Jianhua Zhang, Shengyong Chen, Honghai Liu and Naoyuki Kubota
Sixian Chan, Jian Tao, Xiaolong Zhou, Binghui Wu, Hongqiang Wang and Shengyong Chen
Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual…
Abstract
Purpose
Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual target tracking still has great space for improvement. This paper aims to propose an accurate visual target tracking method based on standard hedging and feature fusion.
Design/methodology/approach
For this study, the authors first learn the discriminative information between targets and similar objects in the histogram of oriented gradients by feature optimization method, and then use standard hedging algorithms to dynamically balance the weights between different feature optimization components. Moreover, they penalize the filter coefficients by incorporating spatial regularization coefficient and extend the Kernelized Correlation Filter for robust tracking. Finally, a model update mechanism to improve the effectiveness of the tracking is proposed.
Findings
Extensive experimental results demonstrate the superior performance of the proposed method comparing to the state-of-the-art tracking methods.
Originality/value
Improvements to existing visual target tracking algorithms are achieved through feature fusion and standard hedging algorithms to further improve the tracking accuracy of robots on targets in reality.
Details
Keywords
Weiguo Sheng, Gareth Howells, Michael Fairhurst, Farzin Deravi and Shengyong Chen
Biometric authentication, which requires storage of biometric templates and/or encryption keys, raises a matter of serious concern, since the compromise of templates or keys…
Abstract
Purpose
Biometric authentication, which requires storage of biometric templates and/or encryption keys, raises a matter of serious concern, since the compromise of templates or keys necessarily compromises the information secured by those keys. To address such concerns, efforts based on dynamic key generation directly from the biometrics have recently emerged. However, previous methods often have quite unacceptable authentication performance and/or small key spaces and therefore are not viable in practice. The purpose of this paper is to propose a novel method which can reliably generate long keys while requires storage of neither biometric templates nor encryption keys.
Design/methodology/approach
This proposition is achieved by devising the use of fingerprint orientation fields for key generation. Additionally, the keys produced are not permanently linked to the orientation fields, hence, allowing them to be replaced in the event of key compromise.
Findings
The evaluation demonstrates that the proposed method for dynamic key generation can offer both good reliability and security in practice, and outperforms other related methods.
Originality/value
In this paper, the authors propose a novel method which can reliably generate long keys while requires storage of neither biometric templates nor encryption keys. This is achieved by devising the use of fingerprint orientation fields for key generation. Additionally, the keys produced are not permanently linked to the orientation fields, hence, allowing them to be replaced in the event of key compromise.
Details
Keywords
Ahmed Aboelfotoh, Ahmed Mohamed Zamel, Ahmad A. Abu-Musa, Frendy, Sara H. Sabry and Hosam Moubarak
This study aims to examine the ability of big data analytics (BDA) to investigate financial reporting quality (FRQ), identify the knowledge base and conceptual structure of this…
Abstract
Purpose
This study aims to examine the ability of big data analytics (BDA) to investigate financial reporting quality (FRQ), identify the knowledge base and conceptual structure of this research field and explore BDA techniques used over time.
Design/methodology/approach
This study uses a comprehensive bibliometric analysis approach (performance analysis and science mapping) using software packages, including Biblioshiny and VOSviewer. Multiple analyses are conducted, including authors, sources, keywords, co-citations, thematic evolution and trend topic analysis.
Findings
This study reveals that the intellectual structure of using BDA in investigating FRQ encompasses three clusters. These clusters include applying data mining to detect financial reporting fraud (FRF), using machine learning (ML) to examine FRQ and detecting earnings management as a measure of FRQ. Additionally, the results demonstrate that ML and DM algorithms are the most effective techniques for investigating FRQ by providing various prediction and detection models of FRF and EM. Moreover, BDA offers text mining techniques to detect managerial fraud in narrative reports. The findings indicate that artificial intelligence, deep learning and ML are currently trending methods and are expected to continue in the coming years.
Originality/value
To the best of the authors’ knowledge, this study is the first to provide a comprehensive analysis of the current state of the use of BDA in investigating FRQ.
Details
Keywords
Weilong Liu, Zhongguo Wang and Xv Zhang
This paper aims to integrate the latent semantic features of annual report text with accounting indicators to construct a financial fraud identification model, and quantitatively…
Abstract
Purpose
This paper aims to integrate the latent semantic features of annual report text with accounting indicators to construct a financial fraud identification model, and quantitatively analyze the impact of different corporate risks on financial fraud behavior in different industries, providing a reference for identifying financial fraud.
Design/methodology/approach
This paper obtains 3,860 corporate annual report samples and accounting indicators from 2001 to 2020 through crawlers and the CSMAR database as our experimental subjects. By integrating latent semantic features with accounting indicators and textual language features, a new indicator system group is constructed. Based on this indicator system group, multiple model identification effects are compared and a stacking-based enterprise financial fraud identification model is constructed. In addition, an econometric model is established to verify the impact of latent semantic features related to enterprises on corporate financial fraud.
Findings
The experimental results show that the constructed stacking-based enterprise financial fraud identification model performs better than other machine learning models and can effectively identify financial fraud. The econometric model established for the latent semantic information of annual reports explains the impact of different corporate trends on fraud behavior in different industries.
Originality/value
This paper combines the textual latent semantic features of annual reports with accounting indicators, expands the scope of data analysis, introduces the idea of ensemble learning, updates the financial fraud identification algorithm and constructs an econometric model for further analysis, providing a reference for financial fraud identification.