Asynchronous Video Interviews and Artificial Intelligence: A Multi-Study Exploration

Asynchronous video interviews (AVIs) provide scalable, low-cost opportunities for matching interviewees and organizations. However, the implications of a shift from synchronous interviews aren’t fully understood, especially when design choices such as AI evaluations are employed. To better understand the impact of AVIs, we undertook an exploratory qualitative study in addition to an experiment. The first study involves 100 qualitative responses and exploratory quantitative tests on the relationships between coded values and demographic and trait variables of the respondents. Our second study tests the impact of AI feedback using a large online AVI service while accounting for various disadvantaged groups that could experience discrimination in their AVI interactions. We developed 5 propositions regarding the interaction of interviewee traits and AVI design. Additionally, we did not find support that AI feedback increases the performance of interviewees, though we identify several traits that lead to high AI scores and human-rater performance

Empowering customer engagement by informative billing: a European approach

Programmes aimed at improving end-use energy efficiency are a keystone in the market strategies of leading distribution system operators (DSOs) and energy retail companies and are increasing in application, soon expected to become a mainstream practice. Informative services based on electricity meter data collected for billing are powerful tools for energy savings in scale and increase customer engagement with the energy suppliers enabling the deployment of demand response programmes helping to optimise distribution grid operation. These services are completely in line with Europe’s 2020 strategy for overall energy performance improvement (cf. directives 2006/32/EC, 2009/72/EC, 2012/27/EU). The Intelligent Energy Europe project EMPOWERING involves 4 European utilities and an international team of university researchers, social scientists and energy experts for developing and providing insight based services and tools for 344.000 residential customers in Austria, France, Italy and Spain. The project adopts a systematic iterative approach of service development based on envisaging the utilities’, customers’ and legal requirements, and incorporates the feedback from testing in the design process. The technological solution provided by the leading partner CIMNE is scalable open source Big Data Analytics System coupled with the DSO’s information systems and delivering a range of value adding services for the customer, such as: - comparison with similar households - indications of performance improvements over time - consumption-weather dependence - detailed consumption visualisation and breakdown - personalised energy saving tips - alerts (high consumption, high bill, extreme temperature, etc.) The paper presents the development approach, describes the ICT system architecture and analyses the legal and regulatory context for providing this kind of services in the European Community. The limitations for third party data access, customer consent and data privacy are discussed, and how these have been overcome with the implementation of the “privacy by design” principle is explained

Enabling e-Research in combustion research community

Abstract This paper proposes an application of the Collaborative e-Science Architecture (CeSA) to enable e-Research in combustion research community. A major problem of the community is that data required for constructing modelling might already exist but scattered and improperly evaluated. That makes the collection of data for constructing models difficult and time-consuming. The decentralised P2P collaborative environment of the CeSA is well suited to solve this distributed problem. It opens up access to scattered data and turns them to valuable resources. Other issues of the community addressed here are the needs for computational resources, storages and interoperability amongst different data formats can also be addressed by the use of Grid environment in the CeSA

Semi-automatic semantic enrichment of raw sensor data

One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management

HELIN Consortium LORI Grant Applicant Information

Completed grant application for HELIN\u27s successful Library of Rhode Island grant. Project Synopsis: The purpose of this project is to collaborate with Rhode Island library and information management professionals, community and business leaders, government and education professionals, and college and university scholars to gather information in preparation for writing a grant to create a statewide digital repository for Rhode Island