Stream-based active learning with linear models

The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.Comment: Published in Knowledge-Based Systems (2022

Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders

Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routine operations. The regression models used by these sensors often require a large number of labeled examples, yet obtaining the label information can be very expensive given the high time and cost required by quality inspections. In this context, active learning methods can be highly beneficial as they can suggest the most informative labels to query. However, most of the active learning strategies proposed for regression focus on the offline setting. In this work, we adapt some of these approaches to the stream-based scenario and show how they can be used to select the most informative data points. We also demonstrate how to use a semi-supervised architecture based on orthogonal autoencoders to learn salient features in a lower dimensional space. The Tennessee Eastman Process is used to compare the predictive performance of the proposed approaches.Comment: ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in the Real Worl

Can Students' Attitudes and Behaviors be Changed by Educational Interventions? A Comparative Case Study

This study examined engineering students’ attitudes and behaviors in a first-year Calculus course. Not  surprisingly, High School mathematics and physics grades correlated closely with self-reported Calculus grades, and a student survey conducted four years apart demonstrated almost identical attitudes and behaviors despite the introduction of a range of measures aimed to enhance learning. The better the grades, the fairer students deemed it to be, and the less of in-depth learning, the poorer the grades. The higher the ambitions, and the more active and hardworking, the better the grades. Academic success factors included an ability to keep pace with progression, and a commitment to advance learning. The minimal impact of interventions appears as surprising; however, this study brings perspectives to make sense of such data, also capable of producing greater future successes

Introducing Statistical Design of Experiments to SPARQL Endpoint Evaluation

This paper argues that the common practice of benchmarking is inadequate as a scientific evaluation methodology. It further attempts to introduce the empirical tradition of the physical sciences by using techniques from Statistical Design of Experiments applied to the example of SPARQL endpoint performance evaluation. It does so by studying full as well as fractional factorial experiments designed to evaluate an assertion that some change introduced in a system has improved performance. This paper does not present a finished experimental design, rather its main focus is didactical, to shift the focus of the community away from benchmarking towards higher scientific rigor. The Semantic Web – ISWC 2013. Lecture Notes in Computer Science Volume 8219, 2013, pp 360-375. The final publication is available at Springe

Projection in 12-run Plackett and Burman

In this article we prove the form of the projections in the 12-run Plackett and Burman design. We do this by exploiting the close relationship between Hadamard matrices, orthogonal two-level arrays and a special type of balanced incomplete block designs. Projections into 2-5 dimensions are treated