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Article
Publication date: 23 February 2024

Junseon Jeong, Minji Park, Hyeonah Jo, Chunju Kim and Ji Hoon Song

This study identifies the policing pre-deployment training content for Korean experts based on needs assessments. Korean policing is at an excellent level to transfer knowledge…

Abstract

Purpose

This study identifies the policing pre-deployment training content for Korean experts based on needs assessments. Korean policing is at an excellent level to transfer knowledge and skills. Pre-deployment training should be designed systematically and training of trainers approaches should be implemented.

Design/methodology/approach

This study used T-tests, Borich needs assessments, and Locus for Focus model analyses to determine the priorities of needs for pre-deployment training in policing. A survey of 116 experienced experts was conducted, with 87 responding (75%).

Findings

The study identified 26 factors that deployed law enforcement professionals want to learn from pre-deployment training. These factors were categorized into three areas: research, training design and methods and understanding of partner countries and international development cooperation. The nine highest priorities for training needs were related to understanding the status and conditions of police training in the country to which policing experts are deployed.

Research limitations/implications

This study was limited to Korean policing experts. And the study did not evaluate the validity of the training curriculum or indicators.

Practical implications

Technical assistance in international policing development cooperation aims to train future trainers who can train local police. This study found that limited learner information and poor communication skills can lead to ineffective technical assistance.

Originality/value

This study highlights the importance of knowledge transfer and effective pre-deployment training for policing. The findings can be used to improve training programs and police human resource development.

Open Access
Article
Publication date: 19 October 2023

Tinna Dögg Sigurdardóttir, Lee Rainbow, Adam Gregory, Pippa Gregory and Gisli Hannes Gudjonsson

The present study aims to examine the scope and contribution of behavioural investigative advice (BIA) reports from the National Crime Agency (NCA).

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Abstract

Purpose

The present study aims to examine the scope and contribution of behavioural investigative advice (BIA) reports from the National Crime Agency (NCA).

Design/methodology/approach

The 77 BIA reports reviewed were written between 2016 and 2021. They were evaluated using Toulmin’s (1958) strategy for structuring pertinent arguments, current compliance with professional standards, the grounds and backing provided for the claims made and the potential utility of the recommendations provided.

Findings

Consistent with previous research, most of the reports involved murder and sexual offences. The BIA reports met professional standards with extremely high frequency. The 77 reports contained a total of 1,308 claims of which 99% were based on stated grounds. A warrant and/or backing was provided for 73% of the claims. Most of the claims in the BIA reports involved a behavioural evaluation of the crime scene and offender characteristics. The potential utility of the reports was judged to be 95% for informative behavioural crime scene analysis and 40% for potential new lines of enquiry.

Practical implications

The reports should serve as a model for the work of behavioural investigative advisers internationally.

Originality/value

To the best of the authors’ knowledge, this is the first study to systematically evaluate BIA reports commissioned by the NCA; it adds to previous similar studies by evaluating the largest number of BIA reports ever reviewed, and uniquely provides judgement of overall utility.

Article
Publication date: 17 June 2021

Ambica Ghai, Pradeep Kumar and Samrat Gupta

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered…

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Abstract

Purpose

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

Design/methodology/approach

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

Findings

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

Research limitations/implications

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

Practical implications

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

Social implications

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

Originality/value

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

Details

Information Technology & People, vol. 37 no. 2
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 26 May 2023

Neha Seth and Deepti Singh

This paper aims to provide a bibliometric review and visualisation analysis of the literature on Sustainable Stock Indices (SSI) between January 2001 and March 2022. The purpose…

Abstract

Purpose

This paper aims to provide a bibliometric review and visualisation analysis of the literature on Sustainable Stock Indices (SSI) between January 2001 and March 2022. The purpose of performing this bibliometric analysis is to empirically report the trend, intellectual structure, knowledge development directions and identify prospective research topics in the area of SSI.

Design/methodology/approach

A total of 222 publications were selected after evaluating, identifying and synthesising the extensive publications using the Preferred Reporting Items for the Systematic Reviews and Meta-Analyses (PRISMA) approach. The articles were extracted from the databases of SCOPUS, Web of Science and Google Scholar. The study uses VOSviewer and RStudio software to answer four research questions.

Findings

The results signify that there has been a considerable increase in the level of research considering SSI. Further, the study shows that SSI is among the top five trending keywords in the research related to finance and environment. Most papers considered as a sample for this study are based on Dow Jones Sustainable Indices. Noteworthy, very few economies are participating in this research domain, and the significant contribution is from the developed countries.

Practical implications

The present review paper may assist the researchers in identifying the trending research topics in this domain. It may serve as a roadmap for several further studies in the area.

Originality/value

This study is unique in terms of reviewing the literature based on SSI. Further, it provides a holistic view of the current trend, global position and research hotspots of SSI, which has important implications for future research.

Details

Qualitative Research in Financial Markets, vol. 16 no. 2
Type: Research Article
ISSN: 1755-4179

Keywords

Article
Publication date: 19 May 2022

Priyanka Kumari Bhansali, Dilendra Hiran, Hemant Kothari and Kamal Gulati

The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses…

Abstract

Purpose

The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.

Design/methodology/approach

This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.

Findings

For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.

Originality/value

The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

Details

International Journal of Pervasive Computing and Communications, vol. 20 no. 2
Type: Research Article
ISSN: 1742-7371

Keywords

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