The Ambient Spotlight: Queryless Desktop Search from Meeting Speech

It has recently become possible to record any small meeting using a laptop equipped with a plug-and-play USB microphone array. We show the potential for such recordings in a personal aid that allows project managers to record their meetings and, when reviewing them afterwards through a standard calendar interface, to find relevant documents on their computer. This interface is intended to supplement or replace the textual searches that managers typically perform. The prototype, which relies on meeting speech recognition and topic segmentation, formulates and runs desktop search queries in order to present its results

The Ambient Spotlight: Personal meeting capture with a microphone array

We present the Ambient Spotlight system for personal meeting capture based on a portable USB microphone array and a laptop. The system combined distant speech recognition and content linking with personal productivity tools, and enables recognised meeting recordings to be integrated with desktop search, calender, and email. 1. OVERVIEW The Ambient Spotlight is a personal, laptop-based application which combines meeting recording using a microphone array, distant speech recognition, topic segmentation, and information retrieval to provide a way to automatically capture and structure recorded meetings, and to integrate their access with standard productivity applications such as calendaring and email. This work builds on recent work on new approaches t

Te Pae Mahutonga and the measurement of community capital in regional Aotearoa New Zealand

Regionally, iwi and hapū have limited influence over structural changes such as population decline, proximity to labour markets and ageing, and to some extent economic cycles. However, there is still considerable value in thinking about how relevant indicators might point to the regeneration and overall well-being of Māori communities. In this paper we present an exploratory framework that links Durie’s Te Pae Mahutonga model of Māori well-being to the measurement of community capital. We use Te Pae Mahutonga as the basis for developing a number of key indicators for understanding Māori well-being in the regions and apply the framework and indicators to three regional settlements in Aotearoa New Zealand: Pōkeno, Huntly and Ōpōtiki

Accessing a Large Multimodal Corpus Using an Automatic Content Linking Device

As multimodal data becomes easier to record and store, the question arises as to what practical use can be made of archived corpora, and in particular what tools allowing efficient access to it can be built. We use the AMI Meeting Corpus as a case study to build an automatic content linking device, i.e. a system for real-time data retrieval. The corpus provides not only the data repository, but is used also to simulate ongoing meetings for development and testing of the device. The main features of the corpus are briefly described, followed by an outline of data preparation steps prior to indexing, and of the methods for building queries from ongoing meeting discussions, retrieving elements from the corpus and accessing the results. A series of user studies based on prototypes of the content linking device have confirmed the relevance of the concept, and methods for task-based evaluation are under development

Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)

This study investigates typical domestic energy demand profiles and their variation over time. It draws on a sample of 13,000 homes from Great Britain, applying k-means cluster analysis to smart meter data on their electricity and gas demand over a three-year period from September 2019 to August 2022. Eight typical demand archetypes are identified from the data, varying in terms of the shape of their demand profile over the course of the day. These include an ‘All daytime’ archetype, where demand rises in the morning and remains high until the evening. Several other archetypes vary in terms of the presence and timing of morning and/or evening peaks. In the case of electricity demand, a ‘Midday trough’ archetype is notable for its negative midday demand and high overnight demand, likely a combination of the effects of rooftop solar panels exporting to the grid during the day and overnight charging of electric vehicles or electric storage heating. The prevalence of each archetype across the sample varies substantially in relation to different temporally-varying factors. Fluctuations in their prevalence on weekends can be identified, as can Christmas Day. Among homes with gas central heating, the prevalence of gas archetypes strongly relates to external temperature, with around half of homes fitting the ‘All daytime’ archetype at temperatures below 0 °C, and few fitting it above 14 °C. COVID-19 pandemic restrictions on work and schooling are associated with households' patterns of daily demand becoming more similar on weekdays and weekends, particularly for households with children and/or workers. The latter group had still not returned to pre-pandemic patterns by March 2022. The results indicate that patterns of daily energy demand vary with factors ranging from societal weekly rhythms and festivals to seasonal temperature changes and system shocks like pandemics, with implications for demand forecasting and policymaking

Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)

This study investigates typical domestic energy demand profiles and their variation over time. It draws on a sample of 13,000 homes from Great Britain, applying k-means cluster analysis to smart meter data on their electricity and gas demand over a three-year period from September 2019 to August 2022. Eight typical demand archetypes are identified from the data, varying in terms of the shape of their demand profile over the course of the day. These include an ‘All daytime’ archetype, where demand rises in the morning and remains high until the evening. Several other archetypes vary in terms of the presence and timing of morning and/or evening peaks. In the case of electricity demand, a ‘Midday trough’ archetype is notable for its negative midday demand and high overnight demand, likely a combination of the effects of rooftop solar panels exporting to the grid during the day and overnight charging of electric vehicles or electric storage heating. The prevalence of each archetype across the sample varies substantially in relation to different temporally-varying factors. Fluctuations in their prevalence on weekends can be identified, as can Christmas Day. Among homes with gas central heating, the prevalence of gas archetypes strongly relates to external temperature, with around half of homes fitting the ‘All daytime’ archetype at temperatures below 0 °C, and few fitting it above 14 °C. COVID-19 pandemic restrictions on work and schooling are associated with households' patterns of daily demand becoming more similar on weekdays and weekends, particularly for households with children and/or workers. The latter group had still not returned to pre-pandemic patterns by March 2022. The results indicate that patterns of daily energy demand vary with factors ranging from societal weekly rhythms and festivals to seasonal temperature changes and system shocks like pandemics, with implications for demand forecasting and policymaking

Towards investigating the validity of measurement of self-regulated learning based on trace data

Contains fulltext : 250033.pdf (Publisher’s version ) (Open Access)Contemporary research that looks at self-regulated learning (SRL) as processes of learning events derived from trace data has attracted increasing interest over the past decade. However, limited research has been conducted that looks into the validity of trace-based measurement protocols. In order to fill this gap in the literature, we propose a novel validation approach that combines theory-driven and data-driven perspectives to increase the validity of interpretations of SRL processes extracted from trace-data. The main contribution of this approach consists of three alignments between trace data and think aloud data to improve measurement validity. In addition, we define the match rate between SRL processes extracted from trace data and think aloud as a quantitative indicator together with other three indicators (sensitivity, specificity and trace coverage), to evaluate the "degree" of validity. We tested this validation approach in a laboratory study that involved 44 learners who learned individually about the topic of artificial intelligence in education with the use of a technology-enhanced learning environment for 45 minutes. Following this new validation approach, we achieved an improved match rate between SRL processes extracted from trace-data and think aloud data (training set: 54.24%; testing set: 55.09%) compared to the match rate before applying the validation approach (training set: 38.97%; test set: 34.54%). By considering think aloud data as "reference point", this improvement of the match rate quantified the extent to which validity can be improved by using our validation approach. In conclusion, the novel validation approach presented in this study used both empirical evidence from think aloud data and rationale from our theoretical framework of SRL, which now, allows testing and improvement of the validity of trace-based SRL measurements.39 p