Auditable Augmented/Mixed/Virtual Reality

Virtual, Augmented and Mixed Reality (XR) technologies are becoming increasingly pervasive. However, the contextual nature of XR, and its tight coupling of the digital and physical environments, brings real propensity for loss and harm. This means that auditability---the ability to inspect how a system operates---will be crucial for dealing with incidents as they occur, by providing the information enabling rectification, repair and recourse. However, supporting audit in XR brings considerations, as the process of capturing audit data itself has implications and challenges, both for the application (e.g., overheads) and more broadly. This paper explores the practicalities of auditing XR systems, characterises the tensions between audit and other considerations, and argues the need for flexible tools enabling the management of such. In doing so, we introduce Droiditor, a configurable open-source Android toolkit that enables the runtime capture of audit-relevant data from mobile applications. We use Droiditor as a means to indicate some potential implications of audit data capture, demonstrate how greater configurability can assist in managing audit-related concerns, and discuss the potential considerations that result. Given the societal demands for more transparent and accountable systems, our broader aim is to draw attention to auditability, highlighting tangible ways forward and areas for future work.

The Epidemiology of Neurological Complications in Adults With Sickle Cell Disease: A Retrospective Cohort Study

Introduction: Risk factors for neurological complications in sickle cell disease differ in the adult and pediatric populations. Here, we focused on neurological complications in adults with sickle cell disease.Methods: Patients were selected using the audit data from the St George's Hospital Red Cell Database. The genotyping, demographics, clinical data, and investigation findings were collected.Results: A total of 303 patients were enrolled in the study: hemoglobin S homozygosity (HbSS) genotype 56%, hemoglobin S and C coinheritance (HbSC) genotype 35%, and hemoglobin S and β-thalassemia coinheritance (HbSβ) thalassemia genotype 9%; the mean age was 38.8 years (±13.5 SD) with 46% males. The most common neurological complication was cerebrovascular disease (n = 37, 12%) including those with ischemic stroke (10%), cerebral vasculopathy (3%), and intracranial hemorrhage (1%). Ischemic stroke was common among the HbSS genotype compared with other genotypes (8 vs. 1.6%, p = 0.001). Comparing the patients with sickle cell disease who had suffered a stroke to those who had not, there was a higher proportion of intracranial vasculopathy (p = 0.001, in particular, Moyamoya) and cognitive dysfunction (p < 0.0001).Conclusion: Our cohort supports previous reports that the most common neurological complication in adult sickle cell patients is cerebrovascular disease. Strategies to prevent cerebral vasculopathy and cognitive impairment should be explored.

AUDIT DATA ANALYTICS DENGAN MS-EXCEL: USE CASE PEMERIKSAAN PAJAK

Dalam pemeriksaan pajak , kegiatan utama yang dilaksanakan adalah pengujian bukti. Pengujian bukti ini dilakukan dengan melakukan pengolahan data yang diterima dari Wajib Pajak. Isu yang berkembang adalah data yang diolah dari Wajib Pajak tidak hanya berupa data dalam bentuk fisik, akan tetapi juga berupa data elektronik seperti pembukuan general ledger (buku besar). Proses pengolahan data elektronik ini menjadi sangat penting pada masa sekarang. Disiplin ilmu pemeriksaan pun ikut berkembang dengan adanya istilah Audit Data Analytics yang merupakan bagian dari Teknik Audit Berbantuan Komputer untuk melakukan pengolahan data elektronik tersebut. Penelitian ini menggunakan metode design science research mengajukan temuan artefak yang berkaitan dengan model dan instantiasi (instantiation) MS-Excel untuk implementasi ADA. Hasil penelitian ini diharapkan dapat menjadi tambahan referensi dalam pemeriksaan pajak berupa artefak dalam bentuk instantiasi penggunaan MS-Excel untuk melakukan data analytics dengan sumber dari general ledger wajib pajak.

Improvement of Association Algorithm and Its Application in Audit Data Mining

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models.

Research on Audit Data Analysis and Decision Tree Algorithm for Benefit Distribution of Enterprise Financing Alliance

When traditional methods analyze the audit data of enterprise financing alliance, there are some problems, such as long algorithm modeling time and low accuracy of interest distribution algorithm of enterprise financing alliance. Therefore, this paper proposes an analysis method of interest distribution of enterprise audit data financing alliance based on the decision tree algorithm. The audit data collection process of enterprise financing alliance is given, and the continuous attributes of audit data are discretized by the C4.5 algorithm. We perform enterprise financing alliance audit data analysis, remove inconsistencies from audit data through data cleaning, and finally realize enterprise financing alliance audit data analysis based on the improved C4.5 algorithm. The experimental results show that this method can shorten the modeling time and improve the accuracy of interest distribution algorithm of enterprise financing alliance. We achieved an average accuracy of 84.7% with the C4.5 algorithm while 84.35% with NBTree.

ONS Data on COVID-19 deaths in children is reliable, and increasingly of concern (Re: "Deaths in children and young people in England after SARS-CoV-2 infection during the first pandemic year")

The recently published “Deaths in children and young people in England after SARS-CoV-2 infection during the first pandemic year” attempts to unpick the issue of paediatric deaths “with” versus “from” COVID-19, additionally reporting on pre-existing comorbidities of the children and young people (CYP) who died after a COVID-19 diagnosis. Linking data from the National Child Mortality Database (NCMD), hospital data from the Secondary Uses Service (SUS), and PICU audit data, the authors have re-examined deaths in CYP after a COVID-19 diagnosis using these datasets. However, whilst data on any pre-existing conditions may be useful to identify the children at highest risk from COVID-19, we have some concerns around the methodology and presentation of the first part of this study.

Audit Data Analysis and Application Based on Correlation Analysis Algorithm

Traditional audit data analysis algorithms have many shortcomings, such as the lack of means to mine the hidden audit clues behind the data, the difficulty of finding increasingly hidden cheating techniques caused by the electronic and networked environment, and the inability to solve the quality defects of the audited data. Correlation analysis algorithm in data mining technology is an effective means to obtain knowledge from massive data, which can complete, muffle, clean, and reduce defective data and then can analyze massive data and obtain audit trails under the guidance of expert experience or analysts. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounds the research status and significance of audit data analysis and application; elaborates the development background, current status, and future challenges of correlation analysis algorithm; introduces the methods and principles of data model and its conversion and audit model construction; conducts audit data collection and cleaning; implements audit data preprocessing and its algorithm description; performs audit data analysis based on correlation analysis algorithm; analyzes the hidden node activation value and audit rule extraction in correlation analysis algorithm; proposes the application of audit data based on correlation analysis algorithm; discusses the relationship between audit data quality and audit risk; and finally compares different data mining algorithms in audit data analysis. The findings demonstrate that by analyzing association rules, the correlation analysis algorithm can determine the significance of a huge quantity of audit data and characterise the degree to which linked events would occur concurrently or sequentially in a probabilistic manner. The correlation analysis algorithm first inputs the collected audit data through preprocessing module to filter out useless data and then organizes the obtained data into a format that can be recognized by data mining algorithm and executes the correlation analysis algorithm on the sorted data; finally, the obtained hidden data is divided into normal data and suspicious data by comparing it with the pattern in the rule base. The algorithm can conduct in-depth analysis and research on the company’s accounting vouchers, account books, and a large number of financial accounting data and other data of various natures in the company’s accounting vouchers; reveal its original characteristics and internal connections; and turn it into an audit. People need more direct and useful information. The study results of this paper provide a reference for further researches on audit data analysis and application based on correlation analysis algorithm.

Linkage of National Congenital Heart Disease Audit data to hospital, critical care and mortality national datasets to enable research focused on quality improvement

Objectives To link five national datasets (three registries, two administrative) and create longitudinal health care trajectories for patients with congenital heart disease (CHD), describing the quality and the summary statistics of the linked dataset. Design Bespoke linkage of record-level patient identifiers across five national datasets. Generation of spells of care defined as periods of time-overlapping events across the datasets. Setting National congenital heart disease audit (NCHDA) procedures in public (NHS) hospitals in England and Wales, paediatric and adult intensive care datasets (PICANet and ICNARC-CMP), administrative hospital episodes (HES inpatient, outpatient, A&E), and mortality registry data. Participants Patients with any CHD procedure recorded in NCHDA between April 2000 and March 2017 from public hospitals. Primary and secondary outcome measures Primary outcomes: Number of linked records, number of unique patients and number of generated spells of care (e.g. inpatient stays, outpatient appointments). Secondary outcomes: Quality and completeness of linkage. Results There were 143,862 records in NCHDA relating to 96,041 unique patients. We identified 65,797 linked PICANet patient admissions, 4,664 linked ICNARC-CMP admissions, and over 6 million linked HES episodes of health care (1.1M Inpatient, 4.7M Outpatient). The 96,041 unique patients had 4,908,153 spells of care comprising 6,481,600 records after quality checks. Considering only years where datasets overlapped, 95.6% surgical procedure records were linked to a corresponding HES record, 93.9% paediatric (cardiac) surgery procedure records were linked to a corresponding PICANet admission, and 76.8% adult surgery procedure records were linked to a corresponding ICNARC-CMP record. Conclusions We successfully linked four national datasets to the core dataset of all CHD procedures performed between 2000 and 2017. This will enable a much richer analysis of longitudinal patient journeys and outcomes. We hope that our detailed description of the linkage process will be useful to others looking to link national datasets to address important research priorities.