Discussion Paper: Social accountability for students in a machine learning era

Abstract Data-centric disciplines like machine learning and data science have become major research areas within computer science and beyond. However, the development of research processes and tools did not keep pace with the rapid advancement of the disciplines, resulting in several insufficiently tackled challenges to attain reproducibility, replicability, and comparability of achieved results. In this discussion paper, we review existing tools, platforms and standardization efforts for addressing these challenges. As a common ground for our analysis, we develop an open science centred process model for machine learning research, which combines openness and transparency with the core processes of machine learning and data science. Based on the features of over 40 tools, platforms and standards, we list the, in our opinion, 11 most central platforms for the research process in this paper. We conclude that most platforms cover only parts of the requirements for overcoming the identified challenges.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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Pharmacology Issues Related to Dementia Syndrome of Depression in Elderly Residents of Long-Term Care Settings

Perspectives on Neurophysiology and Neurogenic Speech and Language Disorders ◽

10.1044/nnsld25.2.61 ◽

2015 ◽

Vol 25 (2) ◽

pp. 61-69

Author(s):

Angel L. Ball ◽

Adina S. Gray

Keyword(s):

Depressive Symptoms ◽

The Elderly ◽

Environmental Communication ◽

Pharmacological Intervention ◽

Team Approach ◽

Discussion Paper ◽

Term Care ◽

Anticholinergic Medications ◽

Inappropriate Treatment ◽

Dementia Syndrome

Pharmacological intervention for depressive symptoms in institutionalized elderly is higher than the population average. Among the patients on such medications are those with a puzzling mix of symptoms, diagnosed as “dementia syndrome of depression,” formerly termed “pseudodementia”. Cognitive-communicative changes, potentially due to medications, complicate the diagnosis even further. This discussion paper reviews the history of the terminology of “pseudodementia,” and examines the pharmacology given as treatment for depressive symptoms in the elderly population that can affect cognition and communication. Clinicians can reduce the risk of misdiagnosis or inappropriate treatment by having an awareness of potential side effects, including decreased attention, memory, and reasoning capacities, particularly due to some anticholinergic medications. A team approach to care should include a cohesive effort directed at caution against over-medication, informed management of polypharmacology, enhancement of environmental/communication supports and quality of life, and recognizing the typical nature of some depressive signs in elderly institutionalized individuals.

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