The Role-Based Performance Scale: Validity Analysis of a Theory-Based Measure

This study introduces a theory-based measure of employee performance (Role Based Performance Scale, RBPS) that is supported with results from a validation study using 10 data sets from six companies. In contrast to traditional, job-related measures of employee performance, we propose an alternative measure of performance based on role theory and identity theory. Because our results support the validity of the scale, we think that the instrument can be used for future research that requires a generalizable measure of performance. The scale demonstrates diagnostic properties that make it useful for practitioners as well as researchers

Population receptive field (pRF) measurements of chromatic responses in human visual cortex using fMRI

The spatial sensitivity of the human visual system depends on stimulus color: achromatic gratings can be resolved at relatively high spatial frequencies while sensitivity to isoluminant color contrast tends to be more low-pass. Models of early spatial vision often assume that the receptive field size of pattern-sensitive neurons is correlated with their spatial frequency sensitivity - larger receptive fields are typically associated with lower optimal spatial frequency. A strong prediction of this model is that neurons coding isoluminant chromatic patterns should have, on average, a larger receptive field size than neurons sensitive to achromatic patterns. Here, we test this assumption using functional magnetic resonance imaging (fMRI). We show that while spatial frequency sensitivity depends on chromaticity in the manner predicted by behavioral measurements, population receptive field (pRF) size measurements show no such dependency. At any given eccentricity, the mean pRF size for neuronal populations driven by luminance, opponent red/green and S-cone isolating contrast, are identical. Changes in pRF size (for example, an increase with eccentricity and visual area hierarchy) are also identical across the three chromatic conditions. These results suggest that fMRI measurements of receptive field size and spatial resolution can be decoupled under some circumstances - potentially reflecting a fundamental dissociation between these parameters at the level of neuronal populations

Informacijski servisni sustav za poljoprivredni IoT

Internet of Things (IoT) was faced with some difficulties which contained mass data management, various standards of object identification, data fusion of multiple sources, business data management and information service providing. In China, some safety monitoring systems of agricultural product always adopt centralized system architecture in which the data is stored concentratively. These systems could not be connected with or accessed by each other. This paper proposed an information system of agriculture Internet of Things based on distributed architecture. A distributed information service system based on IoT-Information Service, Object Naming Service, Discovery Service is designed to provide public information service including of capturing, standardizing, managing and querying of massive business data of agriculture production. A coding scheme for agricultural product, business location and logistic unit is provided for data identification. A business event model of agriculture IoT is presented for business data management. The whole system realizes the tracking and tracing of agricultural products, and quality monitoring of agriculture production. The implementation of this information service system is introduced.Internet stvari suočen je s poteškoćama poput upravljanja s velikom količinom podataka, različitim standardnima identifikacije objekata, fuzije podataka iz više izvora, upravljanja poslovnim podatcima i pružanje informacijskih usluga. Sigurnosno nadgledanje poljoprivrednih proizvoda u Kini uvijek podliježe centraliziranoj arhitekturi gdje su podatci koncentrirani na jednom mjestu. Takvi sustavi ne mogu biti povezani jedni s drugim te jedan drugome ne mogu pristupati. U ovome radu predložen je informacijski sustav za poljoprivredni internet stvari temeljen na distribuiranoj arhitekturi. Distribuirani informacijski servisni sustav baziran na IoT (Internet stvari), sustav za imenovanje objekata i sustav za otkrivanje omogućuju javni informacijski servis uključujući prikupljanje, standardizaciju, upravljanje i ispitivanje velikih količina podataka o poljoprivrednim proizvodima. Prikazana je shema kodiranja za poljopoprivredne proizvode, poslovne lokacije i logističke jedinice za identifikaciju podataka. Poslovni model doga.aja za poljoprivredni IoT je prezentiran za upravljanje poslovnim podatcima. Cjelokupni sustav omogućuje praćenje poljoprivrednih proizvoda te nadgledanje njihove kvalitete. Rad tako.er daje uvid u implementaciju informacijskog servisnog sustava

Framework for contextualized learning ecosystems

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011Using mobile personal devices to interact with pervasive smart learning objects and services that create contextualized learning ecosystems can enhance both the learning outcomes and the motivational states of students. This paper defines and analyzes several pervasive learning ecosystems in which students at the Carlos III University of Madrid interact with contextualized learning objects and services. The technology defining the contextualized learning environments is first introduced and later used in two user experiments. These experiments provide data both about the learning outcomes for students after interacting with smart learning objects and services and about the motivational impact that the use of these technologies have on themThe research leading to these results has been partially funded by the ARTEMISA project TIN2009-14378-C02-02 within the Spanish "Plan Nacional de I+D+I", the Madrid regional community projects S2009/TIC-1650 and CCG10-UC3M/TIC-4992 and the SOLITE CYTED Program 508AC0341. Thanks to INNOVISION for providing the NFC tags for this experiment. Gustavo Ramirez- Gonzalez is funded by the EU Programme Alban, scholarship number E06D101768CO and by the Universidad del Cauca

Human colour perception changes between seasons

Humans identify four 'unique hues' - blue, green, yellow and red - that do not appear to contain mixtures of other colours. Unique yellow (UY) is particularly interesting because it is stable across large populations: participants reliably set a monochromatic light to a stereotypical wavelength. Individual variability in the ratio of L- and M-cones in the retina, and effects of ageing, both impact unique green (UG) settings [1,2], but cannot predict the relatively small inter-individual differences in UY [2,3]. The stability of UY may arise because it is set by the environment rather than retinal physiology. Support for this idea comes from studies of long-term, artificial chromatic adaptation [4,5], but there is no evidence for this process in natural settings. Here, we measured 67 participants in York (UK) in both the winter and summer, and found a significant seasonal change in UY settings. In comparison, Rayleigh colour matches that would not be expected to exhibit environmentally driven changes were found to be constant. The seasonal shift in UY settings is consistent with a model that reweights L- and M-cone inputs into a perceptual opponent colour channel after a small, seasonally-driven change in mean L:M cone activity

Model Checking Branching Properties on Petri Nets with Transits (Full Version)

To model check concurrent systems, it is convenient to distinguish between the data flow and the control. Correctness is specified on the level of data flow whereas the system is configured on the level of control. Petri nets with transits and Flow-LTL are a corresponding formalism. In Flow-LTL, both the correctness of the data flow and assumptions on fairness and maximality for the control are expressed in linear time. So far, branching behavior cannot be specified for Petri nets with transits. In this paper, we introduce Flow-CTL* to express the intended branching behavior of the data flow while maintaining LTL for fairness and maximality assumptions on the control. We encode physical access control with policy updates as Petri nets with transits and give standard requirements in Flow-CTL*. For model checking, we reduce the model checking problem of Petri nets with transits against Flow-CTL* via automata constructions to the model checking problem of Petri nets against LTL. Thereby, physical access control with policy updates under fairness assumptions for an unbounded number of people can be verified.Comment: 23 pages, 5 figure

Meditation Awareness Training (MAT) for Work-related Wellbeing and Job Performance: A Randomised Controlled Trial

Due to its potential to concurrently improve work-related wellbeing (WRW) and job performance, occupational stakeholders are becoming increasingly interested in the applications of meditation. The present study conducted the first randomized controlled trial to assess the effects of meditation on outcomes relating to both WRW and job performance. Office-based middle-hierarchy managers (n = 152) received an eight-week meditation intervention (Meditation Awareness Training; MAT) or an active control intervention. MAT participants demonstrated significant and sustainable improvements (with strong effect sizes) over control-group participants in levels of work-related stress, job satisfaction, psychological distress, and employer-rated job performance. There are a number of novel implications: (i) meditation can effectuate a perceptual shift in how employees experience their work and psychological environment and may thus constitute a cost-effective WRW intervention, (ii) meditation-based (i.e., present-moment-focussed) working styles may be more effective than goal-based (i.e., future-orientated) working styles, and (iii) meditation may reduce the separation made by employees between their own interests and those of the organizations they work for

Using Ownership as an Incentive

Agency theory is used to develop hypotheses regarding the effects of ownership proliferation on firm performance. The authors examine the effects of chief executive officer (CEO) ownership, executive team ownership, and all employee ownership in addition to the moderating effect of risk on firm survival and stock price. Firms with low CEO ownership outperform those with high levels of CEO ownership across all levels of risk, but the effect is most pronounced for low-risk firms. Executive team ownership is negatively related to firm performance, whereas ownership for all employees is positively associated with firm performance, particularly for higher risk firms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67316/2/10.1177_1059601199244003.pd

Machine learning using magnetic stochastic synapses

The impressive performance of artificial neural networks has come at the cost of high energy usage and CO2 emissions. Unconventional computing architectures, with magnetic systems as a candidate, have potential as alternative energy-efficient hardware, but, still face challenges, such as stochastic behaviour, in implementation. Here, we present a methodology for exploiting the traditionally detrimental stochastic effects in magnetic domain-wall motion in nanowires. We demonstrate functional binary stochastic synapses alongside a gradient learning rule that allows their training with applicability to a range of stochastic systems. The rule, utilising the mean and variance of the neuronal output distribution, finds a trade-off between synaptic stochasticity and energy efficiency depending on the number of measurements of each synapse. For single measurements, the rule results in binary synapses with minimal stochasticity, sacrificing potential performance for robustness. For multiple measurements, synaptic distributions are broad, approximating better-performing continuous synapses. This observation allows us to choose design principles depending on the desired performance and the device's operational speed and energy cost. We verify performance on physical hardware, showing it is comparable to a standard neural network

Voltage-controlled superparamagnetic ensembles for low-power reservoir computing

We propose thermally driven, voltage-controlled superparamagnetic ensembles as low-energy platforms for hardware-based reservoir computing. In the proposed devices, thermal noise is used to drive the ensembles' magnetization dynamics, while control of their net magnetization states is provided by strain-mediated voltage inputs. Using an ensemble of CoFeB nanodots as an example, we use analytical models and micromagnetic simulations to demonstrate how such a device can function as a reservoir and perform two benchmark machine learning tasks (spoken digit recognition and chaotic time series prediction) with competitive performance. Our results indicate robust performance on timescales from microseconds to milliseconds, potentially allowing such a reservoir to be tuned to perform a wide range of real-time tasks, from decision making in driverless cars (fast) to speech recognition (slow). The low energy consumption expected for such a device makes it an ideal candidate for use in edge computing applications that require low latency and power. The authors thank the Engineering and Physical Sciences Research Council (Grant No.: EP/S009647/1 and EP/V006339/1) for financial support. The project leading to this application has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 861618 (SpinENGINE)