Specification for minimum information to be included in specification sheets of document copying machines

There is a need to develop an effective data preservation scheme with minimal information loss when the patient’s data are shared in public interest for different research activities. Prior studies have devised different approaches for data preservation in healthcare domains; however, there is still room for improvement in the design of an elegant data preservation approach. With that motivation behind, this study has proposed a medical healthcare-IoTs-based infrastructure with restricted access. The infrastructure comprises two algorithms. The first algorithm protects the sensitivity information of a patient with quantifying minimum information loss during the anonymization process. The algorithm has also designed the access polices comprising the public access, doctor access, and the nurse access, to access the sensitivity information of a patient based on the clustering concept. The second suggested algorithm is K-anonymity privacy preservation based on local coding, which is based on cell suppression. This algorithm utilizes a mapping method to classify the data into different regions in such a manner that the data of the same group are placed in the same region. The benefit of using local coding is to restrict third-party users, such as doctors and nurses, when trying to insert incorrect values in order to access real patient data. Efficiency of the proposed algorithm is evaluated against the state-of-the-art algorithm by performing extensive simulations. Simulation results demonstrate benefits of the proposed algorithms in terms of efficient cluster formation in minimum time, minimum information loss, and execution time for data dissemination.

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All models are wrong, some are useful, but are they reproducible? Commentary on Lee et al. (2019)

10.31234/osf.io/af6w7 ◽

2019 ◽

Author(s):

Michael David Wilson ◽

Russell Boag ◽

Luke Joseph Gough Strickland

Keyword(s):

Gold Standard ◽

Cognitive Modeling ◽

Best Practice ◽

Cognitive Model ◽

Cognitive Models ◽

Reproducible Research ◽

Minimum Information ◽

Starting Point ◽

Bare Minimum ◽

Practical Recommendations

Lee et al. (2019) make several practical recommendations for replicable and useful cognitive modeling. They also point out that the ultimate test of the usefulness of a cognitive model is its ability to solve practical problems. Solution-oriented modeling requires engaging practitioners who understand the relevant applied domain but may lack extensive modeling expertise. In this commentary, we argue that for cognitive modeling to reach practitioners there is a pressing need to move beyond providing the bare minimum information required for reproducibility, and instead aim for an improved standard of transparency and reproducibility in cognitive modeling research. We discuss several mechanisms by which reproducible research can foster engagement with applied practitioners. Notably, reproducible materials provide a starting point for practitioners to experiment with cognitive models and evaluate whether they are suitable for their domain of expertise. This is essential because solving complex problems requires exploring a range of modeling approaches, and there may not be time to implement each possible approach from the ground up. Several specific recommendations for best practice are provided, including the application of containerization technologies. We also note the broader benefits of adopting gold standard reproducible practices within the field.

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On the likelihood of 'encapsulating all uncertainty'

10.31234/osf.io/suf8p ◽

2019 ◽

Author(s):

Kristy Martire ◽

Danielle Navarro ◽

Gary Edmond ◽

Ben R Newell

Keyword(s):

Likelihood Ratio ◽

Human Memory ◽

Minimum Information ◽

Mental Operation

The assignment of personal probabilities to form a forensic practitioner's likelihood ratio is a mental operation subject to all the frailties of human memory, perception and judgment. While we agree that beliefs expressed as coherent probabilities are neither ‘right’ nor ‘wrong’ we argue that debate over this fact obscures both the requirement for and consideration of the ‘helpfulness’ of practitioner's opinayesions. We also question the extent to which a likelihood ratio based on personal probabilities can realistically be expected to ‘encapsulate all uncertainty’. Courts cannot rigorously assess a forensic practitioner's bare assertions of belief regarding evidential strength. At a minimum, information regarding the uncertainty both within and between the opinions of practitioners is required.

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