Guiding Labelling Effort for Efficient Learning With Georeferenced Images

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot’s motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions. To address the controller adaptation challenge to dynamically changing task environments, we propose using a probabilistic learning approach for a millimeter-scale magnetic walking soft robot using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme by finding the gait controller parameters while optimizing the stride length of the walking soft millirobot using a small number of physical experiments. To demonstrate the controller adaptation, we test the walking gait of the robot in task environments with different surface adhesion and roughness, and medium viscosity, which aims to represent the possible conditions for future robotic tasks inside the human body. We further utilize the transfer of the learned GP parameters among different task spaces and robots and compare their efficacy on the improvement of data-efficient controller learning.

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Technology-mediated teaching and learning process: A conceptual study of educators’ response amidst the Covid-19 pandemic

Education and Information Technologies ◽

10.1007/s10639-021-10527-x ◽

2021 ◽

Author(s):

Kingsley Okoye ◽

Jorge Alfonso Rodriguez-Tort ◽

Jose Escamilla ◽

Samira Hosseini

Keyword(s):

Teaching And Learning ◽

Linear Trend ◽

Univariate Analysis ◽

Learning Experiences ◽

Well Being ◽

Learning Tools ◽

Main Challenge ◽

Teachers And Students ◽

Efficient Learning ◽

Teaching Learning

AbstractThe COVID-19 pandemic has disrupted many areas of the human and organizational ventures worldwide. This includes new innovative technologies and strategies being developed by educators to foster the rapid learning-recovery and reinstatement of the stakeholders (e.g., teachers and students). Indeed, the main challenge for educators has been on what appropriate steps should be taken to prevent learning loss for the students; ranging from how to provide efficient learning tools/curriculum that ensures continuity of learning, to provision of methods that incorporate coping mechanisms and acceleration of education in general. For several higher educational institutions (HEIs), technology-mediated education has become an integral part of the modern teaching/learning instruction amidst the Covid-19 pandemic, when digital technologies have consequently become an inevitable and indispensable part of learning. To this effect, this study defines a hybrid educational model (HyFlex + Tec) used to enable virtual and in-person education in the HEIs. Practically, the study utilized data usage report from Massive Open Online Courses (MOOCs) and Emotions and Experience Survey questionnaire in a higher education setting for its experiments. To this end, we applied an Exponential Linear trend model and Forecasting method to determine overall progress and statistics for the learners during the Covid-19 pandemic, and subsequently performed a Text Mining and Univariate Analysis of Variance (ANOVA) to determine effects and significant differences that the teaching–learning experiences for the teachers and students have on their energy (learning motivation) levels. From the results, we note that the hybrid learning model supports continuity of education/learning for teachers and students during the Covid-19 pandemic. The study also discusses its innovative importance for future monitoring (tracking) of learning experiences and emotional well-being for the stakeholders in leu (aftermath) of the Covid-19 pandemic.

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Attribute-efficient learning of Boolean functions from Post closed classes

Discrete Mathematics and Applications ◽

10.1515/dma-2020-0025 ◽

2020 ◽

Vol 30 (5) ◽

pp. 285-301

Author(s):

Anastasiya V. Bistrigova

Keyword(s):

Boolean Functions ◽

Membership Queries ◽

Efficient Learning

AbstractWe consider exact attribute-efficient learning of functions from Post closed classes using membership queries and obtain bounds on learning complexity.

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Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map

Remote Sensing ◽

10.3390/rs12091418 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1418

Author(s):

Runmin Dong ◽

Cong Li ◽

Haohuan Fu ◽

Jie Wang ◽

Weijia Li ◽

...

Keyword(s):

Land Cover ◽

Training Data ◽

Training Dataset ◽

Land Cover Mapping ◽

Remotely Sensed Data ◽

Large Area ◽

National Scale ◽

Substantial Progress ◽

Efficient Learning ◽

Land Cover Maps

Substantial progress has been made in the field of large-area land cover mapping as the spatial resolution of remotely sensed data increases. However, a significant amount of human power is still required to label images for training and testing purposes, especially in high-resolution (e.g., 3-m) land cover mapping. In this research, we propose a solution that can produce 3-m resolution land cover maps on a national scale without human efforts being involved. First, using the public 10-m resolution land cover maps as an imperfect training dataset, we propose a deep learning based approach that can effectively transfer the existing knowledge. Then, we improve the efficiency of our method through a network pruning process for national-scale land cover mapping. Our proposed method can take the state-of-the-art 10-m resolution land cover maps (with an accuracy of 81.24% for China) as the training data, enable a transferred learning process that can produce 3-m resolution land cover maps, and further improve the overall accuracy (OA) to 86.34% for China. We present detailed results obtained over three mega cities in China, to demonstrate the effectiveness of our proposed approach for 3-m resolution large-area land cover mapping.

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Selection for Reinforcement-Free Learning Ability as an Organizing Factor in the Evolution of Cognition

Advances in Artificial Intelligence ◽

10.1155/2013/841646 ◽

2013 ◽

Vol 2013 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Solvi Arnold ◽

Reiji Suzuki ◽

Takaya Arita

Keyword(s):

Selection Pressure ◽

Learning Ability ◽

Environmental Complexity ◽

Baldwin Effect ◽

Neural Structure ◽

Neural Organization ◽

Environmental Structure ◽

Selection For ◽

Efficient Learning ◽

Complexity Thesis

This research explores the relation between environmental structure and neurocognitive structure. We hypothesize that selection pressure on abilities for efficient learning (especially in settings with limited or no reward information) translates into selection pressure on correspondence relations between neurocognitive and environmental structure, since such correspondence allows for simple changes in the environment to be handled with simple learning updates in neurocognitive structure. We present a model in which a simple form of reinforcement-free learning is evolved in neural networks using neuromodulation and analyze the effect this selection for learning ability has on the virtual species' neural organization. We find a higher degree of organization than in a control population evolved without learning ability and discuss the relation between the observed neural structure and the environmental structure. We discuss our findings in the context of the environmental complexity thesis, the Baldwin effect, and other interactions between adaptation processes.

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A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

ACM Transactions on Evolutionary Learning and Optimization ◽

10.1145/3453141 ◽

2021 ◽

Vol 1 (2) ◽

pp. 1-23

Author(s):

Arkadiy Dushatskiy ◽

Tanja Alderliesten ◽

Peter A. N. Bosman

Keyword(s):

Boolean Functions ◽

Surrogate Model ◽

Optimization Problems ◽

State Of The Art ◽

Fitness Function ◽

Optimal Solution ◽

Problem Structure ◽

Novel Approach ◽

Efficient Learning ◽

Nk Landscapes

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.

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