scholarly journals User-Centric Learning for Multiple Access Se-lections

2019 ◽  
Vol 9 (1) ◽  
pp. 2338-2344
Keyword(s):  
Urban Areas ◽  
Learning Algorithm ◽  
Point Of View ◽  
Optimal Decision ◽  
User Association ◽  
Learning To Learn ◽  
Business Growth ◽  
Wireless System ◽  
Q Learning ◽  
User Centric

We are in the age where business growth is based on how user-centric your services or goods is. Current research on wireless system is more focused on ensuring that user could achieve optimal throughput with minimal delay, disregarding what user actually wants from the services. Looking from connectivity point of view, especially in urban areas these days, there are multiple mobile and wireless access that user could choose to get connected to. As people are looking toward machine automation, we understand that the same could be done for allowing users to choose services based on their own requirement. This paper looks into unconventional, non-disruptive approach to provide mobile services based on user requirements. The first stage of this study is to look for user association from three new perspectives. The second stage involved utilizing a reinforcement learning algorithm known as q-learning, to learn from feedbacks to identify optimal decision in reaching user-centric requirement goal. The outcome from the proposed deployment has shown significant improvement in user association with learning aware solution.

Application of improved Q learning algorithm to job shop problem

2009 ◽  
Vol 28 (12) ◽  
pp. 3268-3270
Author(s):  
Chao WANG ◽  
Jing GUO ◽  
Zhen-qiang BAO
Keyword(s):  
Job Shop ◽  
Learning Algorithm ◽  
Q Learning

Integrated waste water management for a small river basin - a case study

1998 ◽  
Vol 38 (11) ◽  
pp. 87-95
Author(s):  
R. Fenz ◽  
M. Zessner ◽  
N. Kreuzinger ◽  
H. Kroiss
Keyword(s):  
Waste Water ◽  
Water Management ◽  
River Basin ◽  
Rural Areas ◽  
Urban Areas ◽  
Waste Water Treatment ◽  
Point Of View ◽  
Political Issue ◽  
The North ◽  
Waste Water Management

In Austria approximately 70% of the population is connected to sewerage and to biological waste water treatment plants. Whereas the urban areas are already provided with these facilities to a very high extent, effort is still needed in rural areas to meet the requirements of the Austrian legislation. The way, this task should be solved has provoked much controversy. It is mainly the question, whether centralised or decentralised sewage disposal systems are preferable from the ecological and economical point of view, that became a political issue during the last 5 years. The Institute for Water Quality and Waste Management was asked to elaborate a waste water management concept for the Lainsitz River Basin, a mainly rural area in the north of Austria discharging to the Elbe river. Both ecological and economical aspects should be considered. This paper presents the methodology that was applied and the criteria which were decisive for the selection of the final solution.

scholarly journals Transition from Emotional to Rational Advertising for Food Products on the Romanian Market

2017 ◽  
Vol 50 (1) ◽  
pp. 101-108
Author(s):  
A.F. Jităreanu ◽  
Elena Leonte ◽  
A. Chiran ◽  
Benedicta Drobotă
Keyword(s):  
Urban Areas ◽  
Eating Habits ◽  
Well Being ◽  
Point Of View ◽  
The Body ◽  
Positive Effects ◽  
New Methods ◽  
Brand Advertising ◽  
Tangible Evidence ◽  
Health And Well Being

Abstract Advertising helps to establish a set of assumptions that the consumer will bring to all other aspects of their engagement with a given brand. Advertising provides tangible evidence of the financial credibility and competitive presence of an organization. Persuasion is becoming more important in advertising. In marketing, persuasive advertising acts to establish wants/motivations and beliefs/attitudes by helping to formulate a conception of the brand as being one which people like those in the target audience would or should prefer. Considering the changes in lifestyle and eating habits of a significant part of the population in urban areas in Romania, the paper aims to analyse how brands manage to differentiate themselves from competitors, to reposition themselves on the market and influence consumers, meeting their increasingly varied needs. Food brands on the Romanian market are trying, lately, to identify new methods of differentiation and new benefits for their buyers. Given that more and more consumers are becoming increasingly concerned about what they eat and the products’ health effects, brands struggle to highlight the fact that their products offer real benefits for the body. The advertisements have become more diversified and underline the positive effects, from the health and well - being point of view, that those foods offer (no additives and preservatives, use of natural ingredients, various vitamins and minerals or the fact that they are dietary). Advertising messages’ diversification is obvious on the Romanian market, in the context of an increasing concern of the population for the growing level of information of some major consumer segments.

scholarly journals The Learning to Learn Competence in Early Childhood Preservice Teachers: An Outdoor and e/m-Learning Experience in the Museum

2021 ◽  
Vol 13 (2) ◽  
pp. 25
Author(s):  
Daniel Abril-López ◽  
Hortensia Morón-Monge ◽  
María del Carmen Morón-Monge ◽  
María Dolores López Carrillo
Keyword(s):  
Social Sciences ◽  
Preservice Teachers ◽  
Early Childhood ◽  
Teaching And Learning ◽  
Formal Education ◽  
Learning Experience ◽  
Point Of View ◽  
Learning To Learn ◽  
Outdoor Experience ◽  
Outdoor Experiences

This study was developed with Early Childhood Preservice Teachers within the framework of the Teaching and Learning of Social Sciences over three academic years (2017–2018, 2018–2019, and 2019–2020) at the University of Alcalá. The main objective was to improve the learning to learn competence during teacher training from an outdoor experience at the Museum of Guadalajara (Spain), using e/m-learning tools (Blackboard Learn, Google Forms, QR codes, and websites) and the inquiry-based learning approach. To ascertain the level of acquisition of this competence in those teachers who were being trained, their self-perception—before and after—of the outdoor experience was assessed through a system of categories adapted from the European Commission. The results show a certain improvement in this competence in Early Childhood Preservice Teachers. Additionally, this outdoor experience shows the insufficient educational adaptation of the museum to the early childhood education stage from a social sciences point of view. Finally, we highlight the importance of carrying out outdoor experiences from an inquiry-based education approach. These outdoor experiences should be carried out in places like museums to encourage contextualized and experiential learning of the youngest in formal education.

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 126
Author(s):  
Sharu Theresa Jose ◽  
Osvaldo Simeone
Keyword(s):  
Learning Algorithm ◽  
Broad Class ◽  
Performance Measure ◽  
Training Data ◽  
Learning To Learn ◽  
Data Set ◽  
Information Theoretic ◽  
Meta Learning ◽  
Task Training ◽  
Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

scholarly journals Aircraft Maintenance Check Scheduling Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 113
Author(s):  
Pedro Andrade ◽  
Catarina Silva ◽  
Bernardete Ribeiro ◽  
Bruno F. Santos
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
Q Learning ◽  
Scheduling Policy ◽  
Real Scenario ◽  
Maintenance Plan ◽  
Small Disturbances

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.

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