A cybersickness review: causes, strategies, and classification methods

Virtual reality (VR) and head-­mounted displays are continually gaining popularity in various fields such as education, military, entertainment, and health. Although such technologies provide a high sense of immersion, they can also trigger symptoms of discomfort. This condition is called cybersickness (CS) and is quite popular in recent virtual reality research. In this work we first present a review of the literature on theories of discomfort manifestations usually attributed to virtual reality environments. Following, we reviewed existing strategies aimed at minimizing CS problems and discussed how the CS measurement has been conducted based on subjective, bio­signal (or objective), and users profile data. We also describe and discuss related works that are aiming to mitigate cybersickness problems using deep and symbolic machine learning approaches. Although some works used methods to make deep learning explainable, they are not strongly affirmed by literature. For this reason in this work we argue that symbolic classifiers can be a good way to identify CS causes, once they possibilities human-­readability which is crucial for analyze the machine learning decision paths. In summary, from a total of 157 observed studies, 24 were excluded. Moreover, we believe that this work facilitates researchers to identify the leading causes for most discomfort situations in virtual reality environments, associate the most recommended strategies to minimize such discomfort, and explore different ways to conduct experiments involving machine learning to overcome cybersickness.

Corporate Decision-Making for Arts Sponsorship

<p><b>Sponsorship is a crucial revenue stream for many non-profit arts organisations. At the same time businesses appear to be viewing sponsorship and philanthropy as an ever more strategic activity, yet little is known about the actual decision-making processes these companies undergo in considering arts sponsorships.</b></p> <p>Examination of sponsorship and philanthropy literature revealed that an opportunity existed to study these processes in more depth, and the research question was posed: How do companies make decisions when considering arts sponsorship? Literature from Organisational Buying Behaviour and Decision-making provided lenses by which these processes could be viewed, and a subsequent framework was developed to inform the research.</p> <p>Multiple cases of positive arts sponsorship decisions from within New Zealand were examined. Responses from 24 in-depth interviews resulted in the identification of ten cases for which information was gathered from multiple informants on both sides of the relationship. In addition, ten interviews were categorised as “experts” on the topic of arts sponsorship more generally, and used as a secondary source of data. Within and between case analyses was combined with comparison of expert responses to yield initial results. Taking a theory-building approach, iteration between results, literature and theory served to develop the final findings.</p> <p>This study revealed a number of key themes which characterise these decisions. Firstly, the expectations and perceptions of society, concerning sponsorship, influence stakeholders, companies and individual managers, and subsequently influence these decisions. Secondly, a co-existence of both commercial and philanthropic goals was found within decisions, suggesting that such decisions are not always categorized into one particular area. Thirdly, a key influential role was identified in these decisions as that of an advocate, being a manager who sees the benefit of the sponsorship and essentially makes it happen within the organisation. Fourthly, it was found that these decisions rely on and are influenced in part by individual intuition, based on personal and professional experience, and serving to pave the way for a type of informedhappenstance, necessary for the decisions to progress. While three decision paths were noted in this study, a general decision process was proposed which would vary based on many of the characteristics above.</p> <p>Overall, this study has contributed to sponsorship and philanthropy literature in revealing arts sponsorship decisions to be complex, with managers influenced by expectations and perceptions of society, commercial and philanthropic goals, individual and company frames, and intuitive and economic justifications. In conclusion, propositions and suggestions for future research are proposed, along with implications for managers in both arts organisations and sponsoring businesses.</p>

Corporate Decision-Making for Arts Sponsorship

<p><b>Sponsorship is a crucial revenue stream for many non-profit arts organisations. At the same time businesses appear to be viewing sponsorship and philanthropy as an ever more strategic activity, yet little is known about the actual decision-making processes these companies undergo in considering arts sponsorships.</b></p> <p>Examination of sponsorship and philanthropy literature revealed that an opportunity existed to study these processes in more depth, and the research question was posed: How do companies make decisions when considering arts sponsorship? Literature from Organisational Buying Behaviour and Decision-making provided lenses by which these processes could be viewed, and a subsequent framework was developed to inform the research.</p> <p>Multiple cases of positive arts sponsorship decisions from within New Zealand were examined. Responses from 24 in-depth interviews resulted in the identification of ten cases for which information was gathered from multiple informants on both sides of the relationship. In addition, ten interviews were categorised as “experts” on the topic of arts sponsorship more generally, and used as a secondary source of data. Within and between case analyses was combined with comparison of expert responses to yield initial results. Taking a theory-building approach, iteration between results, literature and theory served to develop the final findings.</p> <p>This study revealed a number of key themes which characterise these decisions. Firstly, the expectations and perceptions of society, concerning sponsorship, influence stakeholders, companies and individual managers, and subsequently influence these decisions. Secondly, a co-existence of both commercial and philanthropic goals was found within decisions, suggesting that such decisions are not always categorized into one particular area. Thirdly, a key influential role was identified in these decisions as that of an advocate, being a manager who sees the benefit of the sponsorship and essentially makes it happen within the organisation. Fourthly, it was found that these decisions rely on and are influenced in part by individual intuition, based on personal and professional experience, and serving to pave the way for a type of informedhappenstance, necessary for the decisions to progress. While three decision paths were noted in this study, a general decision process was proposed which would vary based on many of the characteristics above.</p> <p>Overall, this study has contributed to sponsorship and philanthropy literature in revealing arts sponsorship decisions to be complex, with managers influenced by expectations and perceptions of society, commercial and philanthropic goals, individual and company frames, and intuitive and economic justifications. In conclusion, propositions and suggestions for future research are proposed, along with implications for managers in both arts organisations and sponsoring businesses.</p>

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Fault detection and diagnosis (FDD) has received considerable attention with the advent of big data. Many data-driven FDD procedures have been proposed, but most of them may not be accurate when data missing occurs. Therefore, this paper proposes an improved random forest (RF) based on decision paths, named DPRF, utilizing correction coefficients to compensate for the influence of incomplete data. In this DPRF model, intact training samples are firstly used to grow all the decision trees in the RF. Then, for each test sample that possibly contains missing values, the decision paths and the corresponding nodes importance scores are obtained, so that for each tree in the RF, the reliability score for the sample can be inferred. Thus, the prediction results of each decision tree for the sample will be assigned to certain reliability scores. The final prediction result is obtained according to the majority voting law, combining both the predicting results and the corresponding reliability scores. To prove the feasibility and effectiveness of the proposed method, the Tennessee Eastman (TE) process is tested. Compared with other FDD methods, the proposed DPRF model shows better performance on incomplete data.

Geospatial Decision-Making Framework Based on the Concept of Satisficing

Decision-making methods used in geospatial decision making are computationally complex prescriptive methods, the details of which are rarely transparent to the decision maker. However, having a deep understanding of the details and mechanisms of the applied method is a prerequisite for the efficient use thereof. In this paper, we present a novel decision-making framework that emanates from the need for intuitive and easy-to-use decision support systems for geospatial multi-criteria decision making. The framework consists of two parts: the decision-making model Even Swaps on Reduced Data Sets (ESRDS), and the interactive visualization framework. The decision-making model is based on the concept of satisficing, and as such, it is intuitive and easy to understand and apply. It integrates even swaps, a prescriptive decision-making method, with the findings of behavioural decision-making theories. Providing visual feedback and interaction opportunities throughout the decision-making process, the interactive visualization part of the framework helps the decision maker gain better insight into the decision space and attribute dependencies. Furthermore, it provides the means to analyse and compare the outcomes of different scenarios and decision paths.

Dynamic Network Pruning with Interpretable Layerwise Channel Selection

Dynamic network pruning achieves runtime acceleration by dynamically determining the inference paths based on different inputs. However, previous methods directly generate continuous decision values for each weight channel, which cannot reflect a clear and interpretable pruning process. In this paper, we propose to explicitly model the discrete weight channel selections, which encourages more diverse weights utilization, and achieves more sparse runtime inference paths. Meanwhile, with the help of interpretable layerwise channel selections in the dynamic network, we can visualize the network decision paths explicitly for model interpretability. We observe that there are clear differences in the layerwise decisions between normal and adversarial examples. Therefore, we propose a novel adversarial example detection algorithm by discriminating the runtime decision features. Experiments show that our dynamic network achieves higher prediction accuracy under the similar computing budgets on CIFAR10 and ImageNet datasets compared to traditional static pruning methods and other dynamic pruning approaches. The proposed adversarial detection algorithm can significantly improve the state-of-the-art detection rate across multiple attacks, which provides an opportunity to build an interpretable and robust model.

Practical Challenges and Methodological Flexibility in Prior Elicitation

The Bayesian statistical framework requires the specification of prior distributions, which reflect pre-data knowledge about the relative plausibility of different parameter values. As prior distributions influence the results of Bayesian analyses, it is important to specify them with care. Prior elicitation has frequently been proposed as a principled method for deriving prior distributions based on expert knowledge. Although prior elicitation provides a theoretically satisfactory method of specifying prior distributions, there are several implicit decisions that researchers need to make at different stages of the elicitation process, each of them constituting important researcher degrees of freedom. Here, we discuss some of these decisions and group them into three categories: decisions about (1) the setup of the prior elicitation; (2) the core elicitation process; and (3) combination of elicited prior distributions from different experts. Importantly, different decision paths could result in greatly varying priors elicited from the same experts. Hence, researchers who wish to perform prior elicitation are advised to carefully consider each of the practical decisions before, during, and after the elicitation process. By explicitly outlining the consequences of these practical decisions, we hope to raise awareness for methodological flexibility in prior elicitation and provide researchers with a more structured approach to navigate the decision paths in prior elicitation. Making the decisions explicit also provides the foundation for further research that can identify evidence-based best practices that may eventually reduce the methodologically flexibility in prior elicitation.

A Recognition Primed Decision Model for Public Consumption of Government Data

The U.S. Census Bureau (Census) collects and maintains data on the nation’s population, neighborhoods, and economy, and shares that information with the public digitally. To successfully provide value to the public, data platforms and the data itself must be constructed in a way that individuals can easily use and understand. However, like many data providers, Census introduces technical terminology that can complicate the user’s experience. This paper describes how such terms affect individuals’ ability to consume data, by applying a recognition primed decision making method. The data used come from 36 in-depth interviews and 1 focus group with data consumers. An empirical model of the recognition-primed decision process uncovered in the data is presented, also elucidating key variations in decision paths according to factors like technical training or experience. These findings illustrate that failing to present user-centered data can limit the ability of consumers to benefit from public information.