title: Unsupervised concept drift detection using a student–teacher approach

authors: Cerqueira, Vitor

abstract: Concept drift detection is a crucial task in data stream evolving environments. Most of state of the art approaches designed to tackle this problem monitor the loss of predictive models. However, this approach falls short in many real-world scenarios, where the true labels are not readily available to compute the loss. In this context, there is increasing attention to approaches that perform concept drift detection in an unsupervised manner, i.e., without access to the true labels after the model is deployed. We propose a novel approach to unsupervised concept drift detection based on a student-teacher learning paradigm. Essentially, we create an auxiliary model (student) to mimic the primary model’s behaviour (teacher). At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.

text: Unsupervised concept drift detection using a student–teacher approach Concept drift detection is a crucial task in data stream evolving environments. Most of state of the art approaches designed to tackle this problem monitor the loss of predictive models. However, this approach falls short in many real-world scenarios, where the true labels are not readily available to compute the loss. In this context, there is increasing attention to approaches that perform concept drift detection in an unsupervised manner, i.e., without access to the true labels after the model is deployed. We propose a novel approach to unsupervised concept drift detection based on a student-teacher learning paradigm. Essentially, we create an auxiliary model (student) to mimic the primary model’s behaviour (teacher). At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.

year: 2022-01-01

title: The dynamic adjustment of factor inputs and its policy implications for major wheat producing areas in China

authors: Chen, Shuzhang

subjects: China

abstract: China's major wheat producing areas play a crucial role in ensuring domestic grain production and food security more generally and it is therefore of significance, both empirically and theoretically, to investigate the current situation and future tendencies of the sector. Based on input- and output-oriented DEA models, overall technical efficiency was estimated for the sector, and both radial and slack adjustments were calculated. The course of the dynamic adjustments was identified and presented for factor inputs over the past decade. The results show that the radial adjustments have exhibited a decreasing trend, while structural, slack adjustments have practically disappeared. The course of the dynamic adjustments suggests that there has been a transformation from labor-intensive to land-intensive and capital-intensive operations which will continue to dominate China's wheat production sector. As a consequence, to optimize factor inputs and reduce radical and slack adjustments, it seems necessary that the major wheat producing areas reduce labor inputs; enhance land-intensive operations; and increase agricultural mechanization.

text: The dynamic adjustment of factor inputs and its policy implications for major wheat producing areas in China China's major wheat producing areas play a crucial role in ensuring domestic grain production and food security more generally and it is therefore of significance, both empirically and theoretically, to investigate the current situation and future tendencies of the sector. Based on input- and output-oriented DEA models, overall technical efficiency was estimated for the sector, and both radial and slack adjustments were calculated. The course of the dynamic adjustments was identified and presented for factor inputs over the past decade. The results show that the radial adjustments have exhibited a decreasing trend, while structural, slack adjustments have practically disappeared. The course of the dynamic adjustments suggests that there has been a transformation from labor-intensive to land-intensive and capital-intensive operations which will continue to dominate China's wheat production sector. As a consequence, to optimize factor inputs and reduce radical and slack adjustments, it seems necessary that the major wheat producing areas reduce labor inputs; enhance land-intensive operations; and increase agricultural mechanization.

year: 2013

title: Indigenous psychologies, fourth world peoples and the international literature: Finding ourselves in online abstracting and indexing databases

authors: Nikora, Linda Waimarie

subjects: indigenous psychologies

abstract: The presence and visibility of indigenous people and experience in the field of indigenous psychologies is vital to articulating existence, life, self-determination and future making. This article highlights examples of indigenous Psychology being both absent and present at the same time in OAD (online academic databases).

text: Indigenous psychologies, fourth world peoples and the international literature: Finding ourselves in online abstracting and indexing databases The presence and visibility of indigenous people and experience in the field of indigenous psychologies is vital to articulating existence, life, self-determination and future making. This article highlights examples of indigenous Psychology being both absent and present at the same time in OAD (online academic databases).

year: 2016