Professional Considerations for Audit Risk in Creating Smart Governance in Indonesia

This study aims to determine how different audit considerations determine risk between certified auditors and non-certified auditors. This study uses an empirical study approach using an experimental method. Experiments without control (natural) are carried out through audit cases encountered in the field during the audit process. This study\u27s participants were 52 auditors with two categories, namely 26 participants with certified auditors and 26 participants for non-certified auditors. Data collection using the deployment of instruments after the field audit process is carried out. Based on the one-way analysis of the Annova test data, it shows that there are significant differences in audit considerations in determining risk between certified auditors and non-certified auditors. Certified auditors have more professional competence in providing audit considerations in determining risk. The problem of work culture for uncertified auditors is a residual risk that is difficult to detect and can lead to audit failure. Professional certified auditors\u27 competence in providing risk considerations will encourage the formation of smart governance, given that auditors are a synergistic catalyst in organizational processes. Practical recommendations to the leadership of a government institution, especially state universities in Indonesia, that the internal auditors in a university, together with the leadership and the authorized departments, need to establish a risk map in a university leadership provision auditors. To always improve competence in the form of expertise certification training in the audit field. Professional judgment in determining audit risk by looking deeper into the theory of judgment decision making (Connolly, Arkes, and Hammond, 2000). The research approach used a study conducted by Fukukawa and Mock (2011). This study uses internal auditors at state universities in Indonesia, where an experiment is designed to determine internal audit risk. The determination of audit risk will ensure that the audit is right on target, effective, and efficient

Evidential Markov chains and trees with applications to non stationary processes segmentation

The triplet Markov chains (TMC) generalize the pairwise Markov chains (PMC), and the latter generalize the hidden Markov chains (HMC). Otherwise, in an HMC the posterior distribution of the hidden process can be viewed as a particular case of the so called "Dempster's combination rule" of its prior Markov distribution p with a probability q defined from the observations. When we place ourselves in the theory of evidence context by replacing p by a mass function m, the result of the Dempster's combination of m with q generalizes the conventional posterior distribution of the hidden process. Although this result is not necessarily a Markov distribution, it has been recently shown that it is a TMC, which renders traditional restoration methods applicable. Further, these results remain valid when replacing the Markov chains with Markov trees. We propose to extend these results to Pairwise Markov trees. Further, we show the practical interest of such combination in the unsupervised segmentation of non stationary hidden Markov chains, with application to unsupervised image segmentation.Les chaînes de Markov Triplet (CMT) généralisent les chaînes de Markov Couple (CMCouple), ces dernières généralisant les chaînes de Markov cachées (CMC). Par ailleurs, dans une CMC la loi a posteriori du processus caché, qui est de Markov, peut être vue comme une combinaison de Dempster de sa loi a priori p avec une probabilité q définie à partir des observations. Lorsque l'on se place dans le contexte de la théorie de l'évidence en remplaçant p par une fonction de masse m, sa combinaison de Dempster avec q généralise ainsi la probabilité a posteriori. Bien que le résultat de cette fusion ne soit pas nécessairement une chaîne de Markov, il a été récemment établi qu'il est une CMT, ce qui autorise les divers traitements d'intérêt. De plus, les résultats analogues restent valables lorsque l'on généralise les différentes chaînes de Markov aux arbres de Markov. Nous proposons d'étendre ces résultats aux arbres de Markov Couple, dans lesquels la loi du processus caché n'est pas nécessairement de Markov. Nous montrons également l'intérêt pratique de ce type de fusion dans la segmentation non supervisée des chaînes de Markov non stationnaires, avec application à la segmentation d'images

A Comparison of Methods for Transforming Belief Function Models to Probability Models

Recently, we proposed a new method called the plausibility transformation method to convert a belief function model to an equivalent probability model. In this paper, we compare the plausibility transformation method with the pignistic transformation method. The tw