Automated Analysis of Human Performance Data could help to understand and possibly predict the performance of the human. To inform future research and enable Automated Analysis of Human Performance Data a systematic mapping study (scoping study) on the state-of-the-art knowledge is performed on three interconnected components(i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis. Using a systematic method of Kitchenham and Charters for performing the systematic mapping study, resulted in a comprehensive search for studies and a categorisation the studies using a qualitative method. This systematic mapping review extends the philosophy of Shyr and Spisic, and Knuth and represents the state-of-art knowledge on Human Performance,Monitoring Human Performance and Automated Data Analysis
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From the article: "After 1993, the concept of strategic alignment is evaluated from the connection between IT and business to much broader definitions in which the connection between all business functions, horizontally and vertically, and later also with projects and stakeholders is mentioned. To achieve stategic alignment there must be a coordination between the strategy of organizations and those who contribute to the implementation of the strategy and the actual performance of an organization. This process is called Human Oriented Performance Management (HOPM). The HOPM model consists of four dimensions: strategy translation, information and visualization, dialogue and action orientation, and continues improvement and organizational learning. To measure the effect of strategic alignment a range of financial performance indicators are used. Based on a literature review this paper explores which financial performance indicators could be used to measure the effect of HOPM. The literature was selected over a period from 1996 – 2015. The research is not only focused on the top of the strategy map, but also on the cause-effect relationships in the strategy map. The underlying performance indicators in the strategy map can show on which figures the dialogue in the HOPM model about strategy implementation must be based. This dialogue is the input to action in which strategic alignment comes about. The goal of the research is to optimize this dialogue by looking for performance indicators that can show the effect of HOPM" The article is used for the course: 'corporate policy' minor MSMM (Masterclass Strategic Marketing Management).
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Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user’s perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human- AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.
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A substantial amount of studies have addressed the influence of sound on human performance. In many of these, however, the large acoustic differences between experimental conditions prevent a direct translation of the results to realistic effects of room acoustic interventions. This review identifies those studies which can be, in principle, translated to (changes in) room acoustic parameters and adds to the knowledge about the influence of the indoor sound environment on people. The review procedure is based on the effect room acoustics can have on the relevant quantifiers of the sound environment in a room or space. 272 papers containing empirical findings on the influence of sound or noise on some measure of human performance were found. Of these, only 12 papers complied with this review's criteria. A conceptual framework is suggested based on the analysis of results, positioning the role of room acoustics in the influence of sound on task performance. Furthermore, valuable insights are pre- sented that can be used in future studies on this topic. Whi le the influence of the sound environment on performance is clearly an issue in many situations, evidence regarding the effectiveness of strategies to control the sound environment by room acoustic design is lacking and should be a focus area in future studies.
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Deze agenda is een strategisch kader voor human capitalontwikkelingen in de creatieve industrie in de Metropoolregio Amsterdam voor de komende vier jaar (2012-2016). De agenda bestrijkt de gehele breedte van de creatieve industrie en richt zich op een interdisciplinaire aanpak en op het stimuleren van een onderzoekende en ondernemende cultuur in het onderwijs. Leidende thema’s zijn: • onderwijs over ondernemerschap;; • vraag en aanbod op elkaar afstemmen;; • alumni & permanente educatie;; • internationalisering. De Creatieve Industrie is de belangrijkste top sector voor de Metropoolregio Amsterdam (CBS monitor topsectoren 2012). Voor de beschrijving van de Creatieve Industrie in de Metropoolregio is een benadering vanuit drie clusters aangehouden: Kunsten & Cultureel Erfgoed, Media & Entertainment, Creatieve Zakelijke Diensten (reclame, mode vormgeving, architectuur). Het Kernteam Creatieve Industrie MRA wil een belangrijke bijdrage leveren aan de Europese en landelijke ambitie om Nederland in 2020 de meest creatieve economie van Europa te laten zijn. Dit vraagt om continue innovatie, slimme en creatieve oplossingen. Daarvoor is slim, creatief, jong (top)talent onmisbaar. Bij deze ambitie hoort een naadloze verbinding en samenwerking tussen bedrijfsleven en kennis- en onderwijsinstellingen. Het concurrerende klimaat, dynamiek en tempo in de sector vragen om snelle toepassing van nieuwe kennis en technologie en om een voortdurende instroom van nieuw (internationaal) creatief (top)talent en permanente bijscholing. Naast een economische waarde heeft de creatieve sector ook een maatschappelijk toegevoegde waarde. Met name de subsector Kunsten & Cultureel Erfgoed bevordert, met een vaak cross-sectorele aanpak, participatie en cohesie van diverse groepen in de samenleving. De toegevoegde waarde van de creatieve industrie wordt door andere sectoren nog onvoldoende op waarde geschat en benut. Voor professionals en aankomend talent is het cruciaal dat zij de juiste kennis en vaardigheden ontwikkelen om de meerwaarde en identiteit van de creatieve industrie over het voetlicht te brengen. De ondertekenaars van deze HCA hebben de intentie de ingezette samenwerking nog concreter vorm te geven. Het Centre of Expertise, Centrum voor Innovatief Vakmanschap en de Amsterdam Campus zijn hierbij dé vehikels om concrete afspraken en projecten tussen de drie partijen uit de gouden driehoek te realiseren. Prioriteit hierbij is de vraagarticulatie vanuit het bedrijfsleven verder aan te scherpen, afspraken hierover tussen partijen zijn reeds gemaakt. AIM wordt gevraagd twee per jaar een bijeenkomst te organiseren om concrete acties met elkaar te benoemen. Deze HCA, met bijbehorende ambitie en invulling, zal dan ook jaarlijks door het Kernteam geëvalueerd en zo nodig bijgesteld worden. Hierbij blijft afstemming met de MRA –agenda’s: HCA ICT en HCA Toerisme en Congressen gewenst.
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Physical activity is crucial in human life, whether in everyday activities or elite sports. It is important to maintain or improve physical performance, which depends on various factors such as the amount of physical activity, the capability, and the capacity of the individual. In daily life, it is significant to be physically active to maintain good health, intense exercise is not necessary, as simple daily activities contribute enough. In sports, it is essential to balance capacity, workload, and recovery to prevent performance decline or injury.With the introduction of wearable technology, it has become easier to monitor and analyse physical activity and performance data in daily life and sports. However, extracting personalised insights and predictions from the vast and complex data available is still a challenge.The study identified four main problems in data analytics related to physical activity and performance: limited personalised prediction due to data constraints, vast data complexity, need for sensitive performance measures, overly simplified models, and missing influential variables. We proposed end investigated potential solutions for each issue. These solutions involve leveraging personalised data from wearables, combining sensitive performance measures with various machine learning algorithms, incorporating causal modelling, and addressing the absence of influential variables in the data.Personalised data, machine learning, sensitive performance measures, advanced statistics, and causal modelling can help bridge the data analytics gap in understanding physical activity and performance. The research findings pave the way for more informed interventions and provide a foundation for future studies to further reduce this gap.
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This research reviews the current literature on the impact of Artificial Intelligence (AI) in the operation of autonomous Unmanned Aerial Vehicles (UAVs). This paper examines three key aspects in developing the future of Unmanned Aircraft Systems (UAS) and UAV operations: (i) design, (ii) human factors, and (iii) operation process. The use of widely accepted frameworks such as the "Human Factors Analysis and Classification System (HFACS)" and "Observe– Orient–Decide–Act (OODA)" loops are discussed. The comprehensive review of this research found that as autonomy increases, operator cognitive workload decreases and situation awareness improves, but also found a corresponding decline in operator vigilance and an increase in trust in the AI system. These results provide valuable insights and opportunities for improving the safety and efficiency of autonomous UAVs in the future and suggest the need to include human factors in the development process.
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Research conducted by the Research Group Study Success indicates that many students experience performance pressure. In addition, we’ve noticed an increase in performance pressure in recent years. A little bit of performance pressure can be a good thing: it can facilitate concentration or hitting your deadlines. Are you feeling pressured over extensive periods of time, or are you experiencing stress, lack of sleep, or decreased concentration due to concerns about delivering on performance? Then it is probably a good plan to spring into action. With this info sheet we will explain what performance pressure entails, what causes it, and we will offer suggestions on how to handle performance pressure.
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Artificially intelligent agents increasingly collaborate with humans in human-agent teams. Timely proactive sharing of relevant information within the team contributes to the overall team performance. This paper presents a machine learning approach to proactive communication in AI-agents using contextual factors. Proactive communication was learned in two consecutive experimental steps: (a) multi-agent team simulations to learn effective communicative behaviors, and (b) human-agent team experiments to refine communication suitable for a human team member. Results consist of proactive communication policies for communicating both beliefs and goals within human-agent teams. Agents learned to use minimal communication to improve team performance in simulation, while they learned more specific socially desirable behaviors in the human-agent team experiment
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