Query: combining 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

abstract: We propose a hybrid formulation of Turing Learning and study its application in mobile robotics. Instead of using a single type of discriminator, in the hybrid formulation, both active and passive discriminators are used. Active discriminators come to their judgments while interacting with the system under investigation, which helps improve model accuracy. Passive discriminators come to their judgments while only observing the system, allowing the reuse of data samples, which for real robots would be costly to obtain. To validate these ideas, we present a case study where a simulated embodied robot is required to calibrate its distance sensor through a process of self modeling, and without metric information of where it resides within the environment. The results show that the hybrid formulation achieves a good level of accuracy with significantly fewer data samples from the robot. The findings suggest that the self modeling process could be realized on a mobile physical robot with a limited time and energy budget.
author_names: Yue Gu

Wei Li

Roderich Gross
title: Turing learning with hybrid discriminators: combining the best of active and passive learning
venue: GECCO Companion
year: 2020

abstract: Active learning techniques are generally considered to be any classroom activities in which students become an active participant in learning, instead of a passive listener to a lecture. Service learning activities are generally experiential (real life) and reflective problem based learning activities in which students enrolled in an academic course provide a needed service to a community partner. In the fall semester of 2016, one section of a calculus based Physics I course included both active learning and service learning components. One of the several active learning activities used in this course was that demonstrations, traditionally performed by the instructor, were performed by the students for an audience of seventh and eighth graders. Prior to the presentations, the groups of students met with the instructor to learn and practice the demonstrations. After perfecting their presentations, the students invited seventh and eighth graders from an inner city school to a "Day of Science," where they presented the demonstrations for part of the day. This paper reports on the active learning and service learning activities involved in the "Day of Science" and analyzes the results.Active learning techniques are generally considered to be any classroom activities in which students become an active participant in learning, instead of a passive listener to a lecture. Service learning activities are generally experiential (real life) and reflective problem based learning activities in which students enrolled in an academic course provide a needed service to a community partner. In the fall semester of 2016, one section of a calculus based Physics I course included both active learning and service learning components. One of the several active learning activities used in this course was that demonstrations, traditionally performed by the instructor, were performed by the students for an audience of seventh and eighth graders. Prior to the presentations, the groups of students met with the instructor to learn and practice the demonstrations. After perfecting their presentations, the students invited seventh and eighth graders from an inner city school to a "Day of Science," where they
author_names: Joseph Jude Trout

Casey Murphy

Morgan Vukicevich
title: Combining Active Learning Techniques and Service Learning in a Section of Physics I with Calculus Course
venue: The Physics Teacher
year: 2019

abstract: We study the problem of combining active learning suggestions to identify informative training examples by empirically comparing methods on benchmark datasets. Many active learning heuristics for classification problems have been proposed to help us pick which instance to annotate next. But what is the optimal heuristic for a particular source of data? Motivated by the success of methods that combine predictors, we combine active learners with bandit algorithms and rank aggregation methods. We demonstrate that a combination of active learners outperforms passive learning in large benchmark datasets and removes the need to pick a particular active learner a priori. We discuss challenges to finding good rewards for bandit approaches and show that rank aggregation performs well.
author_names: Alasdair Tran

Cheng Soon Ong

Christian Wolf
title: Combining active learning suggestions
venue: PeerJ Comput. Sci.
year: 2018

abstract: Technology integration is the process of overcoming different barriers that hinder efficient utilisation of learning technologies. The authors divide technology integration into two components based on technology's role in the integration process. In active integration, the technology integrates learning resources into a learning space, making it adaptive to the changes in the context. In passive integration, the technology is integrated into a learning space so that it does not disturb the learner and the context. Using this division, the authors investigate technology integration in context aware learning spaces (CALSs) which emphasise context sensitiveness and utilise surrounding resources. UFractions is a CALS combining a mobile based story and fraction rods. The authors analyse active and passive integration in UFractions among 305 middle schools pupils in South Africa, Finland and Mozambique. In the analysis the authors support quantitative questionnaire data with qualitative insights from questionnaires, interviews and observations. The findings indicate that technology integration, particularly active integration, in UFractions is incomplete. The concepts of active and passive integration are necessary in order to manage technology's influence on learning experiences in CALSs. Active and passive integration can also be helpful in CALS design, deployment and evaluation, and they could be applied in other learning technology scenarios in the future.
author_names: Teemu Henrikki Laine

Eeva Nygren
title: Active and passive technology integration: a novel approach for managing technology's influence on learning experiences in context aware learning spaces
year: 2016

abstract: Adults can learn to hear the phonetic distinctions of another language with practice. It is commonly assumed that such improvements require active performance of the target task throughout training. However, we have seen that, for other perceptual tasks, similar performance gains can be achieved by replacing some active performance trials with passive stimulus exposures. Here we examined the learning of a non native phonetic contrast using several training protocols that varied in the proportion of active and passive trials during the practice period. Native, monolingual English speakers were trained to identify a Thai phonetic contrast along a voice onset time continuum. A group given half active training and half passive exposures showed as much improvement in the category boundary as a group given active training for the entire practice period. The active passive group out performed a group given half active training and half training on an unrelated task with no passive exposures. Finally, a group giv.
author_names: Nicole L Marrone

Beverly Wright
title: Improved learning of a non native phonetic contrast by combining active task performance with passive stimulus exposures.
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: Online multi label learning is an efficient classification paradigm in machine learning. However, traditional online multi label methods often need requesting all class labels of each incoming sample, which is often human cost and time consuming in labeling classification problem. In order to tackle these problems, in this paper, we present online multi label passive aggressive active (MLPAA) learning algorithm by combining binary relevance (BR) decomposition strategy with online passive aggressive active (PAA) method. The proposed MLPAA algorithm not only uses the misclassified labels to update the classifier, but also exploits correctly classified examples with low prediction confidence. We perform extensive experimental comparison for our algorithm and the other methods using nine benchmark data sets. The encouraging results of our experiments validate the effectiveness of our proposed method.
author_names: Xizhi Guo

Yongwei Zhang

Jianhua Xu
title: Online Multi label Passive Aggressive Active Learning Algorithm Based on Binary Relevance
venue: ICONIP
year: 2017

abstract: Physical Unclonable Functions (PUFs) have emerged as a lightweight alternative to traditional cryptography. The fact that no secret key needs to be stored in non volatile memory makes PUFs especially well suited for embedded systems in which securely generating and storing secret keys is difficult and expensive. Compared to traditional cryptography, PUFs are often believed to be more resistant to implementation attacks. In this paper we will take a closer look at this assumption. Using a controlled Arbiter PUF as an example, we show that just like traditional cryptography strong PUFs are susceptible to implementation attacks. By combining machine learning with with sidechannel analysis we are able to attack designed based on Arbiter PUFs that on are resistant to normal machine learning attacks. We use two different side channels for our attacks: a passive power side channel and an active fault attack based on altering the supply voltage of the controlled PUF. Even in the presence of considerable noise both attacks can accurately model the Controlled Arbiter PUF. Hence, the assumption that PUFs are generally more resistant against side channel attacks is not necessarily true and side channel resistance needs to be considered when PUF designs are evaluated.
author_names: Georg T Becker

Raghavan Kumar
title: Active and Passive Side Channel Attacks on Delay Based PUF Designs
venue: IACR Cryptol. ePrint Arch.
year: 2014

abstract: Recent research suggests that combining adaptive learning algorithms with perceptual learning (PL) methods can accelerate perceptual classification learning in complex domains (e.g. Mettler Kellman, 2014) We hypothesized that passive presentation of category exemplars might act synergistically with active adaptive learning to further enhance PL. Passive presentation and active adaptive methods were applied to PL and transfer in a complex real world domain. Undergraduates learned to interpret real electrocardiogram (ECG) tracings by either: (1) making active classifications and receiving feedback, (2) studying passive presentations of correct classifications, or (3) learning with a combination of initial passive presentations followed by active classification. All conditions showed strong transfer to novel ECGs at posttest and after a one week delay. Most notably, the combined passive active condition proved the most effective, efficient, and enjoyable. These results help illuminate the processes by which PL advances and have direct implications for perceptual and adaptive learning technology.
author_names: K P Thai

Sally Krasne

Philip J Kellman
title: Adaptive Perceptual Learning in Electrocardiography: The Synergy of Passive and Active Classification
venue: CogSci
year: 2015