Search results

Based on this holistic model, the authors propose and analyze seven key issues related to the admissibility of digital media in cross-border trials considering four Latin American countries.

The authors apply the modeling process of the soft systems methodology by Checkland in order to develop a holistic model focused on human situation problems involving digital media and information technology devices or systems.

The authors discuss the status of the identified key issues in each country and offer a perspective on the integration of cross-border work analyzing the contribution of these key issues to the collaboration between countries criminal cases or the use of foreign digital artifacts in domestic trials.

In this study, the authors assumed that the problems of official interaction between agencies of different countries are considered solved. However, for future studies or research, the authors recommend that these issues can be considered as relevant, since they are related to cross-border cooperation topics that will necessarily require unavoidable official arrangements, agreements and formalities.

This work is aimed at defining and analyzing the key issues that can contribute to the application of current techniques and methodologies in digital forensics as a tool to support the legal framework of each country, considering cross-border trials. Finally, the authors highlight the implications of this study lie in the identification and analysis of the key issues that must be considered for digital forensics as a support tool for the admissibility of digital evidence in cross-border trials.

The authors consider that digital forensic will have high demand in cross-border trials, and it will depend on the people mobility between the countries considered in this study.

This paper shows that the soft systems methodology allows elaborating a holistic model focused on social problems involving digital media and informatics devices.

Recent years have witnessed an unexpected and astonishing rise of AI-generated (AIGC), thanks to the rapid advancement of technology and the omnipresence of social media. AIGCs created to mislead are more commonly known as DeepFakes, which erode our trust in online information and have already caused real damage. Thus, countermeasures must be developed to limit the negative impacts of AIGC. This position paper aims to provide a conceptual analysis of the impact of DeepFakes considering the production cost and overview counter technologies to fight DeepFakes. We will also discuss future perspectives of AIGC and their counter technology.

We summarize recent developments in generative AI and AIGC, as well as technical developments to mitigate the harmful impacts of DeepFakes. We also provide an analysis of the cost-effect tradeoff of DeepFakes.

The mitigation of DeepFakes call for multi-disciplinary research across the traditional disciplinary boundaries.

Government and business sectors need to work together to provide sustainable solutions to the DeepFake problem.

The research and development in counter-technologies and other mitigation measures of DeepFakes are important components for the health of future information ecosystem and democracy.

Unlike existing reviews in this topic, our position paper focuses on the insights and perspective of this vexing sociotechnical problem of our time, providing a more global picture of the solutions landscape.

The purpose of this paper is to identify whether there is a lower willingness to report a crime if a victim must hand in their mobile phone as evidence. If that is the case, the research seeks to examine whether privacy concerns and lower willingness correlate with one another and thereby investigate whether privacy concerns could lead to fewer crimes being reported and resolved.

A mobile phone survey was distributed to 400 Swedish adults to identify their hypothetical willingness to report certain crimes with and without handing in their mobile phones as evidence. The results were then analysed using inferential statistics.

The result suggests that there is no meaningful correlation between privacy attitudes and willingness to report crime when the handover of a mobile phone is necessary. The results of this study however show a significant lower willingness to report crimes when the mobile phone must be handed in.

Because the chosen target group were Swedish adults, the research results may lack generalisability for other demographics. Therefore, researchers are encouraged to test other demographics.

This paper’s contribution is the novel exploration of attitudes and behaviours regarding the combination of privacy, digital forensics, mobile phones and crime reportage. This research effort examined the problematic situation that can arise for victims of crime, the invasion of privacy when providing evidence by handing in a mobile phone to the police’s forensic unit for examination.

This paper aims to test the effectiveness of the Haryono Umar (HU)-model used in corruption prevention strategies through corruption detection as a tool for detecting corruption because the mode of corruption is increasingly dynamic and complex by focusing on the causes of corruption: pressure, opportunity, rationalization, capability and lack of integrity.

The research uses multiple regression methods, classification and regression trees and the HU-model application system developed by researchers. The research sample uses secondary data from financial reports on the Indonesia stock exchange according to organizational clustering (such as red, grey and green areas).

The research result showed that of the 470 sample companies, there were 445 companies, or 98.9%, in the red cluster (indicated corruption), 19 companies, or 4.04, in the green clusters or not indicated corruption and six companies, or 1.28%, were included in the grey cluster or potential corruption. By knowing the cluster of an organization, efforts to prevent corruption can be made effective and efficient. Implementing the HU-model proves that the amount of pressure, the abundance of opportunities, the ease of rationalization and the high level of position and authority strengthen the drive for corruption if there is a lack of integrity.

Each internal organization can use this model independently and find conditions related to corruption so that they can immediately take action to prevent it.

The application of the HU-model is a discovery in preventing corruption by focusing on the possibility of corruption occurring in each organization through organizational clustering.

This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.

Multiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.

The study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.

This study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

This study aims to examine the scope and contribution of Forensic Clinical Psychology (FCP) advice from the National Crime Agency (NCA) to criminal investigations in the UK to address the gap in current knowledge and research.

The 36 FCP reports reviewed were written between 2017 and 2021. They were analysed using Toulmin’s (1958) application of pertinent arguments to the evaluation process. The potential utility of the reports was analysed in terms of the advice provided.

Most of the reports involved murder and equivocal death. The reports focused primarily on understanding the offender’s psychopathology, actions, motivation and risk to self and others using a practitioner model of case study methodology. Out of the 539 claims, grounds were provided for 99% of the claims, 91% had designated modality, 62% of the claims were potentially verifiable and 57% of the claims were supported by a warrant and/or backing. Most of the reports provided either moderate or high insight into the offence/offender (92%) and potential for new leads (64%).

The advice provided relied heavily on extensive forensic clinical and investigative experience of offenders, guided by theory and research and was often performed under considerable time pressure. Flexibility, impartiality, rigour and resilience are essential prerequisites for this type of work.

To the best of the authors’ knowledge, this study is the first to systematically evaluate forensic clinical psychology reports from the NCA. It shows the pragmatic, dynamic and varied nature of FCP contributions to investigations and its potential utility.

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.

In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

This study empirically tests the use of loan loss provisions (LLPs) for earnings and capital smoothing when emphasis is laid on banks' riskiness and adoption of the International Financial Reporting Standards (IFRSs) in Nigeria.

Annual bank-level data are hand-extracted between 2007 and 2017 from annual reports of a sample 16 deposit money banks (DMBs), and analysed using appropriate panel regression models subsequent to a number of diagnostic tests including heteroscedasticity, autocorrelation and cross-sectional dependence. The use of both reported LLPs (TLLP) and discretionary LLPs (DLLP) for earnings and capital management is tested to advance the practice in the literature.

Generally, the study finds that Nigerian DMBs manage capital via LLPs, while mixed results are obtained for earnings smoothing. However, during IFRS, Nigerian DMBs' management of capital is identifiable with TLLP, while smoothing of earnings is peculiar to DLLP. Additionally, evidence of the improvement in loan loss reporting quality expected during IFRS for riskier Nigerian DMBs, could not be attained. This is corroborated by the study's findings of the use of both TLLP and DLLP for earnings and capital management during IFRS by DMBs in solvency crisis against the only use of TLLP to manage capital found for the entire period.

The evidential capital and earnings lopsidedness may subject Nigerian DMBs' going-concern to a lot of questions.

The study sets a foremost record in the empirical test of managerial opportunistic behaviour embedded in earnings and capital concurrently while accounting for loan losses by all categories of Nigerian DMBs in terms of riskiness, following accounting regime change.

This review aims to provide an understanding of the strengths and weaknesses of forensic accounting education and profession.

This paper reviews published forensic accounting studies to explore forensic accounting strengths and weaknesses.

The strengths of forensic accounting are its benefits to students and accounting professionals, the significant need and increasing demand, the new career channels and the reduction of fraud. The weakness factors are the lack of regulation, the lack of control over the profession entry, the lack of agreement on how to teach forensic accounting, the lack of specialized research journals, the misconception of its intrinsic aim, the lack of highly qualified practitioners and educators and the lack of public recognition and occupation reputation.

It is hoped that this structured investigation of the factors relevant to the current and future status of forensic accounting education and profession will provide a sufficient overview of the critical issues and concerns that are important to be known for understanding and advancing the vital application of forensic accounting on the Socio-Economic Development. It is anticipated that this paper has an impact on future policy that ultimately contributes to improving business and limit fraud incidents, thus, it can contribute to business and socio-economic development.

The literature on forensic accounting is extensive and varied. However, there is a lack of comprehensive understanding of the strengths and weaknesses of forensic accounting. This study provided policymakers with a comprehensive understanding of forensic accounting.