Query: improving active and passive learning

Candidate papers:

abstract: Inferring behavioral models (e.g. state machines) of software systems is an important element of re engineering activities. Model inference techniques can be categorized as active or passive learning, constructing models by (dynamically) interacting with systems or (statically) analyzing traces, respectively. Application of those techniques in the industry is, however, hindered by the trade off between learning time and completeness achieved (active learning) or by incomplete input logs (passive learning) We investigate the learning time/completeness achieved trade off of active learning with a pilot study at ASML, provider of lithography systems for the semiconductor industry. To resolve the trade off we advocate extending active learning with execution logs and passive learning results.We apply the extended approach to eighteen components used in ASML TWINSCAN lithography machines. Compared to traditional active learning, our approach significantly reduces the active learning time. Moreover, it is capable of learning the behavior missed by the traditional active learning approach.
author_names: Nan Yang

Kousar Aslam

Ramon R H Schiffelers

Leonard Lensink

Dennis Hendriks

Loek G Cleophas

Alexander Serebrenik
title: Improving Model Inference in Industry by Combining Active and Passive Learning
venue: 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)
year: 2019

author_names: Dawn McKinney

Alex D Edgcomb

Roman L Lysecky

Frank Vahid
title: Improving Pass Rates by Switching from a Passive to an Active Learning Textbook in CS0
year: 2020

abstract: This study aims to investigate the effect of the collaborative learning on improving the students' potential when dealing with the active voice and the passive voice in English. To achieve this aim, the researchers conducted this study on the eighth graders in Qalqilia district in Palestine. The treatment was applied to 50 male and female students who were randomly selected from all the district schools. The researchers divided the sample into two groups of 25 students each (a control group and an experimental group) The groups were given the same material about the passive voice and the active voice. The students in the control group were taught the use of the passive voice and the active voice through the traditional method while the students in the experimental group were taught the use of the passive voice and the active voice through the collaborative learning method. The researchers illustrated the collaborative learning principles to the teacher of the experimental group. For details see Appendix III, P23 24. During the second semester 2006 2007 and before applying the collaborative method, the researchers gave the two groups the same test to make sure that they were equivalent. They administered the same test to the two groups after applying the collaborative method. The findings of the study showed that there was a significant difference between the experimental and the control groups in favor of the experimental group, which means that the collaborative learning strategy was more effective in improving students' learning of the active and passive voice. Based on these findings, the researchers recommended that teachers be advised to adopt the collaborative learning strategy that focuses on the learner who is considered as the foundation stone of the whole teaching and learning process.
author_names: Ahmed Awad

Khalaf Al-Makhzoomi
title: The Effect of Collaborative Strategy on Improving Students' Potentials in learning Active Voice and Passive Voice in English
year: 2009

abstract: Context aware learning spaces (CALSs) utilise resources of the surrounding context in the learning process. UFractions is a CALS combining a storytelling game and fraction rods for mathematics education, and it was developed for the South African context and later taken to Finland. We divide technology integration into active and passive integration according to the role of technology in the process. In passive integration the technology is integrated into the CALS so that it does not disturb the learner and the context. In active integration the technology integrates resources into the CALS and makes the system adaptive to contextual changes. We analysed, by a mixed method approach, the need for active and passive integration in UFractions in Finland and South Africa. We identified sixteen disturbance factors which had negative effects on the users of UFractions. The results indicate that by improving active and passive integration in UFractions, disturbance factors can be diminished.
author_names: Teemu Henrikki Laine

Erkki Sutinen

Mike S Joy

Eeva Nygren
title: Active and passive technology integration in context aware learning spaces
year: 2011

abstract: Abstract Recreation students lack some skills and competencies at the beginning of their careers. This may be related to passive learning settings with limited or no exposure to practical experiences during training. To gain the required competencies, students need to be exposed to active learning processes. The focus of this study was to establish if an experiential learning teaching approach, implemented in a recreation degree programme, could develop students' graduate attributes. A holistic single case, case study design employing a convergent parallel mixed method pre post test design was used. An adapted Twin Cycle Experiential Learning Model (TCELM) was applied in a final year recreation module at a higher education institution. The Review of Personal Effectiveness and Locus of Control (ROPELOC) questionnaire and a self report competency assessment survey were completed by 28 students at the beginning and end of the semester. Qualitative data included student reflections and focus group interviews. Data were analysed using descriptive statistics and inductive coding. The ROPELOC revealed statistical significant improvement in the student's leadership skills (p 0.04) and their overall effectiveness (p 0.01) Research skills (p 0.00) adaptability (p 0.00) leadership skills (p 0.00) personal qualities (p 0.02) and knowledge of the profession (p 0.00) were competencies that increased significantly over the semester. From the qualitative data several categories, linked to graduate attributes required by recreation professionals, emerged. Knowledge of the profession was the most mentioned category. These results support the view that an experiential learning teaching approach is beneficial in the preparation of recreation graduates, and should form the teaching foundation for recreation in higher education.
author_names: Cornelia M Schreck

Johannes Theron Weilbach

Gerda Marie Reitsma
title: Improving graduate attributes by implementing an experiential learning teaching approach: A case study in recreation education
year: 2020

abstract: Modern humanoid robots include not only active compliance but also passive compliance. Apart from improved safety and dependability, availability of passive elements, such as springs, opens up new possibilities for improving the energy efficiency. With this in mind, this paper addresses the challenging open problem of exploiting the passive compliance for the purpose of energy efficient humanoid walking. To this end, we develop a method comprising two parts: an optimization part that finds an optimal vertical center of mass trajectory, and a walking pattern generator part that uses this trajectory to produce a dynamically balanced gait. For the optimization part, we propose a reinforcement learning approach that dynamically evolves the policy parametrization during the learning process. By gradually increasing the representational power of the policy parametrization, it manages to find better policies in a faster and computationally efficient way. For the walking generator part, we develop a variable center of mass height ZMP based bipedal walking pattern generator. The method is tested in real world experiments with the bipedal robot COMAN and achieves a significant 18% reduction in the electric energy consumption by learning to efficiently use the passive compliance of the robot.
author_names: Petar Kormushev

Barkan Ugurlu

Darwin Gordon Caldwell

Nikolaos G Tsagarakis
title: Learning to exploit passive compliance for energy efficient gait generation on a compliant humanoid
venue: Auton. Robots
year: 2019

abstract: In India, traditional learning approaches in universities follow passive learning and instruction based studies. The demand for evidence based instructional and interactive active learning processes increases with the advancement in technology. To improve the quality of teaching and student performance, Laureate International University network has taken a unique initiative by applying an iterative and evidence based active learning process in small groups at the University of Petroleum and Energy Studies, Dehradun for the Postgraduate Certificate in Academic Practice program. In this study, active learning processes with digital devices (mobile devices) and digital technologies (modeling tools, simulation, and online resources) having virtual small to medium strength classrooms are integrated into various scenarios with different levels of education. This has been found to be useful in improving student performance during the COVID 19 pandemic. The proposed process applies both traditional and active learning processes with the provision to use mobile devices for digital content. This process also involves teachers attending a Bootcamp, over a 2 month period, consisting of four modules in which they learn about and use digital content that will then be applied in their courses. Results show that the active learning process is of great benefit to students over traditional learning, and it provides a 30% improvement in student's grades. Further, it is observed that long term learning average marks increase by 66.9% in two engineering subjects. The simulation based experimentations are conducted to engage students and faculty members in active learning and simulation learning processes. It shows that the proposed approach (active learning) improves students' learning abilities as compared to the traditional approach.
author_names: Ravi Singhal

Adarsh Kumar

Harvinder Singh

Stephanie Fuller

Sukhpal Singh Gill
title: Digital device based active learning approach using virtual community classroom during the COVID 19 pandemic
year: 2020

abstract: For the problem that large scale labeled samples are not easy to acquire in the course of Support Vector Machines (SVMs) training, a Semi Supervised Active Learning Algorithm for SVMs (QTB ASVM) is proposed in the paper, which efficiently combines the semi supervised learning based on Tri Training and active learning based on Query By Committee (QBC) with SVMs. With this method, QBC active learning is used to select the samples which are the most valuable to current SVM classifier, and Tri Training is used to exploit useful information that remains in the unlabeled samples. The experimental results show that the proposed approach can considerably reduce the labeled samples and costs compared to the SVMs which is either not applied with semi supervised learning or active learning or applied with only one of them, and at the same time it can ensure that the accurate classification performance is kept as the passive SVM, while improving generalization performance and also expediting the SVM training.
author_names: Hailong Xu

Longyue Li

Pengsong Guo
title: Semi supervised active learning algorithm for SVMs based on QBC and tri training
venue: J. Ambient Intell. Humaniz. Comput.
year: 2021

abstract: Microsurgical procedures, such as petroclival meningioma resection, require careful surgical actions in order to remove tumor tissue, while avoiding brain and vessel damaging. Such procedures are currently performed under microscope magnification. Robotic tools are emerging in order to filter surgeons' unintended movements and prevent tools from entering forbidden regions such as vascular structures. The present work investigates the use of a handheld robotic tool (Micron) to automate vessel avoidance in microsurgery. In particular, we focused on vessel segmentation, implementing a deep learning based segmentation strategy in microscopy images, and its integration with a feature based passive 3D reconstruction algorithm to obtain accurate and robust vessel position. We then implemented a virtual fixture based strategy to control the handheld robotic tool and perform vessel avoidance. Clay vascular phantoms, lying on a background obtained from microscopy images recorded during petroclival meningioma surgery, were used for testing the segmentation and control algorithms. When testing the segmentation algorithm on 100 different phantom images, a median Dice similarity coefficient equal to 0.96 was achieved. A set of 25 Micron trials of 80 s in duration, each involving the interaction of Micron with a different vascular phantom, were recorded, with a safety distance equal to 2 mm, which was comparable to the median vessel diameter. Micron's tip entered the forbidden region 24% of the time when the control algorithm was active. However, the median penetration depth was 16.9 mm, which was two orders of magnitude lower than median vessel diameter. Results suggest the system can assist surgeons in performing safe vessel avoidance during neurosurgical procedures.
author_names: Sara Moccia

Simone Foti

Arpita Routray

Francesca Prudente

Alessandro Perin

Raymond F Sekula

Leonardo S Mattos

Jeffrey R Balzer

Wendy K Fellows-Mayle

Elena De Momi

Cameron N Riviere
title: Toward Improving Safety in Neurosurgery with an Active Handheld Instrument
venue: Annals of Biomedical Engineering
year: 2018

abstract: Developing high throughput and high performance phenotyping algorithms is critical to the secondary use of electronic health records for clinical research. Supervised machine learning based methods have shown good performance, but often require large annotated datasets that are costly to build. Simulation studies have shown that active learning (AL) could reduce the number of annotated samples while improving the model performance when assuming that the time of labeling each sample is the same (i.e. cost insensitive) In this study, we proposed a cost sensitive AL (CostAL) algorithm for clinical phenotyping, using the identification of breast cancer patients as a use case. CostAL implements a linear regression model to estimate the actual time required for annotating each individual sample. We recruited two annotators to manual review medical records of 766 potential breast cancer patients and recorded the actual time of annotating each sample. We then compared CostAL, AL, and passive learning (PL, aka random sampling) using this annotated dataset and generated learning curves for each method. Our experimental results showed that CostAL achieved the highest area under the curve (AUC) score among the three algorithms (PL, AL, and CostAL are 0.784, 0.8501, and 0.8673 for user 1 and 0.8006, 0.8806 and 0.9006 for user 2) To achieve an accuracy of 0.94, AL and CostAL could save 36% and 60% annotation time for user 1 and 53% and 70% annotation time for user 2, when they were compared with PL, indicating the value of cost sensitive AL approaches.
author_names: Zongcheng Ji

Qiang Wei

Amy Franklin

Trevor A Cohen

Hua Xu
title: Cost sensitive Active Learning for Phenotyping of Electronic Health Records.
venue: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
year: 2019