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Long-term care facilities are currently installing dynamic lighting systems with the aim to improve the well-being and behaviour of residents with dementia. The aim of this study was to investigate the implementation of dynamic lighting systems from the perspective of stakeholders and the performance of the technology. Therefore, a questionnaire survey was conducted with the management and care professionals of six care facilities. Moreover, light measurements were conducted in order to describe the exposure of residents to lighting. The results showed that the main reason for purchasing dynamic lighting systems lied in the assumption that the well-being and day/night rhythmicity of residents could be improved. The majority of care professionals were not aware of the reasons why dynamic lighting systems were installed. Despite positive subjective ratings of the dynamic lighting systems, no data were collected by the organizations to evaluate the effectiveness of the lighting. Although the care professionals stated that they did not see any large positive effects of the dynamic lighting systems on the residents and their own work situation, the majority appreciated the dynamic lighting systems more than the old situation. The light values measured in the care facilities did not exceed the minimum threshold values reported in the literature. Therefore, it seems illogical that the dynamic lighting systems installed in the researched care facilities will have any positive health effects.
The origins of SWOT analysis have been enigmatic, until now. With archival research, interviews with experts and a review of the available literature, this paper reconstructs the original SOFT/SWOT approach, and draws potential implications. During a firm's planning process, all managers are asked to write down 8 to 10 key planning issues faced by their units. Each manager grades, with evidence, these issues as either safeguarding the Satisfactory; opening Opportunities; fixing Faults; or thwarting Threats: hence SOFT (which is later merely relabeled to Strengths, Weaknesses, Opportunities and Threats, or SWOT). Subgroups of managers have several dialogues about these issues with the instruction to include the needs and expectations of all the firm's stakeholders. Their developed resolutions or proposals become input for the executive planning committee to articulate corporate purpose(s) and strategies. SWOT's originator, Robert Franklin Stewart, emphasized the crucial role that creativity plays in the planning process. The SOFT/SWOT approach curbs mere top-down strategy making to the benefit of strategy alignment and implementation; Introducing digital means to parts of SWOT's original participative, long-range planning process, as suggested herein, could boost the effectiveness of organizational strategizing, communication and learning. Archival research into the deployment of SOFT/SWOT in practice is needed.
The project aim is to improve collusion resistance of real-world content delivery systems. The research will address the following topics: • Dynamic tracing. Improve the Laarhoven et al. dynamic tracing constructions [1,2] [A11,A19]. Modify the tally based decoder [A1,A3] to make use of dynamic side information. • Defense against multi-channel attacks. Colluders can easily spread the usage of their content access keys over multiple channels, thus making tracing more difficult. These attack scenarios have hardly been studied. Our aim is to reach the same level of understanding as in the single-channel case, i.e. to know the location of the saddlepoint and to derive good accusation scores. Preferably we want to tackle multi-channel dynamic tracing. • Watermarking layer. The watermarking layer (how to embed secret information into content) and the coding layer (what symbols to embed) are mostly treated independently. By using soft decoding techniques and exploiting the “nuts and bolts” of the embedding technique as an extra engineering degree of freedom, one should be able to improve collusion resistance. • Machine Learning. Finding a score function against unknown attacks is difficult. For non-binary decisions there exists no optimal procedure like Neyman-Pearson scoring. We want to investigate if machine learning can yield a reliable way to classify users as attacker or innocent. • Attacker cost/benefit analysis. For the various use cases (static versus dynamic, single-channel versus multi-channel) we will devise economic models and use these to determine the range of operational parameters where the attackers have a financial benefit. For the first three topics we have a fairly accurate idea how they can be achieved, based on work done in the CREST project, which was headed by the main applicant. Neural Networks (NNs) have enjoyed great success in recognizing patterns, particularly Convolutional NNs in image recognition. Recurrent NNs ("LSTM networks") are successfully applied in translation tasks. We plan to combine these two approaches, inspired by traditional score functions, to study whether they can lead to improved tracing. An often-overlooked reality is that large-scale piracy runs as a for-profit business. Thus countermeasures need not be perfect, as long as they increase the attack cost enough to make piracy unattractive. In the field of collusion resistance, this cost analysis has never been performed yet; even a simple model will be valuable to understand which countermeasures are effective.
