Persuasion-based recommender system ensambling matrix factorisation and active learning models

Ecological research like wildlife censuses increasingly relies on data on the scale of Terabytes. For example, modern camera trap datasets contain millions of images that require prohibitive amounts of manual labour to be annotated with species, bounding boxes, and the like. Machine learning, especially deep learning [3], could greatly accelerate this task through automated predictions, but involves expansive coding and expert knowledge.In this abstract we present AIDE, the Annotation Interface for Data-driven Ecology [2]. In a first instance, AIDE is a web-based annotation suite for image labelling with support for concurrent access and scalability, up to the cloud. In a second instance, it tightly integrates deep learning models into the annotation process through active learning [7], where models learn from user-provided labels and in turn select the most relevant images for review from the large pool of unlabelled ones (Fig. 1). The result is a system where users only need to label what is required, which saves time and decreases errors due to fatigue.<img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.0402be60f60062057601161/sdaolpUECMynit/12UGE&app=m&a=0&c=131251398e575ac9974634bd0861fadc&ct=x&pn=gnp.elif&d=1" alt="">Fig. 1: AIDE offers concurrent web image labelling support and uses annotations and deep learning models in an active learning loop.AIDE includes a comprehensive set of built-in models, such as ResNet [1] for image classification, Faster R-CNN [5] and RetinaNet [4] for object detection, and U-Net [6] for semantic segmentation. All models can be customised and used without having to write a single line of code. Furthermore, AIDE accepts any third-party model with minimal implementation requirements. To complete the package, AIDE offers both user annotation and model prediction evaluation, access control, customisable model training, and more, all through the web browser.AIDE is fully open source and available under https://github.com/microsoft/aerial_wildlife_detection.&#160;References

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Motivational Active Learning in Blended and Virtual Learning Scenarios

Online Course Management ◽

10.4018/978-1-5225-5472-1.ch047 ◽

2018 ◽

pp. 935-957

Author(s):

Johanna Pirker ◽

Maria Riffnaller-Schiefer ◽

Lisa Maria Tomes ◽

Christian Gütl

Keyword(s):

Active Learning ◽

Learning Strategies ◽

Relevant Literature ◽

Virtual Learning ◽

Learning Approaches ◽

Learning Models ◽

Engagement Strategies ◽

Active And Collaborative Learning ◽

Learning Success ◽

Learning Scenarios

The way people learn has changed over the last years. New pedagogical theories show that engaging and active learning approaches are particularly successful in improving conceptual understanding and enhancing the students' learning success and motivation. The Motivational Active Learning approach combines engagement strategies based on active and collaborative learning models with gamification. While many active learning models rely on in-class setups and active and personal interactions between students and between instructors, MAL was designed to integrate active learning in different settings. Our research project focuses on enhanced learning strategies with MAL in different computer-supported scenarios. This chapter outlines the potential of the pedagogical model MAL (Motivational Active Learning) in the context of blended and virtual learning scenarios; it also summarizes relevant literature and discusses implications and future work.

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Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation

Sensors ◽

10.3390/s20071932 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1932

Author(s):

Ramyar Saeedi ◽

Keyvan Sasani ◽

Assefaw H. Gebremedhin

Keyword(s):

Machine Learning ◽

Active Learning ◽

Mobile Health ◽

Health Monitoring ◽

Learning Models ◽

Healthcare Applications ◽

User Data ◽

Adaptation Cost ◽

Mobile Health Monitoring ◽

Machine Learning Models

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.

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A Comparative Analysis of Convergence Rate for Imbalanced Datasets of Active Learning Models

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) ◽

10.1109/icdsp.2018.8631877 ◽

2018 ◽

Author(s):

Haoke Zhang ◽

Wanqing Wu ◽

Sandeep Pirbhulal Guanglin Li ◽

Hongyi Zhang

Keyword(s):

Comparative Analysis ◽

Active Learning ◽

Convergence Rate ◽

Learning Models ◽

Imbalanced Datasets

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Active learning for the power factor prediction in diamond-like thermoelectric materials

npj Computational Materials ◽

10.1038/s41524-020-00439-8 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Ye Sheng ◽

Yasong Wu ◽

Jiong Yang ◽

Wencong Lu ◽

Pierre Villars ◽

...

Keyword(s):

Machine Learning ◽

Active Learning ◽

Transport Properties ◽

Gradient Boosting ◽

Learning Models ◽

Material Development ◽

Materials Genome ◽

P Type ◽

Type Power ◽

Machine Learning Models

Abstract The Materials Genome Initiative requires the crossing of material calculations, machine learning, and experiments to accelerate the material development process. In recent years, data-based methods have been applied to the thermoelectric field, mostly on the transport properties. In this work, we combined data-driven machine learning and first-principles automated calculations into an active learning loop, in order to predict the p-type power factors (PFs) of diamond-like pnictides and chalcogenides. Our active learning loop contains two procedures (1) based on a high-throughput theoretical database, machine learning methods are employed to select potential candidates and (2) computational verification is applied to these candidates about their transport properties. The verification data will be added into the database to improve the extrapolation abilities of the machine learning models. Different strategies of selecting candidates have been tested, finally the Gradient Boosting Regression model of Query by Committee strategy has the highest extrapolation accuracy (the Pearson R = 0.95 on untrained systems). Based on the prediction from the machine learning models, binary pnictides, vacancy, and small atom-containing chalcogenides are predicted to have large PFs. The bonding analysis reveals that the alterations of anionic bonding networks due to small atoms are beneficial to the PFs in these compounds.

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Multi-Criteria Recommender Systems

International Journal of Mobile Computing and Multimedia Communications ◽

10.4018/ijmcmc.2017100102 ◽

2017 ◽

Vol 8 (4) ◽

pp. 20-48 ◽

Cited By ~ 1

Author(s):

Ferdaous Hdioud ◽

Bouchra Frikh ◽

Brahim Ouhbi ◽

Ismail Khalil

Keyword(s):

Decision Making ◽

Active Learning ◽

Collaborative Filtering ◽

Recommender Systems ◽

Recommender System ◽

Selection Method ◽

Item Selection ◽

Multi Criteria Decision Making ◽

Selection Approach ◽

Comprehensive Study

A Recommender System (RS) works much better for users when it has more information. In Collaborative Filtering, where users' preferences are expressed as ratings, the more ratings elicited, the more accurate the recommendations. New users present a big challenge for a RS, which has to providing content fitting their preferences. Generally speaking, such problems are tackled by applying Active Learning (AL) strategies that consist on a brief interview with the new user, during which she is asked to give feedback about a set selected items. This article presents a comprehensive study of the most important techniques used to handle this issue focusing on AL techniques. The authors then propose a novel item selection approach, based on Multi-Criteria ratings and a method of computing weights of criteria inspired by a multi-criteria decision making approach. This selection method is deployed to learn new users' profiles, to identify the reasons behind which items are deemed to be relevant compared to the rest items in the dataset.

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Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning

Chemical Science ◽

10.1039/c9sc00616h ◽

2019 ◽

Vol 10 (35) ◽

pp. 8154-8163 ◽

Cited By ~ 14

Author(s):

Yao Zhang ◽

Alpha A. Lee

Keyword(s):

Machine Learning ◽

Active Learning ◽

Supervised Learning ◽

Molecular Properties ◽

Learning Models ◽

Molecular Properties Prediction ◽

Design Experiments ◽

Machine Learning Models

We report a statistically principled method to quantify the uncertainty of machine learning models for molecular properties prediction. We show that this uncertainty estimate can be used to judiciously design experiments.

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Pembelajaran Terpadu dan Urgensinya dalam Pengembangan Karakter Anak Sekolah Dasar

Edukasi Lingua Sastra ◽

10.47637/elsa.v17i2.35 ◽

2019 ◽

Vol 17 (2) ◽

pp. 1-12

Author(s):

Elizar Elizar

Keyword(s):

Elementary School ◽

Active Learning ◽

Prospective Teachers ◽

Young Generation ◽

Integrated Learning ◽

Learning Models ◽

Learning Program ◽

Learning Evaluation ◽

Learning Programs

Integrated learning models is a learning that is associated with an increased ability and characters of student in elementary school. School as one of the means and facitating role in shaping the young generation and motivate the studens to give to become savvy genaration, independent, creative, and innovative.To anticipate the occurrence of inefficiency in implementing the learning program should be planned with the best. Prior to the implementation of a learning program conducted there are some things that must be considered, planning before setting up learning programs need, designing and organizing learning evaluation, is used as an effort to obtain information to assess the success of a program. One of some efforts that could be done to improve students’ skill, as prospective teachers, in implementing an active learning was that by implementing integrated learning. It covered modelling a lecturer as a model in implementing integrated learning in the class and it integratef active learning models in fragmented, connected, sequenced, shared, webbed, threaded, integrated, immersed, and networked

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