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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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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Over the past few years a growing number of artists have critiqued the ubiquity of identity recognition technologies. Specifically, the use of these technologies by state security programs, tech-giants and multinational corporations has met with opposition and controversy. A popular form of resistance to recognition technology is sought in strategies of masking and camouflage. Zach Blas, Leo Selvaggio, Sterling Crispin and Adam Harvey are among a group of internationally acclaimed artists who have developed subversive anti-facial recognition masks that disrupt identification technologies. This paper examines the ontological underpinnings of these popular and widely exhibited mask projects. Over and against a binary understanding and criticism of identity recognition technology, I propose to take a relational turn to reimagine these technologies not as an object for our eyes, but as a relationship between living organisms and things. A relational perspective cuts through dualist and anthropocentric conceptions of recognition technology opening pathways to intersectional forms of resistance and critique. Moreover, if human-machine relationships are to be understood as coming into being in mutual dependency, if the boundaries between online and offline are always already blurred, if the human and the machine live intertwined lives and it is no longer clear where the one stops and the other starts, we need to revise our understanding of the self. A relational understanding of recognition technology moves away from a notion of the self as an isolated and demarcated entity in favour of an understanding of the self as relationally connected, embedded and interdependent. This could alter the way we relate to machines and multiplies the lines of flight we can take out of a culture of calculated settings.
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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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This thesis describes an Action Research (AR) project aimed at the implementation of Evidence Based Practice in a mental health nursing setting in the Netherlands. The main research question addressed in this thesis is: In what way is Action Research with an empowering appropriate to implement Evidence Based Practice in a mental health nursing setting in the Netherlands and what is the effect of this implementation on the care experienced by the client, the nursing interventions and the context in this setting compared to a comparative setting? To answer this main research question, the following questions derived from it were addressed: What is Evidence Based Practice? What is known about implementing evidence-based practice in nursing through Action Research? Which factors have to be dealt with in a mental health nursing setting, so the implementation of EBP with AR with an empowering intent will be more successful? Which factors have to be dealt with in a mental health nursing setting, so the implementation of EBP with AR with an empowering intent will be successful? How is EBP implemented through AR with an empowering intent and what are the outcomes for the use of evidence, the context and the facilitation in the setting? What is the effect of the implementation of EBP in mental health nursing using AR with an empowering intent on the care experienced by the client, the nursing interventions and the context compared to a comparison setting? The first two questions were answered by a search of the literature while the remaining questions were answered during the AR study conducted in two mental health organisations in the Netherlands.
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The aim of this project & work package is to develop a European action plan on mental health at work. A major and essential ingredient for this is the involvement of the relevant stakeholders and sharing experiences among them on the national and member state level. The Dutch Ministries of Health and Social Affairs and Employment have decided to participate in this “joint action on the promotion of mental health and well-being” with a specific focus on the work package directed at establishing a framework for action to promote taking action on mental health and well-being at workplaces at national level as well.
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Abstract Aim: This study protocol aims to examine the effectiveness and preconditions of a self-management program—named REducing Delay through edUcation on eXacerbations (REDUX)—in China. Background: The high disease burden in people with chronic lung disease is mainly due to exacerbations. There is a need for effective exacerbation-management interventions. A nurse-led program, REDUX, helped patients self-manage exacerbations. Design: A single-arm pre-post study. Methods: Fifty-four patients and 24 healthcare professionals (HCPs) in Chinese primary care will be included. The core element of the program is a personalized action plan. HCPs will receive training in using the action plan to help patients manage exacerbations. The intervention will start when a patient is referred to the nurse for a post-exacerbation consultation and ends when the patient presents for the second post-exacerbation consultation. During the first post-exacerbation consultation, the patient and nurse will create the action plan. The primary outcomes in patients will include the delays between the onset of exacerbation and recognition, between exacerbation recognition and action, between exacerbation recognition and consultation with a doctor, and when the patients feel better after receiving medical help from HCPs. The secondary outcomes will include preconditions of the program. The ethics approval was obtained in September 2021. Discussion: This study will discuss a culturally adapted nurse-led self-management intervention for people with chronic lung disease in China. The intervention could help Chinese HCPs provide efficient care and reduce their workload. Furthermore, it will inform future research on tailoring nurse-led self-management interventions in different contexts. Impact: The study will contribute to the evidence on the effectiveness and preconditions of REDUX in China. If effective, the result will assist the nursing of people with chronic lung disease. Trial registration: Registered in the Chinese clinical trial registry (ID: 2100051782).
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In case of a major cyber incident, organizations usually rely on external providers of Cyber Incident Response (CIR) services. CIR consultants operate in a dynamic and constantly changing environment in which they must actively engage in information management and problem solving while adapting to complex circumstances. In this challenging environment CIR consultants need to make critical decisions about what to advise clients that are impacted by a major cyber incident. Despite its relevance, CIR decision making is an understudied topic. The objective of this preliminary investigation is therefore to understand what decision-making strategies experienced CIR consultants use during challenging incidents and to offer suggestions for training and decision-aiding. A general understanding of operational decision making under pressure, uncertainty, and high stakes was established by reviewing the body of knowledge known as Naturalistic Decision Making (NDM). The general conclusion of NDM research is that experts usually make adequate decisions based on (fast) recognition of the situation and applying the most obvious (default) response pattern that has worked in similar situations in the past. In exceptional situations, however, this way of recognition-primed decision-making results in suboptimal decisions as experts are likely to miss conflicting cues once the situation is quickly recognized under pressure. Understanding the default response pattern and the rare occasions in which this response pattern could be ineffective is therefore key for improving and aiding cyber incident response decision making. Therefore, we interviewed six experienced CIR consultants and used the critical decision method (CDM) to learn how they made decisions under challenging conditions. The main conclusion is that the default response pattern for CIR consultants during cyber breaches is to reduce uncertainty as much as possible by gathering and investigating data and thus delay decision making about eradication until the investigation is completed. According to the respondents, this strategy usually works well and provides the most assurance that the threat actor can be completely removed from the network. However, the majority of respondents could recall at least one case in which this strategy (in hindsight) resulted in unnecessary theft of data or damage. Interestingly, this finding is strikingly different from other operational decision-making domains such as the military, police and fire service in which there is a general tendency to act rapidly instead of searching for more information. The main advice is that training and decision aiding of (novice) cyber incident responders should be aimed at the following: (a) make cyber incident responders aware of how recognition-primed decision making works; (b) discuss the default response strategy that typically works well in several scenarios; (c) explain the exception and how the exception can be recognized; (d) provide alternative response strategies that work better in exceptional situations.
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In the current discourses on sustainable development, one can discern two main intellectual cultures: an analytic one focusing on measuring problems and prioritizing measures, (Life Cycle Analysis (LCA), Mass Flow Analysis (MFA), etc.) and; a policy/management one, focusing on long term change, change incentives, and stakeholder management (Transitions/niches, Environmental economy, Cleaner production). These cultures do not often interact and interactions are often negative. However, both cultures are required to work towards sustainability solutions: problems should be thoroughly identified and quantified, options for large change should be guideposts for action, and incentives should be created, stakeholders should be enabled to participate and their values and interests should be included in the change process. The paper deals especially with engineering education. Successful technological change processes should be supported by engineers who have acquired strategic competences. An important barrier towards training academics with these competences is the strong disciplinarism of higher education. Raising engineering students in strong disciplinary paradigms is probably responsible for their diminishing public engagement over the course of their studies. Strategic competences are crucial to keep students engaged and train them to implement long term sustainable solutions.
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Growing research in sign language recognition, generation, and translation AI has been accompanied by calls for ethical development of such technologies. While these works are crucial to helping individual researchers do better, there is a notable lack of discussion of systemic biases or analysis of rhetoric that shape the research questions and methods in the field, especially as it remains dominated by hearing non-signing researchers. Therefore, we conduct a systematic review of 101 recent papers in sign language AI. Our analysis identifies significant biases in the current state of sign language AI research, including an overfocus on addressing perceived communication barriers, a lack of use of representative datasets, use of annotations lacking linguistic foundations, and development of methods that build on flawed models. We take the position that the field lacks meaningful input from Deaf stakeholders, and is instead driven by what decisions are the most convenient or perceived as important to hearing researchers. We end with a call to action: the field must make space for Deaf researchers to lead the conversation in sign language AI.
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