Is conditional conservatism a source of deviations of financial statements from Benford's Law?

PurposeIn this paper, the authors examine the association between conditional conservatism and deviations of the first digits of financial statement items from what are expected by Benford's Law.Design/methodology/approachThis research uses data of companies listed on the London Stock Exchange. The authors measure deviations of first digits from Benford's Law following Amiram et al. (2015) and firm-year conditional conservatism following previous studies (Basu, 1997; Khan and Watts, 2009; García Lara et al., 2016). The authors use multiple regressions to provide evidence for their hypothesis.FindingsThe results show that conditional conservatism is positively associated with deviations from Benford's Law. The findings are robust across different measures of deviations and conditional conservatism. Also, the authors find that the relationship between deviations from Benford's Law and conditional conservatism is more pronounced for firms with debt issuance, and for leveraged firms facing financial distress. Next, the authors’ analyses confirm previous evidence by showing that the first digits of financial statement items of UK listed companies conform to Benford's Law at the firm-specific level and the market level, and deviations of income statements are larger than those of balance sheets and cash flow statements.Research limitations/implicationsThe research makes significant contributions to the literature. First, this is the first study that provides empirical evidence suggesting that conditional conservatism may be a source of deviations from Benford’s Law. Second, the authors provide evidence confirming previous US findings (e.g. Amiram et al., 2015) showing that the distributions of first digits of financial statement items of UK listed companies also conform to Benford's Law.Practical implicationsThe authors’ findings have implications for auditors. Auditors should be aware of “false positive” for material misstatements when using Benford's Law as a risk assessment procedure. While both conditional conservatism and earnings management are related to deviations from Benford's Law, conservatism-related biases could indicate less audit risks.Originality/valueThe authors provide new and original evidence suggesting that conditional conservatism is related to deviations from Benford's Law.

An Assessment Framework for PV Parallel MPPT Configuration with a New Utilization for UIPS Loads

The prevalence rate of photovoltaics (PV)-based generation systems has increased by more than 15 folds in the last decade, putting it on the top compared to any other power generation system from the expandability point of view. A portion of this huge expansion serves to energize standalone remote areas. Seeking improvements from different aspects of PV systems has been the focus of many studies. In the track of these improvements, parallel MPPT configuration for PV standalone systems have been introduced in the literature as an alternative to a series configuration to improve the overall efficiency of standalone PV systems. However, this efficiency improvement of the parallel MPPT configuration over the series one is not valid for any standalone application, therefore an assessment procedure is required to determine the most efficient MPPT configuration for different standalone applications. Therefore, in this study, an assessment procedure of parallel MPPT is conducted to demonstrate the suitability of utilizing such a configuration compared to series one, based on load daytime energy contributions. This assessment will help PV system designers to determine which MPPT configuration should be selected for applications under study. Furthermore, a new utilization of parallel MPPT configuration is introduced for operating universal input power supply (UIPS) loads to eliminate the inverter stage, thereby increasing the overall system efficiency and reliability. Finally, a systematic procedure to size the complete system is introduced and reinforced by a sizing example.

Digital fundus image quality assessment

Diabetic retinopathy (DR) is a disease caused by complications of diabetes. It starts asymptomatically and can end in blindness. To detect it, doctors use special fundus cameras that allow them to register images of the retina in the visible range of the spectrum. On these images one can see features, which determine the presence of DR and its grade. Researchers around the world are developing systems for the automated analysis of fundus images. At present, the level of accuracy of classification of diseases caused by DR by systems based on machine learning is comparable to the level of qualified medical doctors.The article shows variants for representation of the retina in digital images by different cameras. We define the task to develop a universal approach for the image quality assessment of a retinal image obtained by an arbitrary fundus camera. It is solved in the first block of any automated retinal image analysis system. The quality assessment procedure is carried out in several stages. At the first stage, it is necessary to perform binarization of the original image and build a retinal mask. Such a mask is individual for each image, even among the images recorded by one camera. For this, a new universal retinal image binarization algorithm is proposed. By analyzing result of the binarization, it is possible to identify and remove imagesoutliers, which show not the retina, but other objects. Further, the problem of no-reference image quality assessment is solved and images are classified into two classes: satisfactory and unsatisfactory for analysis. Contrast, sharpness and possibility of segmentation of the vascular system on the retinal image are evaluated step by step. It is shown that the problem of no-reference image quality assessment of an arbitrary fundus image can be solved.Experiments were performed on a variety of images from the available retinal image databases.

Data privacy during pandemics: a systematic literature review of COVID-19 smartphone applications

Background On January 8, 2020, the Centers for Disease Control and Prevention officially announced a new virus in Wuhan, China. The first novel coronavirus (COVID-19) case was discovered on December 1, 2019, implying that the disease was spreading quietly and quickly in the community before reaching the rest of the world. To deal with the virus’ wide spread, countries have deployed contact tracing mobile applications to control viral transmission. Such applications collect users’ information and inform them if they were in contact with an individual diagnosed with COVID-19. However, these applications might have affected human rights by breaching users’ privacy. Methodology This systematic literature review followed a comprehensive methodology to highlight current research discussing such privacy issues. First, it used a search strategy to obtain 808 relevant papers published in 2020 from well-established digital libraries. Second, inclusion/exclusion criteria and the snowballing technique were applied to produce more comprehensive results. Finally, by the application of a quality assessment procedure, 40 studies were chosen. Results This review highlights privacy issues, discusses centralized and decentralized models and the different technologies affecting users’ privacy, and identifies solutions to improve data privacy from three perspectives: public, law, and health considerations. Conclusions Governments need to address the privacy issues related to contact tracing apps. This can be done through enforcing special policies to guarantee users privacy. Additionally, it is important to be transparent and let users know what data is being collected and how it is being used.

Teacher Behavior Checklist e Implicit Relational Assessment Procedure na avaliação de professores

A avaliação de professores tem sido conduzida com instrumentos como o Teacher Behavior Checklist (TBC) cuja evidência de validade de conteúdo foi obtida por relato verbal. A relação entre seis itens do TBC e o estímulo “Bom Professor” foi avaliada com o IRAP, e foi avaliada a correlação entre a nota dos participantes em uma disciplina da graduação e a avaliação do professor. Participaram 40 estudantes universitários que responderam aos dois instrumentos e informaram a nota recebida na disciplina. As médias dos D-IRAP escores foram estatisticamente significativas para “bom professor-positivo-verdadeiro” e “mau professor-negativo-verdadeiro”. Os índices de correlações entre as notas dos participantes na disciplina e a avaliação do docente não foram estatisticamente significativos.

PART 2. ASSESSMENT OF RATIONALITY BY THE POINT METHOD MINING LOGISTICS SYSTEMS

The article formulates the second part (stages 5-7) of the stage-by-stage assessment procedure using the method of a point scale of levels of social rationality of a real or developed (current, implemented, planned for implementation) integrated logistics system of the mining industry.

PART 1. ASSESSMENT OF RATIONALITY BY THE POINT METHOD MINING LOGISTICS SYSTEMS

The article formulates the first part (stages 1-4) of the stage-by-stage assessment procedure using the method of a point scale of levels of social rationality of a real or developed (current, implemented, planned for implementation) integrated logistics system of the mining industry

HIGHER EDUCATION LEARNERS’ ATTITUDES TOWARDS EMERGENCY ONLINE INSTRUCTION DURING COVID-19 PANDEMIC: THE CONTEXT OF TURKEY

This mixed-methods study aimed at investigating the Turkish higher education learners’ attitudes towards Emergency Online Teaching (EOT) under the Covid-19 pandemic in order to discover the benefits and drawbacks of it. The participants were 251 higher education learners who received the EOT during the Covid-19 crisis in Turkey. Both qualitative and quantitative data were gathered by means of a questionnaire in August 2020. Quantitative data were obtained via closed-ended questions with the response on a Likert-scale format. Qualitative data were acquired through open-ended questions. The results showed that the hurried shift to an online instruction by universities in Turkey was not fully satisfactory and the majority of the respondents (74.1%) preferred face-to-face learning to the online format, however, the participants also stated that they felt safer during this pandemic disease thanks to the availability of distant online education. The drawbacks they mentioned included inadequate technological infrastructure or facilities, lack of sufficient teacher-student and peer interaction, lack of learners’ attention and concentration, tediousness of online lessons, learners’ inadequate engagement in class activities, as well as the absence of comprehensive assessment procedure.

Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques

Recent developments in computer hardware made it possible to assess the viability of permutation-based approaches in image classification. Such approaches sample a reference dataset multiple times in order to train an arbitrary number of machine learning models while assessing their accuracy. So-called iterative accuracy assessment techniques or Monte-Carlo-based approaches can be a useful tool when it comes to assessment of algorithm/model performance but are lacking when it comes to actual image classification and map creation. Due to the multitude of models trained, one has to somehow reason which one of them, if any, should be used in the creation of a map. This poses an interesting challenge since there is a clear disconnect between algorithm assessment and the act of map creation. Our work shows one of the ways this disconnect can be bridged. We calculate how often a given pixel was classified as given class in all variations of a multitude of post-classification images delivered by models trained during the iterative assessment procedure. As a classification problem, a mapping of Calamagrostis epigejos, Rubus spp., Solidago spp. invasive plant species using three HySpex hyperspectral datasets collected in June, August and September was used. As a classification algorithm, the support vector machine approach was chosen, with training hyperparameters obtained using a grid search approach. The resulting maps obtained F1-scores ranging from 0.87 to 0.89 for Calamagrostis epigejos, 0.89 to 0.97 for Rubus spp. and 0.99 for Solidago spp.