(s):  
Giancarlo Sperlì ◽  
Flora Amato ◽  
Vincenzo Moscato ◽  
Antonio Picariello
Keyword(s):  
Social Networks ◽  
Data Model ◽  
Multimedia Content ◽  
Ranking Functions ◽  
Proposed Model ◽  
Hypergraph Learning ◽  
Efficiency And Effectiveness ◽  
Social Network Modeling ◽  
Multimedia Recommendation ◽  
Combine Information

In this paper the authors define a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and to represent in a simple way all the different kinds of relationships that are typical of social networks and multimedia sharing systems, and in particular between multimedia contents, among users and multimedia content and among users themselves. Different applications (e.g. influence analysis, multimedia recommendation) can be then built on the top of the introduce data model thanks to the introduction of proper user and multimedia ranking functions. In addition, the authors provide a strategy for hypergraph learning from social data. Some preliminary experiments concerning efficiency and effectiveness of the proposed approach for analysis of Last.fm network are reported and discussed.

A New Location-Allocation Model for Blood Distribution Considering Limited Lifespan Under Fuzzy Conditions

2018 ◽  
Vol 9 (4) ◽  
pp. 105-127
Author(s):  
Vahidreza Ghezavati ◽  
Yasser Moeini
Keyword(s):  
Real World ◽  
Blood Donors ◽  
Blood Donation ◽  
Fuzzy Theory ◽  
Current Status ◽  
Long Distance ◽  
Allocation Model ◽  
Blood Donations ◽  
Location Allocation ◽  
Proposed Model

One of the important centers among the health facilities is the centers of blood donations. Blood donors may not be able to donate because of the long distance between blood donation centers and their location. In this article, a dynamic hierarchical location-allocation model with fuzzy conditions is offered to locate blood donation centers and assign blood donors to these centers. The limited life span for blood is considered as the most important model assumption. Because of this in real world situations, fuzzy theory and uncertainty approaches will be applied to formulate the problem. In addition, the total amount of donated blood for each area is not definite. The ratio between different expiration dates and blood depends on blood donations during different periods, so this parameter is faced with uncertainty. Numerical examples are presented to show benefits the and the performance of proposed model. In addition, the proposed model is run for a real-world case in the city of Tehran, Iran. The results indicate that applying the proposed optimization model can improve amount of shortage and inventory in the blood network against current status in the case study. Besides, experiments indicate that applying fuzzy theory for this problem can reduce 12.5% of total costs via the certain formulation.

Learning Sentence-to-Hashtags Semantic Mapping for Hashtag Recommendation on Microblogs

2022 ◽  
Vol 16 (2) ◽  
pp. 1-26
Author(s):  
Riccardo Cantini ◽  
Fabrizio Marozzo ◽  
Giovanni Bruno ◽  
Paolo Trunfio
Keyword(s):  
Generative Models ◽  
Semantic Mapping ◽  
Semantic Features ◽  
Huge Amount ◽  
Expansion Strategy ◽  
Language Representation ◽  
Combined Use ◽  
Mapping Process ◽  
Social Media Platforms ◽  
Semantic Expansion

The growing use of microblogging platforms is generating a huge amount of posts that need effective methods to be classified and searched. In Twitter and other social media platforms, hashtags are exploited by users to facilitate the search, categorization, and spread of posts. Choosing the appropriate hashtags for a post is not always easy for users, and therefore posts are often published without hashtags or with hashtags not well defined. To deal with this issue, we propose a new model, called HASHET ( HAshtag recommendation using Sentence-to-Hashtag Embedding Translation ), aimed at suggesting a relevant set of hashtags for a given post. HASHET is based on two independent latent spaces for embedding the text of a post and the hashtags it contains. A mapping process based on a multi-layer perceptron is then used for learning a translation from the semantic features of the text to the latent representation of its hashtags. We evaluated the effectiveness of two language representation models for sentence embedding and tested different search strategies for semantic expansion, finding out that the combined use of BERT ( Bidirectional Encoder Representation from Transformer ) and a global expansion strategy leads to the best recommendation results. HASHET has been evaluated on two real-world case studies related to the 2016 United States presidential election and COVID-19 pandemic. The results reveal the effectiveness of HASHET in predicting one or more correct hashtags, with an average F -score up to 0.82 and a recommendation hit-rate up to 0.92. Our approach has been compared to the most relevant techniques used in the literature ( generative models , unsupervised models, and attention-based supervised models ) by achieving up to 15% improvement in F -score for the hashtag recommendation task and 9% for the topic discovery task.

Cloud and IoT based privacy preserved e-Healthcare system using secured storage algorithm and deep learning

2020 ◽  
Vol 39 (3) ◽  
pp. 3011-3023
Author(s):  
T. Munirathinam ◽  
Sannasi Ganapathy ◽  
Arputharaj Kannan
Keyword(s):  
Deep Learning ◽  
Disease Severity ◽  
Healthcare System ◽  
Data Storage ◽  
Original Data ◽  
Medical Data ◽  
Heart Beat ◽  
Current Status ◽  
Learning Approach ◽  
Proposed Model

Rapid introduction of new diseases and the severity improvement of existing dead diseases due to the bad food habits and lacking of awareness over the health conscious food items those are available in the market. The Internet of Things (IoT) gets more attention for reducing the disease severity by knowing the current status of their disease according to the dynamic inputs of human body through IoT devices today. Moreover, the combination of IoT and cloud computing technologies are playing major roles in e-health services. In this scenario, security is a major issue in the process of data storage and communication. For this purpose, we propose a new e-healthcare system for monitoring the dead disease level by using the technologies such as IoT and Cloud with the help of deep learning approach and fuzzy rules with temporal features. In this system, the medical data is retrieved from various located patients who are utilizing the e-healthcare assisting devices. First, the retrieved and encrypted data is stored in cloud by applying a newly proposed secured cloud storage algorithm. Second, the stored data can be retrieved the data as original data by applying the decryption process. Third, a new cloud framework is introduced for predicting the status of heart beat rates and diabetes levels by using the medical data that is created by applying the UCI Repository dataset. In addition, a new deep learning approach which applies the Convolutional Neural Network for predicting the disease severity. The experimental results are obtained by conducting various experiments for the proposed model by using the dataset and the hospital patient records. The proposed model results outperforms the available disease prediction systems in terms of prediction accuracy.

Multimedia Social Network Modeling Using Hypergraphs

2018 ◽  
pp. 636-660
Author(s):  
Giancarlo Sperlì ◽  
Flora Amato ◽  
Vincenzo Moscato ◽  
Antonio Picariello
Keyword(s):  
Social Networks ◽  
Data Model ◽  
Multimedia Content ◽  
Ranking Functions ◽  
Proposed Model ◽  
Hypergraph Learning ◽  
Efficiency And Effectiveness ◽  
Social Network Modeling ◽  
Multimedia Recommendation ◽  
Combine Information

In this paper the authors define a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and to represent in a simple way all the different kinds of relationships that are typical of social networks and multimedia sharing systems, and in particular between multimedia contents, among users and multimedia content and among users themselves. Different applications (e.g. influence analysis, multimedia recommendation) can be then built on the top of the introduce data model thanks to the introduction of proper user and multimedia ranking functions. In addition, the authors provide a strategy for hypergraph learning from social data. Some preliminary experiments concerning efficiency and effectiveness of the proposed approach for analysis of Last.fm network are reported and discussed.

scholarly journals Proactive Mitigation of the Revocation Risk for Preemptible Virtualized Resources

2021 ◽  
Author(s):  
Panagiotis Oikonomou ◽  
Kostas Kolomvatsos ◽  
Christos Anagnostopoulos
Keyword(s):  
Theoretical Basis ◽  
Performance Metrics ◽  
Current Status ◽  
High Demand ◽  
Generic Models ◽  
Proposed Model ◽  
Intermediate Outcomes ◽  
Optimal Stopping Theory ◽  
Multiple Tasks ◽  
Pros And Cons

<div>The provision of resources at the Cloud follows two generic models. The first model guarantees the provided resources for the requested time while the second involves unreliable resources with lower price compared to the former scheme but with no guarantees concerning an unexpected revocation due to a high demand. In this paper, we focus on the latter model and propose a scheme that monitors the course of execution of tasks placed at unreliable resources and decides when to store their current progress avoid jeopardizing intermediate outcomes in unexpected revocations. We rely on the principles of Optimal Stopping Theory (OST) to manage multiple tasks and decide for which task and when we have to save its current status. The outcome is a novel checkpointing mechanism fully aligned with the needs of the dynamics of an unreliable environment. The proposed model builds upon the heterogeneity of the available services in the Cloud and concludes a proactive mitigation approach of the revocation risk for unreliable virtualized resources. We present the theoretical basis of our mechanism and describe the solution of the identified problem. The pros and cons of our approach are evaluated through extensive simulations and a set of performance metrics.</div>

scholarly journals Proactive Mitigation of the Revocation Risk for Preemptible Virtualized Resources

2021 ◽  
Author(s):  
Panagiotis Oikonomou ◽  
Kostas Kolomvatsos ◽  
Christos Anagnostopoulos
Keyword(s):  
Theoretical Basis ◽  
Performance Metrics ◽  
Current Status ◽  
High Demand ◽  
Generic Models ◽  
Proposed Model ◽  
Intermediate Outcomes ◽  
Optimal Stopping Theory ◽  
Multiple Tasks ◽  
Pros And Cons

<div>The provision of resources at the Cloud follows two generic models. The first model guarantees the provided resources for the requested time while the second involves unreliable resources with lower price compared to the former scheme but with no guarantees concerning an unexpected revocation due to a high demand. In this paper, we focus on the latter model and propose a scheme that monitors the course of execution of tasks placed at unreliable resources and decides when to store their current progress avoid jeopardizing intermediate outcomes in unexpected revocations. We rely on the principles of Optimal Stopping Theory (OST) to manage multiple tasks and decide for which task and when we have to save its current status. The outcome is a novel checkpointing mechanism fully aligned with the needs of the dynamics of an unreliable environment. The proposed model builds upon the heterogeneity of the available services in the Cloud and concludes a proactive mitigation approach of the revocation risk for unreliable virtualized resources. We present the theoretical basis of our mechanism and describe the solution of the identified problem. The pros and cons of our approach are evaluated through extensive simulations and a set of performance metrics.</div>

scholarly journals Status of Curriculum Mapping of Undergraduate Medical Programmes in South Africa

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Gerda C. Botha ◽  
Adegoke O. Adefolalu
Keyword(s):  
South Africa ◽  
Medical Schools ◽  
Host Community ◽  
Current Status ◽  
Future Research ◽  
Curriculum Mapping ◽  
Structured Interviews ◽  
Curriculum Review ◽  
The Status ◽  
Mapping Process

Curriculum mapping in medical education allows for quick determination whether the curriculum meets the required standards and if its contents are aligned with the learning outcomes. This ensures the curriculum stays relevant, producing graduates capable of addressing the health needs of the institution’s host community. The status of curriculum mapping of the undergraduate medical programmes in South African medical schools was not documented in the literature at the time of this research. This study aimed to describe the current status of curriculum mapping of undergraduate medical programmes in South Africa. A qualitative study was conducted among the academic managers from all the eight medical schools in 2015. Semi-structured interviews were used to collect data from fourteen participants who were purposefully sampled, and data analysis was done by inductive thematic analysis after coding and verbatim transcriptions. None of the medical schools had a fully developed mapping platform, however they all possessed various guides and matrices that contained components of their curricula which were mainly used for accreditation purposes. In addition, they all had strategies in place for reviewing their curricula, although some of the institutions were at different stages of developing their own mapping platforms. The challenges described by the institutions as barriers to curriculum review appeared to be related to lack of a proper curriculum mapping process. In conclusion, curriculum mapping was in infancy stage at the time of this research in South Africa, the medical schools that were in the process or about to develop electronic mapping platforms had no uniform outcome framework. Future research on the features of the mapping platforms developed by all the institutions is highly recommended.

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