“Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; and in addressing the issue of fairness, we present experimental evidence regarding the potential for AI/ML to evoke adverse reactions from job applicants during selection procedures. We close by emphasizing increased collaboration among I-O psychologists, computer scientists, legal scholars, and members of other professional disciplines in developing, implementing, and evaluating AI/ML applications in organizational contexts

In Car Audio

This chapter presents implementations of advanced in Car Audio Applications. The system is composed by three main different applications regarding the In Car listening and communication experience. Starting from a high level description of the algorithms, several implementations on different levels of hardware abstraction are presented, along with empirical results on both the design process undergone and the performance results achieved

The hArtes Carlab: Hardware Implementation and Algorithm Development

International audienceIn the last decade Car Infotainment Systems (CIS) have been gaining great attention by the scientific and industrial community: in this context, within the European hArtes Project, an advanced CIS (ACIS) has been designed. The system offers several functionalities employing professional audio equipment and PCs able to manage different I/O audio channels and to provide a large computing capability for complex audio algorithms. The overall architecture is based on NU-Tech platform which manages the whole system from professional equipment to audio streaming and processing. The system has been therefore devised as a real audio laboratory (hArtes CarLab) for audio algorithm exploration and validation, providing a remote access to all the system functionalities. In this paper, starting from the hardware description, a complete set of algorithms to enhance audio reproduction, hands-free communication,and interactivity through speaker and speech recognition features is discussed in relation to the NU-Tech framework. INTRODUCTION In the last decade Car infotainment (i.e. the combination of information with entertainment features) systems have attracted many efforts by industrial research because car market is sensible to the introduction of innovative services for drivers and passengers. The need for an Advanced Car Infotainment System (ACIS) has been recently emerging, able to handle issues left open by CIS systems already on the market, and to overcome their limitations. Due to traffic congestion and growing distance from home to workplace, people spend more and more time in car, that hence becomes an appealing place to do many common activities such as listening to music and news, phone calling and doing many typical office tasks. Narrowing our focus to audio, the key role of the CIS is that it lets the driver concentrate on the road and at the same time it manages many different processing functions such as high quality music playback, hands-free communication, voice commands, speaker recognition, etc. Moreover, from the signal processing point of view, the wideband nature of the audio signal adds complexity while the requested quality calls for high precision signal processing

Mapping Embedded Applications on MPSoCs: The MNEMEE Approach

As embedded systems are becoming the center of our digital life, system design becomes progressively harder. The integration of multiple features on devices with limited resources requires careful and exhaustive exploration of the design search space in order to efficiently map modern applications to an embedded multi-processor platform. The MNEMEE project addresses this challenge by offering a unique integrated tool flow that performs source-to-source transformations to automatically optimize the original source code and map it on the target platform. The optimizations aim at reducing the number of memory accesses and the required memory storage of both dynamically and statically allocated data. Furthermore, the MNEMEE tool flow performs optimal assignment of all data on the memory hierarchy of the target platform. Designers can use the whole flow or a part of it and integrate it into their own design flow. This paper gives an overview of the MNEMEE tool flow along. It also presents two industrial case studies that demonstrate who the techniques and tools developed in the MNEMEE project can be integrated into industrial design flows