This article deals with automatic object recognition. The goal is that in a certain grey-level image, possibly containing many objects, a certain object can be recognized and localized, based upon its shape. The assumption is that this shape has no special characteristics on which a dedicated recognition algorithm can be based (e.g. if we know that the object is circular, we could use a Hough transform or if we know that it is the only object with grey level 90, we can simply use thresholding). Our starting point is an object with a random shape. The image in which the object is searched is called the Search Image. A well known technique for this is Template Matching, which is described first.
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Background: Despite trends towards greater professionalisation of the nursing profession and an improved public image in certain countries, studies also show that large proportions of the public still do not fully appreciate nurses’ competencies. Mapping differences in the societal and professional recognition of nurses allows for benchmarking among countries. Aim: To investigate the level of societal recognition of the nursing profession in nine European countries, and the level of professional recognition perceived by European nurses themselves; to compare levels of recognition between countries; and to identify influencing factors. Methods: A cross-sectional study was conducted. Through an online survey, the study surveyed both the general public and nurses from various healthcare settings across nine countries between December 2022 and June 2023. The instrument used was a combination of self-developed questions on societal and professional recognition, the Work Motivation Scale and an adapted version of the Multidimensional Work Motivation Scale. Data were analysed using SPSS v.29.0, with socioeconomic prestige scores for the public and work environment/work motivation scores for nurses calculated accordingly. Results: A total of 1618 adult citizens and 2335 nurses participated. The public predominantly characterised nurses with attributes such as friendliness, warmth, empathy and compassion. The mean socioeconomic prestige score assigned to nurses was 7.2/10 (SD 1.9), with Portugal having the highest score (M 7.5/10, SD 2.0) and Norway the lowest (M 5.8/10, SD 1.4; p < 0.001). Professional recognition experienced by nurses was generally low (54% indicated rather low, 17% very low). Slovenia, the Netherlands and Belgium had slightly higher mean scores (all M 1.4/3) compared to other countries (p < 0.001). High professional recognition could be predicted for 33% by work environment score (OR = 1.21; 95% CI [1.19–1.24]), work motivation score (OR = 1.02; 95%CI[1.01–1.02]), expertise outside the hospital (OR = 1.57; 95% CI [1.25–1.97]) and work experience (OR = 1.01; 95% CI [1.00–1.02]) corrected for country. Conclusion: The study highlights the need for targeted interventions to improve the professional and public image of the nursing profession while addressing disparities in professional recognition between countries. Longitudinal studies are recommended to monitor changes in public perception and professional recognition among nurses.
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We will demonstrate a prototype exergame aimed at the serious domain of elderly fitness. The exergame incorporates straightforward means to gesture recognition, and utilises a Kinect camera to obtain 2.5D sensory data of the human user.
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Digital surveillance technologies using artificial intelligence (AI) tools such as computer vision and facial recognition are becoming cheaper and easier to integrate into governance practices worldwide. Morocco serves as an example of how such technologies are becoming key tools of governance in authoritarian contexts. Based on qualitative fieldwork including semi-structured interviews, observation, and extensive desk reviews, this chapter focusses on the role played by AI-enhanced technology in urban surveillance and the control of migration between the Moroccan–Spanish borders. Two cross-cutting issues emerge: first, while international donors provide funding for urban and border surveillance projects, their role in enforcing transparency mechanisms in their implementation remains limited; second, Morocco’s existing legal framework hinders any kind of public oversight. Video surveillance is treated as the sole prerogative of the security apparatus, and so far public actors have avoided to engage directly with the topic. The lack of institutional oversight and public debate on the matter raise serious concerns on the extent to which the deployment of such technologies affects citizens’ rights. AI-enhanced surveillance is thus an intrinsically transnational challenge in which private interests of economic gain and public interests of national security collide with citizens’ human rights across the Global North/Global South divide.
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Early detection of reading problems is important to prevent an enduring lag in reading skills. We studied the relationship between speed of word recognition (after six months of grade 1 education) and four kindergarten pre-literacy skills: letter knowledge, phonological awareness and naming speed for both digits and letters. Our sample consisted of 178 pupils divided over seven classes. In agreement with the literature, we found that all four kindergarten tests were related to speed of word recognition in grade 1. We also performed a multiple regression analysis with a set of background variables and the four kindergarten tests. The model explained 53% of the variance in speed of word recognition. However, only letter knowledge and naming speed for digits had a significant direct effect. Our conclusion is, nevertheless, that all four kindergarten tests should be used to identify children at risk for reading problems.
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We present a novel hierarchical model for human activity recognition. In contrast with approaches that successively recognize actions and activities, our approach jointly models actions and activities in a unified framework, and their labels are simultaneously predicted. The model is embedded with a latent layer that is able to capture a richer class of contextual information in both state-state and observation-state pairs. Although loops are present in the model, the model has an overall linear-chain structure, where the exact inference is tractable. Therefore, the model is very efficient in both inference and learning. The parameters of the graphical model are learned with a structured support vector machine. A data-driven approach is used to initialize the latent variables; therefore, no manual labeling for the latent states is required. The experimental results from using two benchmark datasets show that our model outperforms the state-of-the-art approach, and our model is computationally more efficient.
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This article focuses on the recent judgment of the Court of Justice, Aranyosi and Caldararu. After conducting a legal analysis on this case, three issues are identified and they are separately discussed in three sections. The aim of this paper is to show the impact of this judgment on public order and public security in Europe on the one hand and on the individual’s fundamental rights, on the other hand. It is going to be argued that even though there are limits to the principle of mutual recognition, this new exception based on fundamental rights establishes a new procedure for non-surrender. Therefore, the Court of Justice creates a non-execution ground which the EU legislator did not intend to include in the Framework Decision on the European arrest warrant. This is explained by looking at the three interconnected notions of Freedom, Security and Justice.
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Abstract: In this article I will reconstruct the Dutch debate on citizenship against the developing thinking about social citizenship in the context of globalization. I will show that the confusion around citizenship in the Netherlands can be seen as a result from a split between an unanimous negative valuation of Dutch citizen participation and the practice in which the participation of Dutch population is high. Recent theory on citizenship and participation localizes citizen participation, through membership, in the heart of citizenship practices (B.S. Turner). This can be understood as that all kinds of practices and activities can be viewed as citizenship practices and citizenship activities. From this perspective it becomes clear that in the Netherlands, due to the nation-state conceptualisation of citizenship social participation is not acknowledged as part of citizenship. In reverse, the case study learns about the theory of citizenship in practices that that theory functions as a framework that visualises different - sometimes conflicting - notions and practices of citizenship and thereby recognises all these as part of citizenship.
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The authors present the study design and main findings of a quasi-experimental evaluation of the learning efficacy of the Serious Game (SG) 'Hazard Recognition' (HR). The SG-HR is a playable, two-level demonstration version for training supervisors who work at oil and gas drilling sites. The game has been developed with a view to developing a full-blown, game-based training environment for operational safety in the oil and gas industry. One of the many barriers to upscaling and implementing a game for training is the questioned learning efficacy of the game. The authors therefore conducted a study into the game's learning efficacy and the factors that contribute to it. The authors used a Framework for Comparative Evaluation (FCE) of SG, and combined it with the Kowalski model for Hazard Detection and the Noel Burch competence model. Four experimental game sessions were held, two involving 60 professionals working in the oil and gas industry, and two with engineering students and consultants. Relevant constructs were operationalized and data were gathered using pre and post-game questionnaires. The authors conclude that the SG-HR improves players' skills and knowledge on hazard detection and assessment, and it facilitates significant learning efficacy in this topic. The FCE proved very helpful for setting up the evaluation and selecting the constructs.
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Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.
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