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This article provides an overview of the regulation of artificial intelligence (AI) in Brazil, with a focus on its impact on labor and employment. While AI adoption in Brazilian industry is still limited, a significant portion of jobs in the country are considered highly vulnerable to AI. The article highlights the challenges and lack of regulation surrounding platform work and the potential impact of AI on agriculture. The Brazilian government has established the Brazilian National Strategy for Artificial Intelligence (EBIA) to promote responsible AI development and the training of qualified professionals. Additionally, there are bills being drafted to regulate AI and establish principles, rights, and duties for its use, as well as to provide rights for individuals affected by AI systems. The article emphasizes the importance of social dialogue and workers' organizations in protecting workers' rights in the face of AI advancements. [Extracted from the article]

Biodegradable energy storage devices are being developed for real-time monitoring of biometric data, medical diagnosis, prognosis, and therapeutic uses due to the rising need for energy storage devices in biomedical and information technologies. In this review, we have briefly discussed the environment-friendly and biodegradable electrode materials in supercapacitors, batteries, supercapattery, and skin-patchable devices. The charge storage mechanism, biocompatibility, and safety of electrode material are discussed in detail. The electrode material based on carbon, metal, and organic patchable composites is studied. Furthermore, the most essential and core part of this review is discussing the several synthesis methodologies of biological environments and how these biodegradable materials fit into the architecture of energy storage devices. Lastly, this review paper gives a deep understanding of (biodegradable and environment-friendly electrode materials) in the current energy storage systems, challenges, and research directions. [ABSTRACT FROM AUTHOR]

Recently, multimodal authentication methods based on deep learning have been widely explored in biometrics. Nevertheless, the contradiction between the data privacy protection and the requirement of sufficient data when model optimizing has become increasingly prominent. To this end, we proposes a multimodal biometric federated learning framework (FedMB) to realize the multiparty joint training of identity authentication models with different modal data while protecting the users' data privacy. Specifically, a personalized multimodal biometric recognition model fully trained by each participant is first obtained to improve the authentication performance, using modal point clustering with class‐first federated learning methods on the service side with the modal. Then a complementary multimodal biometric recognition strategy is implemented to build a complementary modal model. Finally, the fusion participant local model, with the modal model and complementary modal model, is trained by all participants again to obtain a more personalized modal model. The experimental results have demonstrated that the proposed FedMB can either protect the data privacy or utilize the data from all participants to train the personalized biometric recognition model to improve identity authentication performance. [ABSTRACT FROM AUTHOR]

Offering transactions through mobile devices has many advantages, such as cashless payments, lower transaction costs, and provide employment opportunities. However, it introduces access security challenges that must be dealt with, which may allow unauthorized access, resulting in theft. This work proposes a framework to improve security on the usage of Mobile Money Services (MMS) by using two-factor authentication (2FA) of PIN and iris biometric authentication method (IRBAM). The rapid application development (RAD) approach was used to develop mobile money applications based on the proposed framework. The proposed framework will improve the security of accessing MMS. [ABSTRACT FROM AUTHOR]

Purpose: To present a set of closed‐form analytical equations to create a consistent eye model balance based on clinically measured input parameters in a single step. These models complement the existing iterative approaches in the literature. Methods: Two different approaches are presented, both considering the cornea and lens as equivalent thin lenses. The first, called the Gaussian model, starts by defining the refractive error as the difference between the axial power (or dioptric distance) and the whole eye power, which can be expanded by filling in the formulas for each power. The resulting equation can be solved for either the refractive error, axial length, corneal power, lens power or the distance between the cornea and the lens as a function of the other four parameters. The second approach uses vergence calculations to provide alternative expressions, assuming that the refractive error is located at the corneal plane. Both models are explored for a biometric range typically found in adult human eyes. Results: The Gaussian and vergence models each instantly balance the input data into a working eye model over the human physiological range and far beyond as demonstrated in various examples. The equations of the Gaussian model are more complicated, while the vergence model experiences more singularities, albeit in trivial or highly unlikely parameter combinations. Conclusions: The proposed equations form a flexible and robust platform to create eye models from clinical data. Possible applications lie in creating animal eye models or providing a generic reference for real biometric data and the relationships between the ocular dimensions. [ABSTRACT FROM AUTHOR]

eHealth includes services and systems of healthcare and information technologies. A patient monitoring system is essential to have a medical professional be aware of the patient's condition. On the other hand, telecare medical information system (TMIS) makes a doctor able to make diagnoses and provide treatment advice to a patient via the Internet. How to ensure convenience and user privacy for eHealth at the same time is always an urgent issue. Because sensor networks make obtaining instantaneous information feasible, we first analyze related protocols and makes further discussions with proper modifications to ensure the patient monitoring system's security. Then we propose a biometrics-based mutual authentication scheme to ensure privacy and security for TMIS. [ABSTRACT FROM AUTHOR]

Traditional identity recognition methods are facing significant security challenges due to their vulnerability to leakage and forgery. Brainprint recognition, a novel biometric identification technology leveraging EEG signals, has emerged as a promising alternative owing to its advantages such as resistance to coercion, non-forgeability, and revocability. Nevertheless, the scarcity of high-quality electroencephalogram (EEG) data limits the performance of brainprint recognition systems, necessitating the use of shallow models that may not perform optimally in real-world scenarios. Data augmentation has been demonstrated as an effective solution to address this issue. However, EEG data encompass diverse features, including temporal, frequency, and spatial components, posing a crucial challenge in preserving these features during augmentation. This paper proposes an end-to-end EEG data augmentation method based on a spatial–temporal generative adversarial network (STGAN) framework. Within the discriminator, a temporal feature encoder and a spatial feature encoder were parallelly devised. These encoders effectively captured global dependencies across channels and time of EEG data, respectively, leveraging a self-attention mechanism. This approach enhances the data generation capabilities of the GAN, thereby improving the quality and diversity of the augmented EEG data. The identity recognition experiments were conducted on the BCI-IV2A dataset, and Fréchet inception distance (FID) was employed to evaluate data quality. The proposed method was validated across three deep learning models: EEGNET, ShallowConvNet, and DeepConvNet. Experimental results indicated that data generated by STGAN outperform DCGAN and RGAN in terms of data quality, and the identity recognition accuracies on the three networks were improved by 2.49%, 2.59% and 1.14%, respectively. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to electroencephalography (EEG) biometric authentication using eye blink artifacts. Unlike traditional methods that rely on imagination and mental tasks, which are susceptible to emotional and physical variations, this approach leverages the consistent effects of eye blinks on brainwaves for authentication. Brainwaves were recorded using the NeuroSky Mindwave Mobile 2 headset, and eye blinks were extracted via NeuroSky's blink detection algorithm. An authentication algorithm was developed based on blink strength, time, and frequency. The proposed method demonstrated high performance with an accuracy (ACC) of 97%, a false acceptance rate (FAR) of 5%, and a false rejection rate (FRR) of 1%. This study also explored the impact of emotions and physical exercise on the authentication process, confirming the method's robustness under varying conditions. These findings suggest that eye blink artifacts offer a reliable and practical biometric trait for EEG-based authentication systems, providing a secure alternative to traditional biometric methods. The substantial contribution of this research lies in demonstrating the superior stability and usability of eye blink-based EEG authentication under diverse conditions, compared to existing EEG authentication methods that often require mental tasks or multi-channel recordings. [ABSTRACT FROM AUTHOR]

The identification of individuals through ear images is a prominent area of study in the biometric sector. Facial recognition systems have faced challenges during the COVID-19 pandemic due to mask-wearing, prompting the exploration of supplementary biometric measures such as ear biometrics. The research proposes a Deep Learning (DL) framework, termed DeepBio, using ear biometrics for human identification. It employs two DL models and five datasets, including IIT Delhi (IITD-I and IITD-II), annotated web images (AWI), mathematical analysis of images (AMI), and EARVN1. Data augmentation techniques such as flipping, translation, and Gaussian noise are applied to enhance model performance and mitigate overfitting. Feature extraction and human identification are conducted using a hybrid approach combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). The DeepBio framework achieves high recognition rates of 97.97%, 99.37%, 98.57%, 94.5%, and 96.87% on the respective datasets. Comparative analysis with existing techniques demonstrates improvements of 0.41%, 0.47%, 12%, and 9.75% on IITD-II, AMI, AWE, and EARVN1 datasets, respectively. [ABSTRACT FROM AUTHOR]

Flexible resonant acoustic sensors exhibit enhanced sensitivity near their resonant frequencies and have attracted increasing interest as essential components for next‐generation human‐machine speech interactions. However, membrane‐based resonant acoustic sensors are bulky owing to the inherently high bending stiffness of the sensing membrane. Inspired by the tiny hair‐based sensitive acoustic receptors of spiders, this study reports a hair‐like piezoelectric resonant acoustic particle velocity sensor (HP‐APVS) that enables highly sensitive sound sensing with a significantly reduced size. The HP‐APVS device is essentially a polyimide cantilever array fabricated using Nb0.02‐Pb(Zr0.6Ti0.4)O3 (PZT) on polyimide and self‐bending processes. The experimental results demonstrate that the HP‐APVS shows a linear response to acoustic particle velocity instead of acoustic pressure, with enhanced sensitivity in the resonance mode. Additionally, the proposed HP‐APVS exhibits an outstanding speaker recognition rate of 95.3% with an error rate reduction of 75% compared with that of a reference commercial microphone, indicating many potential applications in the realms of biometric authentication, voice user interfaces, and other intelligent acoustic electronics. [ABSTRACT FROM AUTHOR]

We propose a novel and versatile computational approach, based on hierarchical COSFIRE filters, that addresses the challenge of explainable retina and palmprint recognition for automatic person identification. Unlike traditional systems that treat these biometrics separately, our method offers a unified solution, leveraging COSFIRE filters' trainable nature for enhanced selectivity and robustness, while exhibiting explainability and resilience to decision-based black-box adversarial attack and partial matching. COSFIRE filters are trainable, in that their selectivity can be determined with a one-shot learning step. In practice, we configure a COSFIRE filter that is selective for the mutual spatial arrangement of a set of automatically selected keypoints of each retina or palmprint reference image. A query image is then processed by all COSFIRE filters and it is classified with the reference image that was used to configure the COSFIRE filter that gives the strongest similarity score. Our approach, tested on the VARIA and RIDB retina datasets and the IITD palmprint dataset, achieved state-of-the-art results, including perfect classification for retina datasets and a 97.54% accuracy for the palmprint dataset. It proved robust in partial matching tests, achieving over 94% accuracy with 80% image visibility and over 97% with 90% visibility, demonstrating effectiveness with incomplete biometric data. Furthermore, while effectively resisting a decision-based black-box adversarial attack and impervious to imperceptible adversarial images, it is only susceptible to highly perceptible adversarial images with severe noise, which pose minimal concern as they can be easily detected through histogram analysis in preprocessing. In principle, the proposed learning-free hierarchical COSFIRE filters are applicable to any application that requires the identification of certain spatial arrangements of moderately complex features, such as bifurcations and crossovers. Moreover, the selectivity of COSFIRE filters is highly intuitive; and therefore, they provide an explainable solution. [ABSTRACT FROM AUTHOR]

The goal of information security is to prevent unauthorized access to data. There are several conventional ways to confirm user identity, such as using a password, user name, and keys. These conventional methods are rather limited; they can be stolen, lost, copied, or cracked. Because multimodal biometric identification systems are more secure and have higher recognition efficiency than unimodal biometric systems, they get attention. Single-modal biometric recognition systems perform poorly in real-world public security operations because of poor biometric data quality. Some of the drawbacks of current multimodal fusion methods include low generalization and single-level fusion. This study presents a novel multimodal biometric fusion model that significantly enhances accuracy and generalization through the power of artificial intelligence. Various fusion methods, encompassing pixel-level, feature-level, and score-level fusion, are seamlessly integrated through deep neural networks. At the pixel level, we employ spatial, channel, and intensity fusion strategies to optimize the fusion process. On the feature level, modality-specific branches and jointly optimized representation layers establish robust dependencies between modalities through backpropagation. Finally, intelligent fusion techniques, such as Rank-1 and modality evaluation, are harnessed to blend matching scores on the score level. To validate the model's effectiveness, we construct a virtual homogeneous multimodal dataset using simulated operational data. Experimental results showcase significant improvements compared to single-modal algorithms, with a remarkable 2.2 percentage point increase in accuracy achieved through multimodal feature fusion. The score fusion method surpasses single-modal algorithms by 3.5 percentage points, reaching an impressive retrieval accuracy of 99.6%. [ABSTRACT FROM AUTHOR]

The study aimed to standardize the cryodehydration technique for bovine fetal hearts, focusing on optimizing protocols for each developmental stage to preserve morphological characteristics. We analyzed 29 bovine fetal hearts categorized into early, middle, and late developmental stages. These hearts underwent cryodehydration until a 60%–70% reduction in original fluid volume was achieved. Biometric data were recorded and statistically analyzed using Pearson correlation tests for age versus weight and age versus number of cryodehydration sessions. Morphometric comparisons before and after cryodehydration were performed using paired t‐tests. In Group I, hearts exhibited well‐defined structures, including the atrium cordis, ventriculus cordis, auricula atrii, aorta, truncus pulmonalis, and ramus coronaries arteria, which were preserved in Groups II and III. Additionally, in Group I the heart had a conical or flat apex cordis, whereas those in Groups II and III had a more pronounced apex. The average number of cryodehydration sessions required was 9.38 (±1.2) days for Group I, 12.37 (±1.4) days for Group II, and 15 days for Group III. A positive correlation was found between age and sample weight, indicating that more developed hearts were heavier. Similarly, there was a positive correlation between gestational age and the number of cryodehydration sessions, suggesting that more advanced stages required more cryodehydration sessions. Paired t‐tests demonstrated high statistical significance in the morphometric parameters before and after cryodehydration, indicating a loss of mass during dehydration. However, there was no alteration in the macroscopic structure of the hearts, which remained morphologically preserved. In conclusion, cryodehydration shows promise for preserving and analyzing the external morphological characteristics of bovine fetal cardiac development. It also provides lightweight, odorless, and easy‐to‐handle specimens ideal for detailed morphological studies and offers a unique perspective for investigating cardiac morphology in biological research contexts. [ABSTRACT FROM AUTHOR]

This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This study proposes a method for user authentication by utilizing periocular images captured by a camera attached to a VR headset. Existing datasets have lacked periocular images acquired in VR environments, limiting their practical application. To address this, periocular images were collected from 100 participants using the HTC Vive Pro and Pupil Labs infrared cameras in a VR environment. Participants also watched seven emotion-inducing videos, and emotional evaluations for each video were conducted. The final dataset comprises 1988 monocular videos and corresponding self-assessment manikin (SAM) evaluations for each experimental video. This study also presents a baseline study to evaluate the performance of biometric authentication using the collected dataset. A deep learning model was used to analyze the performance of biometric authentication based on periocular data collected in a VR environment, confirming the potential for implicit and continuous authentication. The high-resolution periocular images collected in this study provide valuable data not only for user authentication but also for emotion evaluation research. The dataset developed in this study can be used to enhance user immersion in VR environments and as a foundational resource for advancing emotion recognition and authentication technologies in fields such as education, therapy, and entertainment. This dataset offers new research opportunities for non-invasive continuous authentication and emotion recognition in VR environments, and it is expected to significantly contribute to the future development of related technologies. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions The purpose of this study was to simulate the impact of biometric measure uncertainties, lens equivalent and toric power labelling tolerances and axis alignment errors on the refractive outcome after cataract surgery with toric lens implantation.In this retrospective non‐randomised cross sectional Monte‐Carlo simulation study we evaluated a dataset containing 7458 LenStar 900 preoperative biometric measurements. The biometric uncertainties from literature, lens power labelling according to ISO 11979, and axis alignment tolerances of a modern toric lens (Hoya Vivinex) were taken to be normally distributed and used in a Monte‐Carlo simulation with 100 000 samples per eye. The target variable was the defocus equivalent (DEQ) derived using the Castrop (DEQC) and the Haigis (DEQH) formulae.Mean/median / 90% quantile DEQC was 0.22/0.21/0.36 D and DEQH was 0.20/0.19/0.32 D. Ignoring the variation in lens power labelling and toric axis alignment the respective DEQC was 0.20/0.19/0.32 D and DEQH was 0.18/0.17/0.29 D. DEQC and DEQH increased with shorter eyes, steeper corneas, equivalent lens power and highly with toric lens power.According to our simulation results, uncertainties in biometric measures, lens power labelling tolerances, and axis alignment errors are responsible for a significant part of the refraction prediction error after cataract surgery with toric lens implantation. Additional labelling of the exact equivalent and toric power on the lens package could be a step to improve postoperative results. [ABSTRACT FROM AUTHOR]

In this research paper, we propose an unsupervised framework for feature learning based on an autoencoder to learn sparse feature representations for EEG-based person identification. Autoencoder and CNN do the person identification task for signal reconstruction and recognition. Electroencephalography (EEG) based biometric system is vesting humans to recognize, identify and communicate with the outer world using brain signals for interactions. EEG-based biometrics are putting forward solutions because of their high-safety capabilities and handy transportable instruments. Motor imagery EEG (MI-EEG) is a maximum broadly centered EEG signal that exhibits a subject's motion intentions without real actions. The Proposed framework proved to be a practical approach to managing the massive volume of EEG data and identifying the person based on their different task with resting states. The experiments have been conducted on the standard publicly available motor imagery EEG dataset with 109 subjects. The highest recognition rate of 87.60% for task-based identification and 99.89% recognition rate for resting-state has been recorded using the Autoencoder-CNN model. The outcomes imply that the overall performance of our proposed framework is similar or advanced to that of the state-of-the-art method. The shape is a realistic technique to control the full-size extent of EEG data and to pick out the individual based totally on their specific task. [ABSTRACT FROM AUTHOR]

The main goals of this study were to document and compare the normal ranges of testicular haemodynamic parameters in pre‐ (aged 8–12 months; n = 4) and postpubertal (aged 24–259 months; n = 16) Miranda donkeys in the breeding season, and to correlate animal biometric data and testicular Doppler indices with basic semen quality parameters of sexually mature jacks. Colour and pulsed‐Doppler ultrasonography were employed to assess blood flow in the left and right distal supratesticular artery (DsTA) and their marginal branches (marginal arteries—MA). Peak systolic velocity (PSV), end diastolic velocity (EDV), pulsatility index (PI) and resistive index (RI) were evaluated in both blood vessels, and TAMV (time‐averaged mean velocity), TABF (total arterial blood flow) and TABF rate (TABF‐R) were calculated for MA. The mean diameter of MA was greater (p < 0.05; 0.24 ± 0.05 vs. 0.19 ± 0.05 cm; mean ± SD) but TABF‐R was less (p < 0.05; 0.004 ± 0.004 vs. 0.02 ± 0.01 mL/s/cm3) in sexually mature donkeys compared with prepubertal animals. Apart from RI values for the right testicle of prepubertal donkeys, PI and RI were consistently greater (p < 0.05) in DsTA compared with MA. Significant correlations were found among select biometric and haemodynamic attributes of the testes (height, width and length, TV, TTV and PSV‐ST) and ejaculate characteristics (volume, sperm defects‐total, head and midpiece) in sexually mature donkeys (n = 8). The present results highlight the importance of scrotal ultrasonography for the reproductive assessment of jacks and provide reference values, based on the available subpopulation of Miranda donkeys that can be used in their clinical and reproductive management and research, or conservation programmes. [ABSTRACT FROM AUTHOR]

Research on multi‐biometric authentication systems using multiple biometric modalities to defend against adversarial attacks is actively being pursued. These systems authenticate users by combining two or more biometric modalities using score or feature‐level fusion. However, research on adversarial attacks and defences against each biometric modality within these authentication systems has not been actively conducted. In this study, we constructed a multi‐biometric authentication system using fingerprint, palmprint, and iris information from CASIA‐BIT by employing score and feature‐level fusion. We verified the system's vulnerability by deploying adversarial attacks on single and multiple biometric modalities based on the FGSM, with epsilon values ranging from 0 to 0.5. The experimental results show that when the epsilon value is 0.5, the accuracy of the multi‐biometric authentication system against adversarial attacks on the palmprint and iris information decreases from 0.995 to 0.018 and 0.003, respectively, and the f1‐score decreases from 0.995 to 0.007 and 0.000, respectively, demonstrating susceptibility to adversarial attacks. In the case of fingerprint data, however, the accuracy and f1‐score decreased from 0.995 to 0.731 and from 0.995 to 0.741, respectively, indicating resilience against adversarial attacks. [ABSTRACT FROM AUTHOR]

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of −0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring. [ABSTRACT FROM AUTHOR]

Purpose: To report the prevalence data for total corneal astigmatism (TCA) in cataract patients. Methods: The authors retrospectively collected and analyzed the preoperative biometric data of the patients who underwent cataract surgery in the Department of Ophthalmology, Peking University Third Hospital, from January 2019 to May 2023. Results: The mean age of the 10817 patients was 71 ± 10 years; the male/female ratio was 4653/6164. The mean TCA obtained by the IOLMaster 700 (Carl Zeiss Meditec AG, Jena, Germany), the Abulafia-Koch (AK) formula, and the Barrett toric calculator was 1.11 ± 0.81 diopter (D), 1.13 ± 0.75 D, and 1.12 ± 0.74 D respectively, which was significantly greater than the mean standard keratometric (K) astigmatism (0.99 ± 0.75 D) obtained by IOLMaster 700. Against-the-rule (ATR) astigmatism was dominant in all the TCA measurements, and its proportion increased with age. TCA measurements by different methods exhibit high variability, with a total of 1574 (8.9%) data sets from 1016 (9.4%) patients showing a difference larger than 0.5 D in at least one pair of TCA measurements. Conclusion: The use of TCA rather than K astigmatism significantly influenced the choice of intraocular lenses (IOLs) as more patients would be candidates for toric IOLs. It was essential to carefully compare and select TCA obtained with multiple methods for optimal postoperative visual quality. [ABSTRACT FROM AUTHOR]

Hand vein identification stands out to be an increasingly popular approach for biometric identification due to its distinctiveness and convenience. While state-of-the-art techniques are able to achieve good performance, they share two common drawbacks: (1) complex preprocessing procedures, e.g., vein enhancement and Region of Interest (ROI) extraction, and (2) vein information loss due to hand ROI partition. To address these issues, we propose VeinTr, an end-to-end full-hand vein identification approach. In particular, our VeinTr consists of three components: a local feature extractor, a lightweight transformer, and a global feature decoder. We first obtain local features via convolution-based ResNet-like blocks. Then the attention mechanism is employed to aggregate global features from local features, which can be then decoded as global hand vein features. Finally, a global feature decoder is applied to generate robust hand features. By doing so, VeinTr is capable of directly extracting robust hand vein features from raw hand vein images. We evaluate our method on CASIA, TPV, and PLUSVein hand vein datasets. Experimental results show that our approach outperforms the state-of-the-art methods and has strong inter-dataset generalization abilities. [ABSTRACT FROM AUTHOR]

This paper considers what liberal philosopher Michael Sandel coins the 'moral limits of markets' in relation to the idea of paying people for data about their biometrics and emotions. With Sandel arguing that certain aspects of human life (such as our bodies and body parts) should be beyond monetisation and exchange, others argue that emerging technologies such as Personal Information Management Systems can enable a fairer, paid, data exchange between the individual and the organisation, even regarding highly personal data about our bodies and emotions. With the field of data ethics rarely addressing questions of payment, this paper explores normative questions about data dividends. It does so by conducting a UK-wide, demographically representative online survey to quantitatively assess adults' views on being paid for personal data about their biometrics and emotions via a Personal Information Management System, producing a data dividend, a premise which sees personal data through the prism of markets and property. The paper finds diverse attitudes based on socio-demographic characteristics, the type of personal data sold, and the type of organisation sold to. It argues that (a) Sandel's argument regarding the moral limits of markets has value in protecting fundamental freedoms of those in society who are arguably least able to (such as the poor); but (b) that contexts of use, in particular, blur moral limits regarding fundamental freedoms and markets. [ABSTRACT FROM AUTHOR]

The article questions digital agency as a subjective experience of refugees when crossing Europe's external and internal borders. More concretely, the article asks how refugees experience digital agency, and how this concrete experience is constituted in specific border practices. In doing so, it examines the contradictions that arise between European laws and human rights in the context of border practices. The analysis of the data of this paper reflects two important components of digital agency: the sense of ownership and control of one's body and actions, and the capacity to think independently and thus make considered choices. This study is based on a qualitative research approach based on narrative interviews. The data was imported to MAXQDA, a software package that allows data to be efficiently collected, organised, analysed and visualised electronically. The article shows how refugees and asylum seekers, when crossing the border to Europe, do not simply enter another country, but a (powerful) institutional system. In having their biometric and digital data collected, they are confronted with several practices of border and risk management that have become routinised and thus 'normalised' on an institutional level. Thus, their digital agency must be understood as precarious, underscored by an installed coercive environment. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects objects that sense, communicate, and interact to achieve shared goals, and its integration with manufacturing has led to Industry 4.0. Security is crucial for user acceptance, involving data confidentiality, integrity, authentication, identity management, privacy, and trust among devices. Trust management is vital in IoT and IIoT for reliable services, accurate data collection, device authentication, and secure decision-making. This paper surveys blockchain-based trust mechanisms in IoT and IIoT, categorizing them into four types: basic trust frameworks, enhanced trust interaction, trust data management, and trust-based transaction validation. We analyze these mechanisms to identify their strengths, weaknesses, and current challenges. Blockchain shows promise in improving trust management by decentralizing trust, ensuring transparency, and enhancing security. However, challenges like scalability, energy consumption, and integration with existing IoT infrastructure remain. This survey aims to guide future research by highlighting these issues and proposing potential solutions, contributing to the development of blockchain-based trust management systems in IoT and IIoT. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this article is to identify methods for securing data developed in geoinformation systems and published on websites where data is stored in the cloud. Design/methodology/approach: The study used GIS tools to identify the level of selected natural hazards. The PHA method was then used to determine the magnitude of the emergency risk. The functionality of GibHub for secure cloud storage was then analysed. Findings: The result of the analysis presents a selected IT tool for geodata cybersecurity. Practical implications: The article presents the most important functionalities of the QGIS software, such as the process of generating web maps. It has been shown that IT tools are currently an integral part of critical infrastructure management processes. The most important issue of publishing confidential information is ensuring an appropriate level of security of IT systems. These aspects were also presented extensively in the work in the form of strongly developing tools based on biometric authentication. Originality/value: The article is a valuable material both for theoreticians in the field of security engineering and computer science, as well as for practitioners who come into contact with the issues of geoinformatics tools and cybersecurity in their daily work. The article is addressed to people dealing with the area of knowledge related to crisis management, IT and cybersecurity. [ABSTRACT FROM AUTHOR]

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The interactive installation "I'm sorry I made you feel that way" explores the intimate connection between a virtual body and physical well-being by linking real biometric data to a digital body representation. Developed during Martina Menegon's research visit using open-source tools, the interactive installation was exhibited in Vienna, London, Lisbon, and Pristina. It highlights generative AI's potential to create empathic digital connections, raising questions about the value and care of virtual bodies when linked to the well-being of a physical person. [ABSTRACT FROM AUTHOR]

Masked face recognition (MFR) has emerged as a critical domain in biometric identification, especially with the global COVID-19 pandemic, which introduced widespread face masks. This survey paper presents a comprehensive analysis of the challenges and advancements in recognizing and detecting individuals with masked faces, which has seen innovative shifts due to the necessity of adapting to new societal norms. Advanced through deep learning techniques, MFR, along with face mask recognition (FMR) and face unmasking (FU), represents significant areas of focus. These methods address unique challenges posed by obscured facial features, from fully to partially covered faces. Our comprehensive review explores the various deep learning-based methodologies developed for MFR, FMR, and FU, highlighting their distinctive challenges and the solutions proposed to overcome them. Additionally, we explore benchmark datasets and evaluation metrics specifically tailored for assessing performance in MFR research. The survey also discusses the substantial obstacles still facing researchers in this field and proposes future directions for the ongoing development of more robust and effective masked face recognition systems. This paper serves as an invaluable resource for researchers and practitioners, offering insights into the evolving landscape of face recognition technologies in the face of global health crises and beyond. [ABSTRACT FROM AUTHOR]

This paper situates the data privacy debate in the context of what I call the death of the data subject. My central claim is that concept of a rights-bearing data subject is being pulled in two contradictory directions at once, and that simultaneous attention to these is necessary to understand and resist the extractive practices of the data industry. Specifically, it is necessary to treat the problems facing the data subject structurally, rather than by narrowly attempting to vindicate its rights. On the one hand, the data industry argues that subjects of biometric identification lack legal standing to pursue claims in court, and Facebook recently denied that that its facial recognition software recognizes faces. On the other hand, industry takes consent to terms of service and arbitration clauses to create enforceable legal subject positions, while using promises of personalization to create a phenomenological subject that is unaware of the extent to which it is being manipulated. Data subjects thus have no legal existence when it is a matter of corporate liability, but legal accountability when it is a matter of their own liability. Successful reform should address the power asymmetries between individuals and data companies that enable this structural disempowerment. [ABSTRACT FROM AUTHOR]

In today's digital era, ensuring robust and secure authentication mechanisms is crucial. Multi-fusion biometric authentication systems have emerged as a powerful solution to enhance security and reliability by integrating multiple biometric traits. This paper presents a novel Multi-Fusion Biometric Authentication approach using Minutiae-Driven Fixed-Size Template Matching (MFTM). The proposed method leverages the unique features of minutiae points in fingerprints and combines them with other biometric modalities, such as iris and facial recognition, to create a fixed-size template for matching. The fusion process involves extracting and normalizing minutiae points from the fingerprint, followed by their integration with iris and facial features using a robust feature fusion algorithm. The fixed-size template ensures consistency and efficiency in the matching process, addressing challenges related to template size variability and computational overhead. Extensive experiments conducted on standard biometric datasets demonstrate that the proposed MFTM approach significantly enhances authentication accuracy, reduces false acceptance and rejection rates, and provides a highly secure and scalable authentication solution suitable for various applications, including access control and identity verification. The results show an authentication accuracy of 98.7%, a false acceptance rate (FAR) of 0.2%, and a false rejection rate (FRR) of 0.5%. Additionally, the computational time for matching is reduced by 25% compared to traditional methods, highlighting the efficiency and practicality of the proposed approach. [ABSTRACT FROM AUTHOR]

The measurement accuracy of current wearable pulse sensors is grandly challenged by motion artifacts caused by body biomechanical activities. In this study, a honeycomb‐structure‐inspired wearable pulse sensor is reported which not only performs ambulant cardiovascular monitoring but also realizes biometric authentication utilizing the acquired individual pulse wave profiles. The sensor showcases an impressive sensitivity of 46.2 mV Pa−1, a swift response time of 21 ms, and exceptional durability (minimal degradation after 6000 cycles). For practical application in clinical settings, the sensor is able to record pulse signals continuously and accurately from individuals aged between 27 and 57 years, especially including a 29‐year‐old pregnant woman. Leveraging deep learning algorithms, the sensor further utilizes individual pulse wave profiles for biometric authentication, reaching a classification accuracy of up to 99.4%. The honeycomb‐structure‐inspired wearable pulse sensor marks a significant advancement in the field of practical cardiovascular monitoring and biometric authentication. [ABSTRACT FROM AUTHOR]

Innovations in wearable technology and artificial intelligence have enabled consumer devices to process and transmit data about human mental states (cognitive, affective, and conative) through what this paper refers to as "cognitive biometrics." Devices such as brain-computer interfaces, extended reality headsets, and fitness wearables offer significant benefits in health, wellness, and entertainment through the collection and processing and cognitive biometric data. However, they also pose unique risks to mental privacy due to their ability to infer sensitive information about individuals. This paper challenges the current approach to protecting individuals through legal protections for "neural data" and advocates for a more expansive legal and industry framework, as recently reflected in the draft UNESCO Recommendation on the Ethics of Neurotechnology, to holistically address both neural and cognitive biometric data. Incorporating this broader and more inclusive approach into legislation and product design can facilitate responsible innovation while safeguarding individuals' mental privacy. Biometric data relating to human cognitive, affective, and conative states ("cognitive biometrics") are not adequately protected under existing privacy laws. The authors argue for an expansive approach that would treat neural and cognitive biometric data holistically as categories of "sensitive data" and provide them with analogous privacy protections. [ABSTRACT FROM AUTHOR]

Currently, security enhancement of biometric systems is an important issue that deserves consideration. This is attributed to the threats facing traditional recognition systems, which depend on Personal Identification Numbers (PINs) that can be stolen, easily. Utilization of original biometrics to access user services may lead to loss of the biometrics forever, if hacking attempts succeed in gaining access to the storage database of original templates. To address this concern and to avoid the utilization of original biometrics, we keep them away from being compromised through the utilization of cancelable biometric templates. This paper introduces a novel methodology for user authentication with multiple biometrics to generate distorted non-invertible cancelable templates to be stored in the database. The proposed framework begins with Discrete Cosine Transform (DCT) to achieve data compression in a multi-biometric scenario. After that, Double Random Phase Encoding (DRPE) is applied to increase the security level of the generated templates. Finally, an adaptive filter is used to induce an effect of whitening to generate the cancelable biometric templates. The generated patterns are uncorrelated due to the effect of encryption and adaptive filtering, which improves the security level against identity theft and provides good performance. Simulation results prove a good performance of the proposed cancelable biometric recognition framework with an Area under the Receiver Operating Characteristic curve (AROC) of 52.12% and an Equal Error Rate (EER) of 44.8462%. [ABSTRACT FROM AUTHOR]

Cancellable biometrics is an important trend in biometric security systems, allowing for intentional distortions or variations in the original biometric data to be used for authentication, while preserving privacy of users and security of the original data. This paper presents two cancellable biometric recognition algorithms based on quaternion mathematics. The first algorithm depends on the Fractional Fourier transform (FRFT) with quaternion mathematics to induce intentional distortions in biometric data, while the second algorithm relies on quaternion rotation to achieve the desired level of distortion. Both algorithms are evaluated using Equal Error Rate (EER) and Area under Receiver Operating Characteristic curve (AROC). The simulation results indicate that both algorithms achieve EER values close to 0 and AROC values close to 1, demonstrating their effectiveness and reliability for cancellable biometric recognition. The proposed cancellable biometric recognition algorithms are intended to preserve privacy of users and security of biometric data, while maintaining high levels of accuracy and performance. The use of quaternion mathematics allows for intentional distortions to be introduced in a controlled and secure manner, ensuring that the original biometric data remains protected from either hacking or unauthorized access. In conclusion, the proposed cancellable biometric recognition algorithms based on quaternion mathematics are reliable and effective solutions for biometric security, providing both security and privacy preservation within the biometric authentication process. [ABSTRACT FROM AUTHOR]

Introduction Modern warfare operations are volatile, highly complex environments, placing immense physiological, psychological, and cognitive demands on the warfighter. To maximize cognitive performance and warfighter resilience and readiness, training must address psychological stress to enhance performance. Resilience in the face of adversity is fundamentally rooted in an individual's psychophysiological stress response and optimized through decreased susceptibility to the negative impact of trauma exposure. The current project aims to optimize warfighter expertise, resilience, adaptability, and performance by utilizing a validated Full Dive Virtual Reality (FDVR) training platform to provide high-fidelity, safe, and scalable, controlled stress exposure in highly realistic simulated training scenarios with the most advanced, immersive technology available. Materials and Methods Following Institutional Review Board approval and consent, 2 operators were fitted with high-fidelity virtual reality headsets with hand and eye tracking, full-body haptic feedback suits, a 360° omnidirectional treadmill, and Food and Drug Administration (FDA) cleared biometric monitors. Following acclimation, operators were placed in an industrial fire scenario and instructed to respond as a firefighter and paramedic, to search for and resuscitate any casualties, extinguish the fire, and exfiltrate safely. Following initial acclimation and after each demonstration (n  = 2), 3 semistructured interviews asked operators their perceptions and experiences related to FDVR, focusing on usability, feasibility, and safety. Biometric data were continuously recorded using the Caretaker Medical VitalStream. Results Proof-of-concept (POC) testing proved that the FDVR training platform is usable, safe, and feasible. It creates an immersive environment with physiological responses to mimic realistic Mass Casualty Events (MCEs). Using a case study approach, transcript data were analyzed using thematic analysis. Three major themes emerged: Sensory deficits reduced realism, but sensory feedback improved fidelity, vestibular discord affected the virtual reality experience but only when the system did not respond naturally to operator movement after acclimation, and movement accommodations were made by operators to enhance usability, especially for fine motor movements. Biometric data analysis correlated timestamps from the VitalStream unit with operator responses to stress-inducing events (i.e. explosions, fires, and a deceased victim). Both operators exhibited significant physiological responses, including elevated heart rate, systolic blood pressure, and mean arterial pressure, particularly following explosions, encountering fire, and encountering the deceased victim within the training environment. Conclusions The FDVR training platform overcomes the obstacles of in-person simulation training and provides the closest to real-life experience available. It will allow warfighters to train with their teams in immersive environments that replicate the conditions in which they are expected to perform their duties. The POC demonstrated that physiological responses can be mapped to scenario events to allow tracking of stress responses, cognitive load, as well as performance, and decision-making of the warfighter. The POC only involved 2 operators, but served to prove that the platform was safe and effective. Future testing plans to include 200 warfighters in operational teams of 10 to 12 to further validate the training effectiveness of the FDVR platform. [ABSTRACT FROM AUTHOR]

Human gait recognition is a biometric identification method that uses a person's gait patterns to identify them. Gait Recognition (GR) is the steps to follow in recognizing walking style of an individual. It identifies a person as of a longer distance with higher precision owing to its unique characteristics; thus, it is mostly utilized for tracking purposes. In computer vision software, specifically, to track the same individual across multiple scenarios, the individual recognition across numerous cameras is a significant task. However, owing to the position of caught image or video, human identification by means of GR could be limited. Therefore, for person re-identification, a novel GR methodology has been proposed here. Five phases were undergone by the proposed methodology. Initially, from an openly accessible dataset, the input video is collected, which is then transmuted into a number of frames. To eliminate the noise from the frames, the Mean Modified Filter (MMF) is utilized after frame conversion. The Gaussian Taxicab Mixture Model (GTMM) is employed to get rid of the background from the frames. After that, Similarity Histogram Silhouette Segmentation (SHSS) model is utilized to segment the humans' silhouette as of the background removed frames; thus, generating the energy image. The features are extracted from this energy image. The Uniform Alex Neural Network classifier (UALNN) uses these features to identify the human gait. Improved recognition performance is attained from the experiential outcomes. The overall recognition outcomes obtained concluded that the proposed methodology is highly promising. [ABSTRACT FROM AUTHOR]

Definition: Eye-tracking is a biometrics technique that has started to find applications in research related to our interaction with the built environment. Depending on the focus of a given study, the collection of valence and arousal measurements can also be conducted to acquire emotional, cognitive, and behavioral insights and correlate them with eye-tracking data. These measurements can give architects and designers a basis for data-driven decision-making throughout the design process. In instances involving existing structures, biometric data can also be utilized for post-occupancy analysis. This entry will discuss eye-tracking and eye-tracking simulation in the context of our current understanding of the importance of our interaction with the built environment for both physical and mental well-being. [ABSTRACT FROM AUTHOR]

Background: Intraoral scans (IOS) provide precise 3D data of dental crowns and gingival structures. This paper explores an application of IOS in human identification. Methods: We propose a dental biometrics framework for human identification using 3D dental point clouds based on machine learning-related algorithms, encompassing three stages: data preprocessing, feature extraction, and registration-based identification. In the data preprocessing stage, we use the curvature principle to extract distinguishable tooth crown contours from the original point clouds as the holistic feature identification samples. Based on these samples, we construct four types of local feature identification samples to evaluate identification performance with severe teeth loss. In the feature extraction stage, we conduct voxel downsampling, then extract the geometric and structural features of the point cloud. In the registration-based identification stage, we construct a coarse-to-fine registration scheme in order to realize the identification task. Results: Experimental results on a dataset of 160 individuals demonstrate that our method achieves a Rank-1 recognition rate of 100% using complete tooth crown contours samples. Utilizing the remaining four types of local feature samples yields a Rank-1 recognition rate exceeding 96.05%. Conclusions: The proposed framework proves effective for human identification, maintaining high identification performance even in extreme cases of partial tooth loss. [ABSTRACT FROM AUTHOR]

Keystroke dynamics is the field of computer science that exploits data derived from the way users type. It has been used in authentication systems, in the identification of user characteristics for forensic or commercial purposes, and to identify the physical and mental state of users for purposes that serve human–computer interaction. Studies of keystroke dynamics have used datasets created from volunteers recording fixed-text typing or free-text typing. Unfortunately, there are not enough keystroke dynamics datasets available on the Internet, especially from the free-text category, because they contain sensitive and personal information from the volunteers. In this work, a free-text dataset is presented, which consists of 533 logfiles, each of which contains data from 3500 keystrokes, coming from 164 volunteers. Specifically, the software developed to record user typing is described, the demographics of the volunteers who participated are given, the structure of the dataset is analyzed, and the experiments performed on the dataset justify its utility. [ABSTRACT FROM AUTHOR]

The buzzword "smart borders" captures the latest instantiation of media technologies constituting state bordering. This article traces historical techniques of knowledge-production and decision-making at the border, in the case of Ellis Island immigration station, New York City (1892–1954). State bordering has long been enabled by media technologies, engulfed with imaginaries of neutral, unambiguous, efficient sorting between desired and undesired migrants—promises central to today's "smart border" projects. Specifically, the use of "proxies" for decision-making is traced historically, for example, biometric or biographic data, collected as seemingly authentic and neutral stand-ins for the migrant. Techniques of selecting, storing, and correlating proxies through media technologies demonstrate how public health anxieties, eugenics, and scientific technocracy of the Progressive Era formed the context of proxies being entrusted to enable decision-making. This pre-digital history of automation reveals how the logics and politics of proxification endure in contemporary border regimes and automated media at large. [ABSTRACT FROM AUTHOR]

This paper proposed a noble face recognition and biometric encryption technique for the biometric passport by applying encrypted biometric data encoded into the High-Capacity Color Two-Dimensional (HCC2D) Code to secure the biometric data. The proposed biometric encryption method is attained by applying the Secure Force (SF) algorithm and encoding it into the HCC2D code. The face recognition technology is achieved by combining the Viola-Jones (VJ) algorithm and the Local Binary Patterns (LBP) algorithm to extract the facial features and generate the template. This method will ensure the safety and security of a biometric passport from unauthorized access without knowing the passport holder. The proposed method will provide more security for border crossing and illegal immigrants for national security. [ABSTRACT FROM AUTHOR]

Despite all the benefits of what customer data can bring to the marketing field, customers' privacy concern is one of the key challenges that comes with Big Data, privacy threats have evolved along with the evolution of information technology and resulted in privacy protection behaviour among customers. The present research conducted an experimental study to examine the effects of external stimuli in triggering privacy protection behaviour, including the presence of surveillance cameras, company disclosure of biometric data collection, and customers' prior knowledge of privacy. The study involved a 3 (environmental awareness: low/average/high) x 2 (company disclosure: yes/no) between subject designs. Data was collected on the Tencent questionnaire platform () with 554 effective responses. Partial least squares structural equation modelling (PLS-SEM) was employed to analyze the data. The results revealed that the presence of surveillance cameras, company disclosure in biometric data collection, and the customer's prior knowledge of privacy are crucial factors in triggering a customer's privacy protection behaviour in the service industry. Practical implications for retail store operators and theoretical contributions are discussed. The current study extends the knowledge of privacy protection by incorporating additional factors (privacy literacy) into the existing protection motivation theory. Additionally, it extends its application to the physical environment which is often overlooked. [ABSTRACT FROM AUTHOR]

Fingerprint recognition technology is widely employed for identity verification and access control across diverse domains in which the quality of fingerprint images is critical for accurate biometric identification. However, fingerprint images can be damaged or incomplete due to various factors such as sensor limitations, environmental conditions, or physical injuries. To address this, several approaches including attention mechanism based autoencoders, multi-kernel sequential and multi-kernel stacked autoencoders, and multi-kernel ensemble autoencoders are proposed to perform reconstruction of incomplete and damaged fingerprints. Attention mechanisms play a crucial role by selectively emphasizing important regions and details, further enhancing information capture. By utilizing different autoencoder models with varying kernel sizes, local details and global context can be effectively captured, resulting in more precise restoration. The adoption of multi-kernel sequential and multi-kernel stacked autoencoders enables the extraction of increasingly abstract features, enhancing the model's ability to capture complex fingerprint characteristics. Utilizing autoencoder-based methods for the reconstruction of damaged or incomplete fingerprint images can enhance the accuracy and reliability of fingerprint identification systems leading to improved security and efficiency in real-world applications. Evaluation is conducted using image quality assessment metrics and feature-based matching is utilized to compute fingerprint matching accuracy. The multi-kernel ensemble autoencoder model produces the best reconstruction output with an average fingerprint matching accuracy of 93.81 %. The findings highlight the effectiveness of the proposed work in achieving high-quality reconstruction output and accurate fingerprint matching. The proposed work can be applied in various domains that include law enforcement, security, forensic analysis, and biometric authentication. [ABSTRACT FROM AUTHOR]

The Regulation (EU) 2016/679 on the protection of natural persons regarding the processing of personal data (GDPR) is one of the key fundamental pieces of European legislation to protect human rights and freedoms. However, the development of AI systems that are capable of collecting and processing large amounts of data and predicting user habits and emotional states has affected traditional legal categories and tested their resilience. This paper assesses the limits of the current formulation of the GDPR which does not take expressly into account the category of inferred data as a special category of data. Furthermore, it questions whether the toolbox put in place by the GDPR is still effective in protecting data subjects from practices such as neuromarketing and eye-tracking systems. It shows that it is certainly the essential starting point, but that, on the other hand, cannot be spared criticism. For this, in the recent years, the European legislator has adopted further legislations including, in particular, the Digital Services Act (DSA) and the Artificial Intelligence Act (AIA). Although representing a step forward in protection against such technologies, they each have critical aspects that need to be considered. [ABSTRACT FROM AUTHOR]

Deep learning models have demonstrated significant advantages over traditional algorithms in image processing tasks like object detection. However, a large amount of data are needed to train such deep networks, which limits their application to tasks such as biometric recognition that require more training samples for each class (i.e., each individual). Researchers developing such complex systems rely on real biometric data, which raises privacy concerns and is restricted by the availability of extensive, varied datasets. This paper proposes a generative adversarial network (GAN)-based solution to produce training data (palm images) for improved biometric (palmprint-based) recognition systems. We investigate the performance of the most recent StyleGAN models in generating a thorough contactless palm image dataset for application in biometric research. Training on publicly available H-PolyU and IIDT palmprint databases, a total of 4839 images were generated using StyleGAN models. SIFT (Scale-Invariant Feature Transform) was used to find uniqueness and features at different sizes and angles, which showed a similarity score of 16.12% with the most recent StyleGAN3-based model. For the regions of interest (ROIs) in both the palm and finger, the average similarity scores were 17.85%. We present the Frechet Inception Distance (FID) of the proposed model, which achieved a 16.1 score, demonstrating significant performance. These results demonstrated StyleGAN as effective in producing unique synthetic biometric images. [ABSTRACT FROM AUTHOR]

The advent of the metaverse has given rise to metacrime, a novel category of criminal activities occurring in the metaverse, which not only challenges conventional digital criminality but existing law enforcement frameworks. To address the scholarship vacancy, this study examines the intersections and distinctions between metacrime and conventional cybercrime by employing a multi-disciplinary literature review and comparative analysis. We identified five shared characteristics between these two crime types: crime classification, continuous evolution, hyper-spatial-temporality (global reach), anonymity, and governance challenges. Crucially, our research highlights the distinct epistemological aspects of metacrime through its criminogenic, victimogenic, etiological, ethical, and regulatory dimensions, exemplified by virtual-to-physical attacks, immersive virtual reality attacks, victimization superrealism, complexities of human-avatar interactivity, excessive misuse of biometric data, increasingly vulnerable populations, and avatars' liability. Our findings underscore the imperative need for tailored and forward-thinking regulatory responses to address the intricate challenges of metacrime, thereby ensuring the security and integrity of evolving digital environments. [ABSTRACT FROM AUTHOR]