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Artificial intelligence and auditing in small- and medium-sized firms : Expectations and applications
(2022-09-01) Rikhardsson, Pall; Thórisson, Kristinn R.; Bergthorsson, Gudmundur; Batt, Catherine; Department of Computer Science
Auditing is a field of expertise often mentioned as being ripe for automation using artificial intelligence methods at all levels of operations. Primarily, the application of artificial intelligence (AI) in the auditing profession is done by and for large organizations, leveraging large datasets. While AI approaches for big data are continually improving, methods for small data are scarce. Yet most firms in the world employ fewer than 50 people and can, therefore, rarely rely on big data for automation. In our study, we ask auditors, who mainly audit SMEs, about their expectations towards the impact of AI on the auditing profession and where they expect it to provide the most value when it comes to auditing SMEs. We find that these auditors expect significant improvements in their own efficiency on the job, that learning to use AI applications will not be a challenge for them, and that the use of AI in auditing firms will become mandatory in the future. They expect the performance of certain tasks to become AI-augmented, including risk assessment of individual transactions, conducting audit interviews, performing all manners of analysis, writing confirmation letters, performing the final verification of annual reports, and performing physical observations. Considering these results, we discuss the potential impact of these developments, such as how AI could make the auditing process more effective and efficient but also how AI could lead to an even higher concentration of the auditing service industry.
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COVID-19 infection, vaccine status, and avoidance behaviors in adults with attention deficit and hyperactivity disorder : A cross-sectional study
(2022-08-24) Kilic, Ozge; Boylu, Muhammed Emin; Karakaya-Erdur, Sila; Suma-Berberoglu, Merve; Gudjonsson, Gisli; Young, Susan; Deveci, Erdem; Kirpinar, Ismet
Objective: We aim to examine infection risk and vaccine status of COVID-19 in attention deficit and hyperactivity disorder and evaluate the impact of demographic, clinical, and COVID-19-related factors on the infection status and behavioral avoidance of COVID-19. Methods: This cross-sectional study assessed adults with attention deficit and hyperactivity disorder recruited from an outpatient psychiatry clinic. Patients and healthy controls completed a survey on sociodemographic data, COVID-19 infection status, and vaccine status. COVID-19 Disease Perception Scale, COVID-19 Avoidance Attitudes Scale, Attitudes toward COVID-19 Vaccine Scale, Adult Attention Deficit and Hyperactivity Disorder Self-report Screening Scale for DSM-5, Adult Attention Deficit and Hyperactivity Disorder Self-Report Scale Symptoms Checklist, Patient Health Questionnaire-9, and State-Trait Anxiety Inventory were applied. Results: Ninety patients and 40 healthy controls participated. Patients did not differ from controls in COVID-19 infection and vaccine status, and behavioral avoidance of COVID-19. No demographic and clinical factor significantly affected the COVID-19 infection status. Patients scored higher than controls in the perception of COVID-19 as contagious (p = 0.038), cognitive avoidance of COVID-19 (p = 0.008), and positive attitudes toward the COVID-19 vaccine (p = 0.024). After adjustment of possible factors, a positive perception of the COVID-19 vaccine and a perception of COVID-19 as dangerous were the two factors significantly affecting behavioral avoidance of COVID-19 [R2 = 0. 17, F(2) = 13.189, p < 0.0001]. Conclusion: Infection and vaccine status of COVID-19 in patients did not significantly differ from controls. No demographic and clinical factor significantly affected the COVID-19 infection status. Approximately four-fifths of the patients were fully vaccinated as recommended by national and global health organizations. This has increased the knowledge base showing that the COVID-19 vaccine is acceptable and receiving the vaccine is endorsed by ADHD patients. Attention deficit and hyperactivity disorder itself may provoke no kind of mental disturbance in sense of perception of the danger of this disease. Our findings have increased the knowledge base showing that the COVID-19 vaccine is acceptable and the actual practice of receiving the vaccine is endorsed in this population. Our message for practice would be to take into account not only the core symptoms and the comorbidities of the disorder but also the perception of the disease while exploring its link with COVID-19.
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Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients
(2022-09) Duprez, Joan; Tabbal, Judie; Hassan, Mahmoud; Modolo, Julien; Kabbara, Aya; Mheich, Ahmad; Drapier, Sophie; Vérin, Marc; Sauleau, Paul; Wendling, Fabrice; Benquet, Pascal; Houvenaghel, Jean François
Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.
Verk
The Complexity of Periodic Energy Minimisation
(Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2022-08-01) Adamson, Duncan; Deligkas, Argyrios; Gusev, Vladimir V.; Potapov, Igor; Szeider, Stefan; Ganian, Robert; Silva, Alexandra
The computational complexity of pairwise energy minimisation of N points in real space is a longstanding open problem. The idea of the potential intractability of the problem was supported by a lack of progress in finding efficient algorithms, even when restricted the integer grid approximation. In this paper we provide a firm answer to the problem on Zd by showing that for a large class of pairwise energy functions the problem of periodic energy minimisation is NP-hard if the size of the period (known as a unit cell) is fixed, and is undecidable otherwise. We do so by introducing an abstraction of pairwise average energy minimisation as a mathematical problem, which covers many existing models. The most influential aspects of this work are showing for the first time: 1) undecidability of average pairwise energy minimisation in general 2) computational hardness for the most natural model with periodic boundary conditions, and 3) novel reductions for a large class of generic pairwise energy functions covering many physical abstractions at once. In particular, we develop a new tool of overlapping digital rhombuses to incorporate the properties of the physical force fields, and we connect it with classical tiling problems. Moreover, we illustrate the power of such reductions by incorporating more physical properties such as charge neutrality, and we show an inapproximability result for the extreme case of the 1D average energy minimisation problem.
Verk
A Generic Solution to Register-Bounded Synthesis with an Application to Discrete Orders
(Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2022-07-01) Exibard, Léo; Filiot, Emmanuel; Khalimov, Ayrat; Bojanczyk, Mikolaj; Merelli, Emanuela; Woodruff, David P.
We study synthesis of reactive systems interacting with environments using an infinite data domain. A popular formalism for specifying and modelling such systems is register automata and transducers. They extend finite-state automata by adding registers to store data values and to compare the incoming data values against stored ones. Synthesis from nondeterministic or universal register automata is undecidable in general. However, its register-bounded variant, where additionally a bound on the number of registers in a sought transducer is given, is known to be decidable for universal register automata which can compare data for equality, i.e., for data domain (ℕ,=). This paper extends the decidability border to the domain (ℕ,<) of natural numbers with linear order. Our solution is generic: we define a sufficient condition on data domains (regular approximability) for decidability of register-bounded synthesis. The condition is satisfied by natural data domains like (ℕ,<). It allows one to use simple language-theoretic arguments and avoid technical game-theoretic reasoning. Further, by defining a generic notion of reducibility between data domains, we show the decidability of synthesis in the domain (ℕd,

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