Too sick to drive : how motion sickness severity impacts human performance

There are multiple concerns surrounding the development and rollout of self-driving cars. One issue has largely gone unnoticed - the adverse effects of motion sickness as induced by self-driving cars. The literature suggests conditionally, highly and fully autonomous vehicles will increase the onset likelihood and severity of motion sickness. Previous research has shown motion sickness can have a significant negative impact on human performance. This paper uses a simulator study design with 51 participants to assess if the scale of motion sickness is a predictor of human performance degradation. This paper finds little proof that subjective motion sickness severity is an effective indicator of the scale of human performance degradation. The performance change of participants with lower subjective motion sickness is mostly statistically indistinguishable from those with higher subjective sickness. Conclusively, those with even acute motion sickness may be just as affected as those with higher sickness, considering human performance. Building on these results, it could indicate motion sickness should be a consideration for understanding user ability to regain control of a self-driving vehicle, even if not feeling subjectively unwell. Effectiveness of subjective scoring is discussed and future research is proposed to help ensure the successful rollout of self-driving vehicles

Human Performance Assessments in Cadet Populations

This study assessed potential physiological differences between the Ranger Challenge (RC) Competition team and junior year cadets in an Army Reserve Officer Training Corps (ROTC) program. The method included: RC (m = 11, f = 2) and junior year cadets (m = 7, f = 3) were assessed in the following areas: 1) quickness and agility (5-10-5 shuttle run), 2) total-body power (standing broad jump), and 3) grip strength (hand grip dynamometry) assessed. The 5-10-5 shuttle run was performed twice (opening once to the left and once to the right). The standing broad jump required that cadets stand with their toes behind a line, perform a maximum of three preparatory movements, triple extend their knees, hips, and ankles while using their upper body to propel them as far forward as possible. After the jump the distanced reached was measured from the line to the heel of the nearest foot. Hand grip dynamometry was performed once on each hand. The cadet held the dynamometer out to his or her side and squeezed it as they lowered it to their hip. The results were that there were no significant differences between groups for the 5-10-5 shuttle run (p = 0.91), standing broad jump (p = 0.49), or grip strength (p = 0.31). RC did not outperform

Human Resource Management and Performance

The relationship between Human Resource Management (HRM) and performance of the firm has been a hot debated topic in the field of HRM/IR for the last decade. Most scientific research on this topic originates from the USA. In our paper we will give an overview of recent USA-based research outcomes as a frame of reference for presenting recent findings from the Netherlands in this respect. These Dutch findings are interesting and contrasting USA-based approaches because they reflect the Western-European model for industrial relations or the so-called Rhineland model. A model in which legislation, institutions andstakeholders like workscouncils and trade unions play an important role in shaping HRM policies and practices. So the very often proclaimed relationship between corporate strategies, aligned HRM policies and their subsequent effect on performance is in a Dutch setting mitigated by institutions and stakeholders inside and outside the organization.human resource management;performance;HRM theory;institutionalism;overview

Predicting Performance Of Initial Public Offering (IPO) Firms: Should Human Resource Management (HRM) Be In The Equation?

Population ecology is utilized to understand the role of human resource management (HRM) in enhancing the performance of initial public offering (IPO) companies. This is done by examining the determinants of structural inertia and developing hypotheses on the relationship between HRM and organizational performance. The results indicate that two human resource variables (human resource value and organization-based rewards) predict initial investor reaction and long-term survival. The rewards variable negatively affects initial performance while positively impacting survival

Evaluating Visual Conversational Agents via Cooperative Human-AI Games

As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. In this work, we design a cooperative game - GuessWhich - to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend - that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.Comment: HCOMP 201

English Conversational Telephone Speech Recognition by Humans and Machines

One of the most difficult speech recognition tasks is accurate recognition of human to human communication. Advances in deep learning over the last few years have produced major speech recognition improvements on the representative Switchboard conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This then raises two issues - what IS human performance, and how far down can we still drive speech recognition error rates? A recent paper by Microsoft suggests that we have already achieved human performance. In trying to verify this statement, we performed an independent set of human performance measurements on two conversational tasks and found that human performance may be considerably better than what was earlier reported, giving the community a significantly harder goal to achieve. We also report on our own efforts in this area, presenting a set of acoustic and language modeling techniques that lowered the word error rate of our own English conversational telephone LVCSR system to the level of 5.5%/10.3% on the Switchboard/CallHome subsets of the Hub5 2000 evaluation, which - at least at the writing of this paper - is a new performance milestone (albeit not at what we measure to be human performance!). On the acoustic side, we use a score fusion of three models: one LSTM with multiple feature inputs, a second LSTM trained with speaker-adversarial multi-task learning and a third residual net (ResNet) with 25 convolutional layers and time-dilated convolutions. On the language modeling side, we use word and character LSTMs and convolutional WaveNet-style language models

Measuring Organizational Performance in Strategic Human Resource Management: Problems and Prospects

A major challenge for Strategic Human Resource Management research in the next decade will be to establish a clear, coherent and consistent construct for organizational performance. This paper describes the variety of measures used in current empirical research linking human resource management and organizational performance. Implications for future research are discussed amidst the challenges of construct definition, divergent stakeholder criteria and the temporal dynamics of performance. The concept of performance information markets that addresses these challenges is proposed as a framework for the application of multi-dimensional weighted performance measurement systems