title: Mini-batching with fused training and testing for data streams processing on the Edge

authors: Luna, R

subjects: computer science

abstract: Edge Computing (EC) has emerged as a solution to reduce energy demand and greenhouse gas emissions from digital technologies. EC supports low latency, mobility, and location awareness for delay-sensitive applications by bridging the gap between cloud computing services and end-users. Machine learning (ML) methods have been applied in EC for data classification and information processing. Ensemble learners have often proven to yield high predictive performance on data stream classification problems. Mini-batching is a technique proposed for improving cache reuse in multi-core architectures of bagging ensembles for the classification of online data streams, which benefits application speedup and reduces energy consumption. However, the original mini-batching presents limited benefits in terms of cache reuse and it hinders the accuracy of the ensembles (i.e., their capacity to detect behavior changes in data streams). In this paper, we improve mini-batching by fusing continuous training and test loops for the classification of data streams. We evaluated the new strategy by comparing its performance and energy efficiency with the original mini-batching for data stream classification using six ensemble algorithms and four benchmark datasets. We also compare mini-batching strategies with two hardware-based strategies supported by commodity multi-core processors commonly used in EC. Results show that mini-batching strategies can significantly reduce energy consumption in 95% of the experiments. Mini-batching improved energy efficiency by 96% on average and 169% in the best case. Likewise, our new mini-batching strategy improved energy efficiency by 136% on average and 456% in the best case. These strategies also support better control of the balance between performance, energy efficiency, and accuracy.

text: Mini-batching with fused training and testing for data streams processing on the Edge Edge Computing (EC) has emerged as a solution to reduce energy demand and greenhouse gas emissions from digital technologies. EC supports low latency, mobility, and location awareness for delay-sensitive applications by bridging the gap between cloud computing services and end-users. Machine learning (ML) methods have been applied in EC for data classification and information processing. Ensemble learners have often proven to yield high predictive performance on data stream classification problems. Mini-batching is a technique proposed for improving cache reuse in multi-core architectures of bagging ensembles for the classification of online data streams, which benefits application speedup and reduces energy consumption. However, the original mini-batching presents limited benefits in terms of cache reuse and it hinders the accuracy of the ensembles (i.e., their capacity to detect behavior changes in data streams). In this paper, we improve mini-batching by fusing continuous training and test loops for the classification of data streams. We evaluated the new strategy by comparing its performance and energy efficiency with the original mini-batching for data stream classification using six ensemble algorithms and four benchmark datasets. We also compare mini-batching strategies with two hardware-based strategies supported by commodity multi-core processors commonly used in EC. Results show that mini-batching strategies can significantly reduce energy consumption in 95% of the experiments. Mini-batching improved energy efficiency by 96% on average and 169% in the best case. Likewise, our new mini-batching strategy improved energy efficiency by 136% on average and 456% in the best case. These strategies also support better control of the balance between performance, energy efficiency, and accuracy.

year: 2024

title: The bounded inverse Weibull distribution: An extreme value alternative for application to environmental maxima?

authors: Bardsley, W. Earl

subjects: bounded extremes

abstract: There has long been interest in making inferences about future low-probability natural events that have magnitudes greater than any in the past record. Given a stationary time series, the unbounded Type 1 and Type 2 asymptotic extreme value distributions are often invoked as giving theoretical justification for extrapolating to large magnitudes and long return periods for hydrological variables such as rainfall and river discharge. However, there is a problem in that environmental extremes are bounded above by the bounded nature of their causal variables. Extrapolation using unbounded asymptotic models therefore cannot be justified from extreme value theory and at some point there will be over-prediction of future magnitudes. This creates the apparent contradiction, for example, of annual rainfall maxima being well approximated by Type 2 extreme value distributions despite the bounded nature of rainfall magnitudes. An alternative asymptotic extreme value approach is suggested for further investigation, with the model being the asymptotic distribution of minima (Weibull distribution) applied to block maxima reciprocals. Two examples are presented where data that are well matched by Type 1 or Type 2 extreme value distributions give reciprocals suggestive of lower bounds (upper bound γ to the original data). The asymptotic model here is a 3-parameter Weibull distribution for the reciprocals, with positive location parameter γ−1. When this situation is demonstrated from data, parameter estimation can be carried out with respect to the distribution of reciprocals of 3-parameter Weibull random variables. This distribution is referenced here as the bounded inverse Weibull distribution. A maximum likelihood parameter estimation methodology is presented, together with a parametric bootstrap approach for obtaining one-sided upper confidence limits to γ. When data permits estimation of γ, the bounded inverse Weibull distribution is suggested as an improved alternative to Type 1 or Type 2 extreme value distributions because the upper bound reality is recognised. However, extensive application to many data sets is required to evaluate the practical utility of the bounded approach for extrapolating beyond the largest recorded event.

text: The bounded inverse Weibull distribution: An extreme value alternative for application to environmental maxima? There has long been interest in making inferences about future low-probability natural events that have magnitudes greater than any in the past record. Given a stationary time series, the unbounded Type 1 and Type 2 asymptotic extreme value distributions are often invoked as giving theoretical justification for extrapolating to large magnitudes and long return periods for hydrological variables such as rainfall and river discharge. However, there is a problem in that environmental extremes are bounded above by the bounded nature of their causal variables. Extrapolation using unbounded asymptotic models therefore cannot be justified from extreme value theory and at some point there will be over-prediction of future magnitudes. This creates the apparent contradiction, for example, of annual rainfall maxima being well approximated by Type 2 extreme value distributions despite the bounded nature of rainfall magnitudes. An alternative asymptotic extreme value approach is suggested for further investigation, with the model being the asymptotic distribution of minima (Weibull distribution) applied to block maxima reciprocals. Two examples are presented where data that are well matched by Type 1 or Type 2 extreme value distributions give reciprocals suggestive of lower bounds (upper bound γ to the original data). The asymptotic model here is a 3-parameter Weibull distribution for the reciprocals, with positive location parameter γ−1. When this situation is demonstrated from data, parameter estimation can be carried out with respect to the distribution of reciprocals of 3-parameter Weibull random variables. This distribution is referenced here as the bounded inverse Weibull distribution. A maximum likelihood parameter estimation methodology is presented, together with a parametric bootstrap approach for obtaining one-sided upper confidence limits to γ. When data permits estimation of γ, the bounded inverse Weibull distribution is suggested as an improved alternative to Type 1 or Type 2 extreme value distributions because the upper bound reality is recognised. However, extensive application to many data sets is required to evaluate the practical utility of the bounded approach for extrapolating beyond the largest recorded event.

year: 2021

title: Activating whanau (extended family) processes within a community and school literacy partnership

authors: Glynn, Ted

subjects: Maori Education

abstract: "The paper describes the positive outcomes of a home and school literacy partnership in one mainstream primary school where the principal and the large majority of students, teachers, and whānau belong to one Māori hapū (sub-tribe), Ngāti Whakaue. Participating students were identified by teachers as experiencing difficulties with reading and writing. They were assigned randomly to one of two conditions: (1) participating in their school’s regular reading programme (school group) or (2) participating also in the home tutored reading and writing programme (home and school group).

Students in the home and school group improved their reading and writing outcomes over and above the outcomes achieved by students participating only in the regular school programme. There were two key reasons for the success of this partnership. The first was parents and whānau members successful implementation of both a Māori language reading tutoring programme, Tatari, Tautoko Tauawhi, and the two components of the writing programme, Tuhi Atu Tui Mai and Whakaputa Whakaaro. The second was the professional educational and cultural expertise of the home and school liaison worker, Hiro. Because of her language and cultural expertise and her mana (acknowledged authority and standing), Hiro was able to engage Māori parents and whānau actively in the home and school project. The co-constructed narrative between Hiro and Mere (the second author) provided a salient context for understanding the power of culturally preferred pedagogy in establishing effective home and school partnerships."

text: Activating whanau (extended family) processes within a community and school literacy partnership "The paper describes the positive outcomes of a home and school literacy partnership in one mainstream primary school where the principal and the large majority of students, teachers, and whānau belong to one Māori hapū (sub-tribe), Ngāti Whakaue. Participating students were identified by teachers as experiencing difficulties with reading and writing. They were assigned randomly to one of two conditions: (1) participating in their school’s regular reading programme (school group) or (2) participating also in the home tutored reading and writing programme (home and school group).

Students in the home and school group improved their reading and writing outcomes over and above the outcomes achieved by students participating only in the regular school programme. There were two key reasons for the success of this partnership. The first was parents and whānau members successful implementation of both a Māori language reading tutoring programme, Tatari, Tautoko Tauawhi, and the two components of the writing programme, Tuhi Atu Tui Mai and Whakaputa Whakaaro. The second was the professional educational and cultural expertise of the home and school liaison worker, Hiro. Because of her language and cultural expertise and her mana (acknowledged authority and standing), Hiro was able to engage Māori parents and whānau actively in the home and school project. The co-constructed narrative between Hiro and Mere (the second author) provided a salient context for understanding the power of culturally preferred pedagogy in establishing effective home and school partnerships."

year: 2004