Richting een geautomatiseerde continuïteitsanalyse

Het beoordelen van de continuïteitsanalyse in het controleproces berust op het professionele oordeel van de controlerend accountant. Om het individuele en persoonlijk oordeel (professional judgement) van de accountant te ondersteunen en willekeur zoveel mogelijk te vermijden, zou een directere bron van informatie, in de vorm van een geautomatiseerde continuïteitsanalyse, ondersteuning kunnen bieden. Met behulp van een combinatie van zestien forecasting algoritmes is een methode ontwikkeld om de continuïteitsanalyse te automatiseren. Ten behoeve van het valideren van de forecasting algoritmes zijn 225 administraties verdeeld in een train- en testset. De resultaten tonen een betrouwbaarheidspercentage van 96,49% voor het Extra Trees Regressor model op basis van de conditie ‘lopende verplichtingen’ voor één van de administraties

Peri-articular histiocytic sarcoma and synovial cell sarcoma in Bernese Mountain dogs : a retrospective investigation of the prevalence of these tumours in association with previously diseased joints

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On the link between perceived parental rearing behaviors and self-conscious emotions in adolescents

Contains fulltext : 170400.pdf (publisher's version ) (Open Access)This study examined relationships between the self-conscious emotions of guilt and shame in both clinical (N = 104) and non-clinical (N = 477) (young) adolescents aged 11-18 years, who completed a questionnaire to assess perceived parental rearing behaviors (EMBU-C) and a scenario-based instrument to measure proneness to guilt and shame (SCEMAS). Results indicated that parental rearing dimensions were positively related to self-conscious emotions. Regarding the non-clinical sample, both favourable (emotional warmth) and unfavourable (rejection) paternal and maternal rearing dimensions were significant correlates of guilt- and shame-proneness. The results for the clinical sample were less conclusive: only maternal emotional warmth and rejection were found to be significantly associated with guilt and shame. Interestingly, no associations between any of the paternal rearing dimensions and self-conscious emotions emerged. Taken together, these results are in keeping with the notion that parental rearing factors are involved in the development of both adaptive and maladaptive self-conscious emotions in adolescents.10 p

A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update

International audienceObjective: Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach: We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results: We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance: This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these Review of Classification Algorithms for EEG-based BCI 2 methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI