title: Balancing performance and energy consumption of bagging ensembles for the classification of data streams in edge computing

authors: Cassales, Guilherme

subjects: computer science

abstract: In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.

text: Balancing performance and energy consumption of bagging ensembles for the classification of data streams in edge computing In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.

year: 2022

title: Metabarcoding inventory of an arctic tundra soil ecosystem reveals highly heterogeneous communities at a small scale

authors: Jorna, Jesse

abstract: Biodiversity surveys of Arctic soil ecosystems are limited. Here, we provide a sequence-based inventory of soil fauna from an Arctic tundra ecosystem near the Canadian High Arctic Research Station in Cambridge Bay, Nunavut. Invertebrate communities were extracted at a vegetated and non-vegetated site in three replicates and inventoried using 18S metabarcode sequencing. A total of 361 amplicon sequence variants (ASV) were identified and assigned to the closest matching taxonomic orders, most of which belonged to the Nematoda and Arthropoda. Vegetated soils showed no significantly higher ASV richness relative to non-vegetated soils although they contained a significantly higher diversity of arthropod taxa including insects, mites, and springtails. Most taxa were found only at a single location and even samples from the same site displayed distinct communities, suggesting that belowground species richness in Arctic tundra habitats is highly heterogeneous. Preserving soil biodiversity in a changing Arctic is essential for Inuit communities who rely on intact tundra ecosystems for their health and wellbeing.

text: Metabarcoding inventory of an arctic tundra soil ecosystem reveals highly heterogeneous communities at a small scale Biodiversity surveys of Arctic soil ecosystems are limited. Here, we provide a sequence-based inventory of soil fauna from an Arctic tundra ecosystem near the Canadian High Arctic Research Station in Cambridge Bay, Nunavut. Invertebrate communities were extracted at a vegetated and non-vegetated site in three replicates and inventoried using 18S metabarcode sequencing. A total of 361 amplicon sequence variants (ASV) were identified and assigned to the closest matching taxonomic orders, most of which belonged to the Nematoda and Arthropoda. Vegetated soils showed no significantly higher ASV richness relative to non-vegetated soils although they contained a significantly higher diversity of arthropod taxa including insects, mites, and springtails. Most taxa were found only at a single location and even samples from the same site displayed distinct communities, suggesting that belowground species richness in Arctic tundra habitats is highly heterogeneous. Preserving soil biodiversity in a changing Arctic is essential for Inuit communities who rely on intact tundra ecosystems for their health and wellbeing.

year: 2023-05-01

title: Supervision of Maori doctoral students: A descriptive report

authors: McKinley, Elizabeth

subjects: Maori education

abstract: This report follows up a previous paper that outlined the goals and plans of a research project that focused on both theoretical and cultural questions regarding the supervisory process for Māori doctoral students (McKinley, Grant, Middleton, Irwin, & Williams, 2007). The major goal of the project is to enhance understanding of the teaching and learning process of supervision for students and supervisors, particularly around issues of culture that arise in research methodologies and practices. This paper reports on the completed project by providing further operational background, design features, the nature of the student and supervisor samples and a summary of interview findings. The results show that there are indeed distinctive issues arising within the supervision of Māori doctoral students. Some of these are to do with both pleasures and challenges found in the supervision relationship, while others relate to the kinds of projects the students undertake. Many projects for example, push at the disciplinary boundaries of Western knowledge and are often rooted in a political desire to enhance the everyday lives of Māori. Yet others are connected to identity formation processes that concern many Māori during their years as doctoral students. A central message for supervisors from this work is that the supervision of Māori doctoral students may require unfamiliar forms of engagement but that these are likely to be deeply rewarding in many different ways.

text: Supervision of Maori doctoral students: A descriptive report This report follows up a previous paper that outlined the goals and plans of a research project that focused on both theoretical and cultural questions regarding the supervisory process for Māori doctoral students (McKinley, Grant, Middleton, Irwin, & Williams, 2007). The major goal of the project is to enhance understanding of the teaching and learning process of supervision for students and supervisors, particularly around issues of culture that arise in research methodologies and practices. This paper reports on the completed project by providing further operational background, design features, the nature of the student and supervisor samples and a summary of interview findings. The results show that there are indeed distinctive issues arising within the supervision of Māori doctoral students. Some of these are to do with both pleasures and challenges found in the supervision relationship, while others relate to the kinds of projects the students undertake. Many projects for example, push at the disciplinary boundaries of Western knowledge and are often rooted in a political desire to enhance the everyday lives of Māori. Yet others are connected to identity formation processes that concern many Māori during their years as doctoral students. A central message for supervisors from this work is that the supervision of Māori doctoral students may require unfamiliar forms of engagement but that these are likely to be deeply rewarding in many different ways.

year: 2009