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Improving responsiveness of time-sensitive applications by exploiting dynamic task dependencies
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Actuator Fault Compensation Tracking Control for Multi Linked 2WD Mobile Robots
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The effect of capacity management strategies on employees\textquotesingle well‐being: A quantitative investigation into the long‐term healthcare industry
No full textInternational audienceThe aim of this study is to understand the effects of different capacity management strategies on the well‐being of employees in long‐term healthcare organizations. Such strategies may produce psychological effects in terms of job satisfaction and well‐being among employees, namely frontline employees, thus affecting service quality. We collected 2158 observations from 42 nursing homes in Italy. Our results show that all capacity management strategies addressed in this study can influence the perceived degree of fatigue or of job hazard, and some of them can influence both. Moreover, a better perception of job hazard and fatigue leads to a higher degree of reported well‐being from employees, although with the former, it is only through the mediation of job satisfaction. We conclude our paper by discussing theoretical contributions and policy implications
“It's not how it looks ! " Exploring managerial perspectives on employee wellbeing
No full textInternational audienceThe literature on employee wellbeing (EW) has largely focussed on employees' subjective experiences and has generally assumed that managers’ interpretations of EW are consistent and non‐problematic. Tensions inherent in managing complex expectations, and diverse results, have not been adequately investigated, and ways in which EW practices are viewed by senior managers have not been sufficiently examined. This paper attempts to fill this gap by exploring the perceptions of senior managers with human resources (HR) responsibilities affecting EW. There is a specific focus on the tensions experienced by these senior managers and the related tactics they adopted to successfully manage them. We gathered data from focus groups made up of 20 senior managers from companies operating in the Milan County in Italy. An analysis of this data identified four predominant dimensions of EW, as well as the tensions felt by the managers and the various tactics they used to overcome them. Finally, we classified the interpretative tactics into four broad resolution strategies (i.e. flexible , integrative , separated and reciprocal thinking ) that senior managers adopted to cognitively address their experience of tensions
Kernel Approximation Methods for Speech Recognition
No full textWe study large-scale kernel methods for acoustic modeling in speech recognition and compare their performance to deep neural networks (DNNs). We perform experiments on four speech recognition datasets, including the TIMIT and Broadcast News benchmark tasks, and compare these two types of models on frame-level performance metrics (accuracy, cross-entropy), as well as on recognition metrics (word/character error rate). In order to scale kernel methods to these large datasets, we use the random Fourier feature method of Rahimi and Recht [2007]. We propose two novel techniques for improving the performance of kernel acoustic models. First, in order to reduce the number of random features required by kernel models, we propose a simple but effective method for feature selection. The method is able to explore a large number of non-linear features while maintaining a compact model more efficiently than existing approaches. Second, we present a number of frame-level metrics which correlate very strongly with recognition performance when computed on the heldout set; we take advantage of these correlations by monitoring these metrics during training in order to decide when to stop learning. This technique can noticeably improve the recognition performance of both DNN and kernel models, while narrowing the gap between them. Additionally, we show that the linear bottleneck method of Sainath et al. [2013a] improves the performance of our kernel models significantly, in addition to speeding up training and making the models more compact. Together, these three methods dramatically improve the performance of kernel acoustic models, making their performance comparable to DNNs on the tasks we explored
Migrate when necessary: toward partitioned reclaiming for soft real-time tasks
No full textInternational audienceThis paper presents a new strategy for scheduling soft real-time tasks on multiple identical cores. The proposed approach is based on partitioned CPU reservations and it uses a reclaiming mechanism to reduce the number of missed deadlines. We introduce the possibility for a task to temporarily migrate to another, less charged, CPU when it has exhausted the reserved bandwidth on its allocated CPU. In addition, we propose a simple load balancing method to decrease the number of deadlines missed by the tasks. The proposed algorithm has been evaluated through simulations, showing its effectiveness (compared to other multi-core reclaiming approaches) and comparing the performance of different partitioning heuristics (Best Fit, Worst Fit and First Fit)
Revgest: Augmenting Gestural Musical Instruments with Revealed Virtual Objects
No full textInternational audienceGestural interfaces, which make use of physiological signals, hand / body postures or movements, have become widespread for musical expression. While they may increase the transparency and expressiveness of instruments, they may also result in limited agency, for musicians as well as for spectators. This problem becomes especially true when the implemented mappings between gesture and music are subtle or complex. These instruments may also restrict the appropriation possibilities of controls, by comparison to physical interfaces. Most existing solutions to these issues are based on distant and/or limited visual feedback (LEDs, small screens). Our approach is to augment the gestures themselves with revealed virtual objects. Our contributions are, first a novel approach of visual feedback that allow for additional expressiveness, second a software pipeline for pixel-level feedback and control that ensures tight coupling between sound and visuals, and third, a design space for extending gestural control using revealed interfaces. We also demonstrate and evaluate our approach with the augmentation of three existing gestural musical instruments
Analyzing of Facial Paralysis by Shape Analysis of 3D Face Sequences
No full textInternational audienceIn this paper, we address the problem of quantifying the facial asymmetry from 3D face sequence (4D). We investigate the role of 4D data to reveal the amount of both static and dynamic asymmetry in the clinical case of facial paralysis. The goal is to provide tools to clinicians to evaluate quantitatively facial paralysis treatment based on Botulinum Toxin (BT), which can provide qualitative and quantitative evaluations. To this end, Dense Scalar Fields (DSFs), based on Riemannian analysis of 3D facial shape, is proposed to quantify facial deformations. To assess this approach, a new 3D facial sequences of 16 patients data set is collected, before and after injecting the BT. For each patient, we have collected 8 facial expressions before and after injecting BT. Experimental results obtained on this data set show that the proposed approach allows clinicians to evaluate more accurately the facial asymmetry before and after the treatment
