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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]

Internet of Things (IoT)-assisted consumer electronics refer to common devices that are improved with IoT technology, allowing them to attach to the internet and convey with other devices. These smart devices contain smart home systems, smartphones, wearables, and appliances, which can be monitored remotely, gather, and share data, and deliver advanced functionalities like monitoring, automation, and real-time upgrades. Safety in IoT-assisted consumer electronics signifies a cutting-edge technique to improve device safety and user authentication. Iris recognition (IR) is a biometric authentication technique that employs the exclusive patterns of the iris (the colored part of the eye that surrounds the pupil) to recognize individuals. This method has gained high popularity owing to the uniqueness and stability of iris patterns in finance, healthcare, industries, complex systems, and government applications. With no dual irises being equal and small changes through an individual’s lifetime, IR is considered to be more trustworthy and less susceptible to exterior factors than other biometric detection models. Different classical machine learning (ML)-based IR techniques, the deep learning (DL) approach could not depend on feature engineering and claims outstanding performance. In this paper, we propose an enhanced IR using the Remora fractals optimization algorithm with deep learning (EIR-ROADL) technique for biometric authentication. The main intention of the EIR-ROADL model is to project a hyperparameter-tuned DL technique for automated and accurate IR. For securing consumer electronics, blockchain (BC) technology can be used. In the EIR-ROADL technique, the EIR-ROADL approach uses the Inception v3 method for the feature extraction procedures and its hyperparameter selection process takes place using ROA. For the detection and classification of iris images, the EIR-ROADL technique applies the variational autoencoder (VAE) model. The experimental assessment of the EIR-ROADL algorithm can be executed on benchmark iris datasets. The experimentation outcomes indicated better IR outcomes of the EIR-ROADL methodology with other current approaches and ensured better biometric authentication results. [ABSTRACT FROM AUTHOR]

In the rapidly evolving domain of biometric security, the significance of Fingerprint Presentation Attack Detection (FPAD) has become increasingly paramount, given the susceptibility of Automatic Fingerprint Identification System (AFIS) to advanced spoofing techniques. This systematic literature review (SLR), spanning from 2022 to the second quarter of 2024, delves into the intricate challenges and burgeoning opportunities within FPAD. It focuses on innovative methodologies for detecting presentation attacks, the prevalent challenges posed by spoof fabrications (including materials like silicone, gelatine, and latex), and the exploration of potential advancements in FPAD effectiveness. The comprehensive analysis, based on a rigorous review protocol, scrutinizes 40 seminal peer-reviewed articles from the IEEE Xplore and ScienceDirect databases. This exploration uncovers a diverse range of strategies in FPAD, including software-centric and hardware-assisted approaches, each bearing unique implications for security enhancement and user privacy considerations. A pivotal finding of this review is the identification of critical research gaps, particularly in the development of algorithms capable of universal detection, the system's adaptability to novel spoofing materials, and the ethical management of biometric data. This review provides a contemporary assessment of the current state of FPAD and establishes a foundation for future research directions. It highlights the need for continuous innovation in response to the evolving sophistication of spoofing techniques and the imperative of maintaining a balance between robust security measures and user-centric design in biometric systems. This review underscores the dynamic interplay between technological advancements, the ingenuity of attackers, and the ongoing endeavour to achieve reliable, user-friendly, and ethically responsible biometric security solutions. [ABSTRACT FROM AUTHOR]

The Global Mobility Network (GLOMONET) is crucial in the context of the Internet of Things (IoT) as it enables users to connect to the Internet from anywhere, anytime. However, while GLOMONET offers numerous benefits, it also present security challenges that need to be addressed. To ensure secure mutual authentication and prevent attacks such as impersonation, physical attacks, and DoS attacks, GLOMONET must provide strong anonymity, address loss of synchronization issues, and incorporate unclonable devices as an essential security feature. Therefore, this article proposes a new authentication protocol for GLOMONET that addresses these security issues. A formal analysis using Mao and Boyd's logic showed that the proposed protocol provides secure mutual authentication properties, and the RoR model demonstrated its resistance to attacks. In addition, the Programming Model Analysis (Scyther tool) confirmed that the proposed protocol meets security features and can withstand known attacks. The time complexity demonstration shows that the proposed protocol has a total complexity time of 5.482 seconds, the lowest time in the comparison subsection, indicating its suitability for use in GLOMONET. [ABSTRACT FROM AUTHOR]

In today's educational landscape, the online examination system has become crucial, particularly due to the challenges posed by the coronavirus disease 2019 (COVID-19) pandemic. Despite its advantages in expediting result dissemination and reducing resource consumption, online examinations face significant security threats like leakage, cheating, fraud, and hacking, which hinder their widespread adoption. This paper addresses these security concerns by proposing integrating advanced security algorithms and biometric devices. It presents a comprehensive literature review on existing online examination systems, focusing on their security mechanisms, and compares these findings with a proposed framework. Additionally, a questionnaire was administered across Jordanian governmental and private universities to explore strategies for safeguarding computerized tests through encryption and authentication methods. The results reveal that Jordanian institutions lack adequate security safeguards and procedural standards. Key recommendations include encrypting the question bank stored in databases and employing biometric identification techniques to enhance the security and effectiveness of student verification. The proposed framework aims to improve the overall security, speed, and secrecy of the online examination process, addressing the critical gaps identified in current systems. This research contributes to developing more secure and reliable online examination systems in higher education. [ABSTRACT FROM AUTHOR]

Gait recognition (GR) has emerged as a significant biometric identification technique, leveraging an individual's walking pattern for various applications such as surveillance, forensic analysis, and person identification. Despite its non-intrusive nature, GR systems face challenges due to their sensitivity to pose variations, limiting functionality in real-world scenarios where people exhibit diverse walking styles and body orientations. This review paper aims to comprehensively discuss GR systems, focusing on approaches and challenges in designing accurate and robust systems capable of handling bodily variations. GR's prominence spans across domains including surveillance, security, healthcare, and human-computer interaction, positioning it as a versatile biometric modality complementary to the traditional methods like fingerprint and face recognition. The review offers an in-depth analysis of GR systems, detailing silhouette-based, model-based, and deep-learning approaches. Silhouette-based methods capture gait information by analyzing the outline and locomotion of a person's silhouette, while model-based approaches utilize skeletal models to describe gait patterns. The paper elucidates the challenges and limitations of GR systems, encompassing factors such as walking conditions, clothing, viewpoint, and environmental influences. Additionally, it explores potential future directions in GR research, highlighting the technology's ongoing evolution and integration into diverse applications. As a valuable resource, this review serves researchers, practitioners, and policymakers by providing insights into the current state of GR systems and avenues for further research and development. It underscores the importance of addressing challenges to enhance GR's accuracy and robustness, ensuring its continued relevance in biometric identification across various domains. [ABSTRACT FROM AUTHOR]

Introduction: People with hypermobile Ehlers–Danlos syndrome (hEDS) experience multisystemic dysfunction with varying severity and unpredictability of flare occurrence. Cohort studies suggest that individuals with hEDS have a higher risk for autonomic dysfunction. The gold standard for assessing autonomic function, clinically, is the heart rate variability (HRV) assessment from 24-h Holter monitor electrocardiogram data, but this is expensive and can only be performed in short durations. Since their advent, biometric devices have been a non-invasive method for tracking HRV to assess autonomic function. This study aimed to understand the intra- and inter-individual variability in autonomic function and to associate this variability with gastrointestinal symptoms in individuals with hEDS using wearable devices. Methods: We studied 122 days of biometric device data from 26 individuals, including 35 days highlighted as high gastrointestinal (GI) dysfunction and 48 days as low GI dysfunction. Utilizing wavelet analysis to assess the frequency domains of heart rate signals, we compared participants' HRV data for high, low, very low (VLF), and ultralow (ULF) frequency domains associated with physiological differences. Results: We found a significant difference between the VLF and ULF signals on high-GI symptom days compared with low-symptoms days for 92 and 76% of the signals sampled, respectively. Discussion: Our pilot data show a change in HRV for individuals with hEDS experiencing a flare day for a single-body system. Future research will focus on evaluating the relationship between longitudinal multisystemic symptom severity fluctuations and HRV. [ABSTRACT FROM AUTHOR]

In light of the demographic shift towards an aging population, there is an increasing prevalence of dementia among the elderly. The negative impact on mental health is preventing individuals from taking proper care of themselves. For individuals requiring hospital care, those receiving home care, or as a precaution for a specific individual, it is advantageous to utilize monitoring equipment to track their biological parameters on an ongoing basis. This equipment can minimize the risk of serious accidents or severe health hazards. The objective of the present research project is to design an armband with an accurate location tracking system. This is of particular importance for individuals with dementia and Alzheimer's disease, who frequently leave their homes and are unable to find their way back. The proposed armband also includes a fingerprint identification system that allows only authorized personnel to use it. Furthermore, in hospitals and healthcare facilities the biometric identification system can be used to trace periodic medical or nursing visits. This process improves the reliability and transparency of healthcare. The test results indicate that the armband functions in accordance with the desired design specifications, with performance evaluation of the main features including fall detection, where a hit rate of 100% was obtained, a fingerprint recognition test demonstrating accuracy from 88% to 100% on high-quality samples, and a GPS tracking test determining position with a difference of between 1.8 and 2.1 m. The proposed solution may be of benefit to healthcare professionals, supported housing providers, elderly people as target users, or their family members. [ABSTRACT FROM AUTHOR]

This study proposes a biometric authentication method using infrared (IR)-based periocular images captured in virtual reality (VR) environments with head-mounted displays (HMDs). The widespread application of VR technology highlights the growing need for robust user authentication in immersive environments. To address this, the study introduces a novel periocular biometric authentication system optimized for HMD usage. Ensuring reliable authentication in VR environments necessitates overcoming significant challenges, including flicker noise and infrared reflection. Flicker noise, caused by alternating current (AC)-powered lighting, produces banding artifacts in images captured by rolling-shutter cameras, obstructing biometric feature extraction. Additionally, IR reflection generates strong light glare on the iris surface, degrading image quality and negatively impacting the model's generalization performance and authentication accuracy. This study utilized the AffectiVR dataset, which includes noisy images, to address these challenges. In the preprocessing phase, iris reflections were removed, reducing the Equal Error Rate (EER) from 6.73% to 5.52%. Furthermore, incorporating a Squeeze-and-Excitation (SE) block to mitigate flicker noise and enhance model robustness resulted in a final EER of 6.39%. Although the SE block slightly increased the EER, it significantly improved the model's ability to suppress noise and focus on critical periocular features, ensuring enhanced robustness in challenging VR environments. Heatmap analysis revealed that the proposed model effectively utilized periocular features, such as the skin around the eyes and eye contours, compared to prior approaches. This study establishes a crucial groundwork for advancing robust biometric authentication systems capable of overcoming noise challenges in next-generation immersive platforms. [ABSTRACT FROM AUTHOR]

Summary: Due to the proliferation of the wireless communication technologies, we are now in the era of ubiquitous computing. People can enjoy services almost in any time and any location based on the mobile intelligent devices. Meanwhile, more and more personal information are transmitted through the network. To protect these sensitive information, authentication and privacy protection are primary concerns in ubiquitous communication environments. However, traditional physical‐based biometric authentication scheme usually requires explicit cooperation of the user, which is inconvenient to the users. In order to solve this problem, a behavior biometric authentication protocol with privacy protection is designed using matrix multiplication and homomorphic encryption for ubiquitous communication environments. The correctness, security, and privacy protection properties are proved with security analysis. Performance evaluation shows that our protocol achieves stronger security and higher communication efficiency compared with other related protocols, which makes it very suitable for ubiquitous communication environments because communication consumes much more energy than computation does in mobile communication. [ABSTRACT FROM AUTHOR]

Secure user authentication has grown importance in today's modern culture. It is significant to authenticate the user identity in numerous consumer applications particularly financial transactions. Traditional authentication methods rely on easy-to-guess passwords, PIN numbers, or tokens with several security flaws, such as those printed on the back of credit cards for PIN numbers. As an alternative to current systems, biometric authentication techniques based on physical and behavioral characteristics have been proposed. Multibiometric systems, which combine several biometrics, are developed as a result of the difficulties that single-biometric authentication systems encountered in real-world applications including lack of precision and noisy data. The proposed system provides better performance and greater accuracy compared with other authentication techniques. The majority of them is inconvenient and demand complicated user interactions. This paper proposes Enhanced Elman Spike Neural Network along Glowworm Swarm Optimization (EESNN-GSO-AMT) for Multiple Transaction Authentication. The images are collected via SDUMLA-HMTalong CASIA V5 dataset. The pictures are provided to pre-processing to enhance the images quality utilizing Learnable Edge Collaborative Filter (LECF). The preprocessed images are fed to feature extraction using Adaptive and concise empirical wavelet transform (ACEWT) and the features are extracted such as entropy, homogeneity, energy and contrast. The extracting features are provided to EESNN classifier to categorize authorized or unauthorized persons. In general, the EESNN classifier does not express adapting optimization methods to determine ideal parameters to ensure accurately. Therefore, it is proposed to utilize the Glowworm Swarm Optimization to enhanceEESNN, which accurately categorizes the authorized and unauthorized person. The efficiency of the proposed approach is assessed usingsome metrics. The proposed EESNN-GSO-AMT method attains higher accuracy 20.54%, 21.76% and 23.89%; greater sensitivity 20.12% 20.34% and 21.43%; higher precision 23.34%, 22.68% and 24.34% are analyzed to the existing methods, like Optimal feature level fusion for safe human authentication in multimodal biometric scheme (OptGWO-AMT-FV), Joint attention network for finger vein authentication (JAnet-AMT-FV), Finger Vein Recognition Utilizing Deep Learning Technique (DCNN-AMT-FV) respectively. [ABSTRACT FROM AUTHOR]

Today, cloud computing has received greater attention for storing and processing huge amounts of data over the internet. Security concerns are one of the most challenging issues that have affected the growth of cloud computing services. Data storage in the cloud is unsecured using a password system that can be easily stolen by intruders. Hence, there is a need to achieve cloud data security using multimodal biometric cryptosystem-based authentication and encryption systems and to mitigate the risk of attackers, especially in the case of accessing the data from unauthorized users. In this paper, we propose a Secure Multimodal Cloud Authentication and Authorization Scheme (SMCAAS) framework that brings additional security to both encryption and authentication for user biometric identification against different attacks in the cloud environment. In the SMCAAS framework, we presented an improved encryption system using the Biometric Identity Based Encryption Method (BIBE) to facilitate the exchange of the trapdoor between the user's Biometric Identity Record (BIR) and the cloud server with additional security measures in cloud computing environments. Then a trusted cryptographic hash function uses the Secure Hash Algorithm-512 (SHA-512) to authenticate the biometric user who intends to access the system. Based on the derived two steps, the Multimodal Biometric Authentication Module (MBAM) module performs multimodal biometric features to provide additional security and is designed to verify the user's biometric identity in a cloud environment. Informal security analysis proves that the SMCAAS approach is secure against different attacks. Numerical results demonstrate that the SMCAAS approach obtains high security in terms of error rate (ERR) and biometric quality metrics, and also that the performance measures of the encryption and decryption time for the message file achieve less average time than other methods. [ABSTRACT FROM AUTHOR]

The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over traditional biometric techniques. PPG signals are non-invasive, inherently contain liveness information that is highly resistant to spoofing, and are cost-efficient, making them a superior alternative for biometric authentication. A comprehensive protocol was established to collect PPG signals from 40 subjects using a custom-built acquisition system. These signals were then transformed into two-dimensional representations through the Gram matrix conversion technique. To analyze and authenticate users, we employed an EfficientNetV2 B0 model integrated with a Long Short-Term Memory (LSTM) network, achieving a remarkable 99% accuracy on the test set. Additionally, the model demonstrated outstanding precision, recall, and F1 scores. The refined model was further validated in real-time identification scenarios, underscoring its effectiveness and robustness for next-generation biometric recognition systems. [ABSTRACT FROM AUTHOR]

We developed 3 formulas to predict vaults in eyes with vertical ICL implantation and compared the performance with those of machine learning, both of which achieved sufficient performance. Purpose: To design formulas for predicting postoperative vaults in vertical implantable collamer lens (ICL) implantation and to achieve more precise predictions using machine learning models. Design: Retrospective, observational study. Setting: Eye Clinic Tokyo Methods: We retrospectively reviewed the medical records of 720 eyes in 408 patients who underwent vertical ICL implantation. The data included age, sex, refractions, anterior segment biometric data, and surgical records. We designed 3 formulas (named V1 to V3 formulas) using multiple linear regression analysis and tested 4 machine learning models. Results: Predicted vaults by V1 to V3 formulas were 444.17 ± 93.83 μm, 444.08 ± 98.64 μm, and 444.27 ± 108.81 μm, with a mean absolute error of 127.97 ± 107.92 μm, 126.41 ± 105.86 μm, and 122.90 ± 103.00 μm, respectively. There were no significant differences in error among the V1 to V3 formulas, despite the fact that the V1 and V2 formulas referred to limited parameters (3 and 4, respectively) and the V3 formula referred to all 12 parameters. 2 of 4 machine learning models—Extreme Gradient Boosting and Random Forest Regressor—showed better performance in predicted vaults: 444.52 ± 120.51 μm and 446.00 ± 102.55 μm, and mean absolute error: 118.31 ± 100.55 μm and 118.63 ± 99.34 μm, respectively. Conclusions: This is the first study to design V1 to V3 formulas for vertical ICL implantation. The V1 and V2 formulas exhibited good performance despite the limited parameters. In addition, 2 of the 4 machine learning models predicted more precise results. [ABSTRACT FROM AUTHOR]

In recent years, researchers have focused on the biometric applications of bioelectrical signals, particularly electroencephalograms (EEG), to enhance information security. Using EEG as a biometric offers advantages that cannot be forgotten or forged. One approach to utilizing EEG signals for biometric purposes involves recording auditory evoked potentials (AEP). AEPs are electrical potentials that arise in response to auditory stimulation in the cerebral cortex. These signals are stimulus-dependent and can vary with the auditory stimulus, allowing these signals to be employed even if the registered signal was compromised. In this paper, discriminative features are extracted and classified using convolutional neural networks. A dataset recorded from 20 users using auditory stimulation is analyzed. The reported results demonstrate a classification accuracy of 98.99% in identification mode and an equal error rate of 1.18% in verification mode. These outcomes showcase the proposed method's high accuracy, marking an improvement over existing methods. Furthermore, the system's practicality is enhanced by utilizing fewer channels, and its performance is assessed by reducing the number of channels. [ABSTRACT FROM AUTHOR]

Owing to its capability to offer remote services, the Internet of Things (IoT) has immersed itself in all areas of our daily lives. However, this big use of IoT networks makes the user's data change insecurely in open channels vulnerable to malicious use. As a result, the security of the user's data in an IoT environment becomes a critical issue. Given that authentication is a mechanism that may prevent hackers from retrieving and exploiting data communicated between IoT devices, researchers have proposed many lightweight IoT authentication schemes in the last decades. However, most of these schemes are based on two authentication factors and are unable to ensure unlink ability, key secrecy, perfect forward secrecy, and resistance to node capture, denial of service (DoS) attacks, stolen verifiers, denning-SSACO attacks, and GWN bypassing. In this paper, we present an anonymous three-factor authentication scheme based on elliptic curve cryptography (ECC), which can provide all security services and resist well-known attacks. Then, based on informal security analysis and the formal security proof using ProVerif we show that our provided scheme is secure and can resist known attacks. Finally, we show the comparison result among our protocol and other protocols in terms of computation overheads, communication overheads, and security features. [ABSTRACT FROM AUTHOR]

Authentication is the most crucial aspect of security and a predominant measure employed in cybersecurity. Cloud computing provides a shared electronic device resource for users via the internet, and the authentication techniques used must protect data from attacks. Previous approaches failed to resolve the challenge of making passwords secure, memorable, usable, and time-saving. Graphical Password (GP) is still not widely utilized in reality because consumers suffer from multiple login stages. This paper proposes an Indexed Choice-Based Graphical Password (ICGP) scheme for improving the authentication part. ICGP consists of two stages: registration and authentication. At the registration stage, the user registers his/her data user name a number called Index Number (IN), and chooses an image from a grid of images. After completing the registration, ICGP gives the user a random unique number (UNo) to be a user ID. At the authentication stage, the user chooses a different image from the grid based on the random appearance of the registered image dimensions on the grid plus the registered Index Number. ICGP password is a combination of three factors; user's name, UNo, and any image. According to the experiments, the proposed ICGP has achieved great improvements when compared to prior methods. The ICGP has increased the possible password numbers from 9.77e + 6 to 3.74e + 30, the password space from 1.20e + 34 to 1.37e + 84, and decreased the password entropy from 7.16e − 7 to 8.26e − 30. [ABSTRACT FROM AUTHOR]

Different biological data may be used to identify people in this investigation. The system uses complex multimodal fusion, feature extraction, classification, template matching, adjustable thresholding, and more. A trustworthy multimodal feature vector (B) is created using the Multimodal Fusion Algorithm from voice, face, and fingerprint data. The key objectives are weighing, normalizing, and extracting characteristics. Complex feature extraction algorithms improve this vector and ensure its accuracy and reliability. Hamming distance is utilized in template matching for accuracy. Support vector machines to ensure classification accuracy. The adaptive threshold technique adjusts option limits based on the biology score mean and standard deviation when external conditions change. A thorough look at the research shows how algorithms operate together and how vital each aspect is for locating criminals. Change the multimodal fusion weights for optimum results. Thorough research using tables and photographs revealed that the fingerprint approach is optimal. Fast, simple, and precise technologies may enable new unlawful recognition tools. The adaptive thresholding algorithm's multiple adaptation steps allow the system to adjust to diverse study circumstances. The Multimodal Biometric Identification System is a cutting-edge leader in its area and provides a trustworthy, practical, and customizable research choice. This novel strategy is at the forefront of criminal recognition technology and has been supported by ablation research. It affects reliability, accuracy, and adaptability. [ABSTRACT FROM AUTHOR]

In the domain of animal management, the technology for individual livestock identification is in a state of continuous evolution, encompassing objectives such as precise tracking of animal activities, optimization of vaccination procedures, effective disease control, accurate recording of individual growth, and prevention of theft and fraud. These advancements are pivotal to the efficient and sustainable development of the livestock industry. Recently, visual livestock biometrics have emerged as a highly promising research focus due to their non-invasive nature. This paper aims to comprehensively survey the techniques for individual livestock identification based on computer vision methods. It begins by elucidating the uniqueness of the primary biometric features of livestock, such as facial features, and their critical role in the recognition process. This review systematically overviews the data collection environments and devices used in related research, providing an analysis of the impact of different scenarios on recognition accuracy. Then, the review delves into the analysis and explication of livestock identification methods, based on extant research outcomes, with a focus on the application and trends of advanced technologies such as deep learning. We also highlight the challenges faced in this field, such as data quality and algorithmic efficiency, and introduce the baseline models and innovative solutions developed to address these issues. Finally, potential future research directions are explored, including the investigation of multimodal data fusion techniques, the construction and evaluation of large-scale benchmark datasets, and the application of multi-target tracking and identification technologies in livestock scenarios. [ABSTRACT FROM AUTHOR]

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The purpose of this study was to determine the actual diagnostic status of aseptic meningitis and consider the factors that cause difficulties in its diagnosis.This study retrospectively reviewed the medical records of aseptic meningitis treated at our hospital from 2013 to 2022 and compared biometric data to distinguish between timely diagnose and difficult diagnose cases.This retrospective observational study included 127 patients aged 18 years or older. 66 (52.0 %) were female, with a median age of 35.9±15.9 years. The main symptoms were headache (122, 96.1 %), general malaise (110, 86.6 %), vomiting (79, 62.2 %). The 127 patients were classified into two groups: A timely diagnosis group diagnosed within 6 days of symptom onset, and a difficult diagnosis group diagnosed within 7 days or longer. There were significant differences between the two groups in the proportion of patients with a history of antimicrobial treatment and fever above 38 °C, and in the positive rates of neck stiffness and jolt accentuation of headache (JAH). The total number of hospitals involved in the process of diagnosis was significantly higher in the difficult diagnosis group and the length of inpatient stay was significantly longer. Multivariate analysis revealed significant differences in neck stiffness, JAH, and prior antibacterial therapy.Atypical cases, such as neck stiffness and JAH negativity, may make the diagnosis difficult. Clinicians should be aware of this atypical presentation of aseptic meningitis. [ABSTRACT FROM AUTHOR]

The article discusses the presence of wild boars on Crete since prehistory. It is noted that the evidence for wild or feral pigs on Crete is often unclear. Biometric data do not support the existence of a stable wild population on the island. It is emphasized that further systematic data collection is necessary. The study deals with archaeological investigations of animal bone findings in various regions, including Crete, Cyprus, and Turkey, and examines aspects of animal husbandry and hunting in prehistoric societies. [Extracted from the article]

AbstractSoftware-driven wearable technologies are emerging as a control for heat-related illnesses. Such devices collect biometric data and estimate risk noninvasively. However, little is known about workplace implementation strategies and stakeholder acceptance of the devices. As part of a mixed-methods pilot study to evaluate the feasibility of wearable technologies, the authors invited six agricultural workers with no device experience to participate in a semi-structured focus group, after wearing two devices (e.g., MākuSafe, Des Moines, IA, United States; SlateSafety, V2, Atlanta, GA, United States) for a standard work week. The focus group was separated into two parts: the first assessed the overall understanding of heat stress, and the second captured workers’ perceptions of the wearable technologies. For each topic, the authors extracted relevant themes that describe farm workers’ general understanding of heat hazards and worker interaction with wearable technology used in heat-related risk. These themes provide relevant answers to the questions outlined in the semi-structured questionnaire that can guide future research into the use of these devices in occupational settings. Wearable technologies continue to be used to control heat-related illnesses. Therefore, it is critically important to gather key strategies for employer implementation and user-interface considerations. [ABSTRACT FROM AUTHOR]

Biometric stress monitoring has become a critical area of research in understanding and managing health problems resulting from stress. One of the fields that emerged in this area is biometric stress monitoring, which provides continuous or real-time information about different anxiety levels among people by analyzing physiological signals and behavioral data. In this paper, we propose a new approach based on the CapsNets model for continuously monitoring psychophysiological stress. In the new model, streams of biometric data, including physiological signals and behavioral patterns, are taken up for analysis. In testing using the Swell multiclass dataset, it performed with an accuracy of 92.76%. Further testing of the WESAD dataset reveals an even better accuracy at 96.76%. The accuracy obtained for binary classification of stress and no stress class is applied to the Swell dataset, where this model obtained an outstanding accuracy of 98.52% in this study and on WESAD, 99.82%. Comparative analysis with other state-of-the-art models underlines the superior performance; it achieves better results than all of its competitors. The developed model is then rigorously subjected to 5-fold cross-validation, which proved very significant and proved that the proposed model could be effective and efficient in biometric stress monitoring. [ABSTRACT FROM AUTHOR]

This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the unique characteristics and forgery resistance of ECG signals. The proposed system is designed using autocorrelation profiles (ACPs) and a convolutional neural network and is optimized for real-time processing even in constrained hardware environments. Additionally, advanced signal processing algorithms were applied to refine the ECG data and minimize noise in driving environments. The system's performance was evaluated using a public dataset of 154 participants and a real-world dataset of 10 participants, achieving F1-Scores of 96.8% and 96.02%, respectively. Furthermore, an ablation study was conducted to analyze the importance of components such as ACPs, normalization, and filtering. When all components were removed, the F1-Score decreased to 60.1%, demonstrating the critical role of each component. These findings highlight the potential of the proposed system to deliver high accuracy and efficiency not only in vehicle environments but also in various security applications. [ABSTRACT FROM AUTHOR]

Background: Student wellness is of increasing concern in medical education. Increased rates of burnout, sleep disturbances, and psychological concerns in medical students are well documented. These concerns lead to impacts on current educational goals and may set students on a path for long-term health consequences. Methods: Undergraduate medical students were recruited to participate in a novel longitudinal wellness tracking project. This project utilized validated wellness surveys to assess emotional health, sleep health, and burnout at multiple timepoints. Biometric information was collected from participant Fitbit devices that tracked longitudinal sleep patterns. Results: Eighty-one students from three cohorts were assessed during the first semester of their M1 preclinical curriculum. Biometric data showed that nearly 30% of the students had frequent short sleep episodes (<6 hours of sleep for at least 30% of recorded days), and nearly 68% of students had at least one episode of three or more consecutive days of short sleep. Students that had consecutive short sleep episodes had higher rates of stress (8.3%) and depression (5.4%) symptoms and decreased academic efficiency (1.72%). Conclusions: Biometric data were shown to significantly predict psychological health and academic experiences in medical students. Biometrically assessed sleep is poor in medical students, and consecutive days of short sleep duration are particularly impactful as it relates to other measures of wellness. Longitudinal, biometric data tracking is feasible and can provide students the ability to self-monitor health behaviors and allow for low-intensity health interventions. [ABSTRACT FROM AUTHOR]

PURPOSE: A major barrier to the incorporation of biometric data into clinical practice is the lack of device integration with electronic medical records (EMRs). We developed infrastructure to transmit biometric data from an Apple Watch into the EMR for physician review. The study objective was to test feasibility of using this infrastructure for patients undergoing radiotherapy. METHODS: The study included patients with breast or prostate cancer receiving ≥3 weeks of radiotherapy who reported owning an Apple Watch. Daily resting heart rate (HR), HR variability, step count, and exercise minutes were automatically transferred to our EMR using a custom app installed on each patient's iPhone. Biometric data were presented to the treating radiation oncologist for review on a weekly basis during creation of the on-treatment note. Feasibility was defined a priori as physician review of biometric data for at least 90% of patients. Time trends in biometric data were tested using the Jonckheere-Terpstra test. Patient satisfaction was assessed using the System Usability Scale (SUS), with scores above 80 considered above-average user experience. RESULTS: Of the 20 patients enrolled, biometric data were successfully transmitted to the EMR and reviewed by the radiation oncologist for 95% (n = 19) of patients, thus meeting the a priori feasibility threshold. For patients with radiation courses ≥4 weeks, exercise minutes decreased over time (P =.01) and daily mean HR variability increased over time (P =.02). The median SUS was 82.5 (IQR, 70-87.5). CONCLUSION: Our study demonstrates the feasibility of real-time integration of biometric data collected from an Apple Watch into the EMR with subsequent physician review. The high rates of physician review and patient satisfaction provide support for further development of large-scale collection of wearable device data. [ABSTRACT FROM AUTHOR]

This article continues the discussion on advanced jurisprudence, outlined in "Algorithms, Ethics and Justice" where restorative justice was proposed for the mitigation of artificial intelligence (AI) crimes. "Algorithms, Ethics and Justice" proposed an alternative approach to the current legal system by looking into restorative justice for AI crimes and how the ethics of care could be applied to AI technologies. This article will expand the notion of cybercrimes from AI crimes to extended reality (XR) crimes, given the rise of the metaverse, and the future scenario of biometric data of EEG-capable headsets being misused by rogue companies and/or criminals. The article will do so first by discussing Mill's text On Liberty, to serve as a context for exploring open justice in XR, and then by continuing the discussion around the right to be forgotten and the freedom of the press versus privacy, through a comparative analysis between the legal situation in the EU and that of the US. The article concludes by reviewing possible international open justice scenarios for XR criminals. [ABSTRACT FROM AUTHOR]

In this study, the length–weight relationships of Pontastacus leptodactylus, a freshwater crayfish species found in the Keban Dam Lake, were assessed using both conventional methods and artificial intelligence techniques. Throughout the research process, all biometric measurements of the crayfish were meticulously recorded, including TL, TW, and other biometric data. These measurements were analyzed using both the conventional length–weight relationship method and artificial neural networks. The results obtained using artificial neural networks and conventional methods were compared, and the analysis was based on MAPE and R2 performance criteria. The study showed that the ANNs method outperformed the conventional LWR method, showing more accurate results. The models employed to predict the length–weight relationships of the crayfish demonstrated high accuracy, and the Artificial Neural Networks method was identified as the most effective model. These results provide strong evidence that the ANNs method performs significantly better in predicting the LWRs of freshwater crayfish. [ABSTRACT FROM AUTHOR]

The biometric authentication in today's digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346. [ABSTRACT FROM AUTHOR]

In an intelligent driving environment, monitoring the physiological state of drivers is crucial for ensuring driving safety. This paper proposes a method for monitoring and analyzing driver physiological characteristics by combining electronic vehicle identification (EVI) with multimodal biometric recognition. The method aims to efficiently monitor the driver's heart rate, breathing frequency, emotional state, and fatigue level, providing real-time feedback to intelligent driving systems to enhance driving safety. First, considering the precision, adaptability, and real-time capabilities of current physiological signal monitoring devices, an intelligent cushion integrating MEMSs (Micro-Electro-Mechanical Systems) and optical sensors is designed. This cushion collects heart rate and breathing frequency data in real time without disrupting the driver, while an electrodermal activity monitoring system captures electromyography data. The sensor layout is optimized to accommodate various driving postures, ensuring accurate data collection. The EVI system assigns a unique identifier to each vehicle, linking it to the physiological data of different drivers. By combining the driver physiological data with the vehicle's operational environment data, a comprehensive multi-source data fusion system is established for a driving state evaluation. Secondly, a deep learning model is employed to analyze physiological signals, specifically combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN extracts spatial features from the input signals, while the LSTM processes time-series data to capture the temporal characteristics. This combined model effectively identifies and analyzes the driver's physiological state, enabling timely anomaly detection. The method was validated through real-vehicle tests involving multiple drivers, where extensive physiological and driving behavior data were collected. Experimental results show that the proposed method significantly enhances the accuracy and real-time performance of physiological state monitoring. These findings highlight the effectiveness of combining EVI with multimodal biometric recognition, offering a reliable means for assessing driver states in intelligent driving systems. Furthermore, the results emphasize the importance of personalizing adjustments based on individual driver differences for more effective monitoring. [ABSTRACT FROM AUTHOR]

This contribution examines the legal intricacies surrounding law enforcement data collection practices in Bulgaria within the framework of the Law Enforcement Directive 2016/680 (LED) and relevant Court of Justice of the European Union (CJEU) rulings, particularly cases C-205/21 and C-80/23. The analysis underscores challenges in interpreting the concept of "strict necessity" and ensuring compliance with its provisions, in particular, Art. 10. Key findings reveal ambiguities in the LED's application, particularly concerning judicial review and the scope of data collection. The subsequent Case C-80/23 further seeks clarification on the strict necessity standard and the scope of judicial review in the collection of biometric and genetic data. The outcome of both cases has broader implications for EU member states, highlighting the need for legislative alignment and underscoring the complexities and challenges in balancing effective law enforcement with the protection of fundamental rights. [ABSTRACT FROM AUTHOR]

This article explores how IDEMIA, a French security company, constructed an infrastructure for the identification and authentication services of the Colombian National Civic Registry (NCR) for more than 20 years of contractual relationships. The paper is divided into two sections. In the first part, we detail the history of infrastructure identification development by the NCR. The contracting model imposed a state action that allowed IDEMIA to grow together with NCR to create an infrastructure that ties all types of agencies and institutions as users of their technological solutions and services. By controlling the infrastructure and expanding its reach to other sectors, IDEMIA has been able to experiment with new technologies, such as a facial recognition engine and digital ID wallet, to generate new dependencies. In the second part, we expose three controversies in which the NCR has defended its exclusive competence against the National Police, the Government, and the private sector. Thus, it secured IDEMIA's position as a key provider of technological solutions for the NCR's public service. This case shows the commodification of legal identity, the entanglement of public and private interests that makes it hard to differentiate them, and the importance of historical analysis to explore the infrastructure power of technology companies. [ABSTRACT FROM AUTHOR]

Conventional authentication methods, such as passwords and PINs, are vulnerable to multiple threats, from sophisticated hacking attempts to the inherent weaknesses of human memory. This highlights a critical need for a more secure, convenient, and user-friendly approach to authentication. This paper introduces M2auth, a novel multimodal behavioral biometric authentication framework for smartphones. M2auth leverages a combination of multiple authentication modalities, including touch gestures, keystrokes, and accelerometer data, with a focus on capturing high-quality, intervention-free data. To validate the efficacy of M2auth, we conducted a large-scale field study involving 52 participants over two months, collecting data from touch gestures, keystrokes, and smartphone sensors. The resulting dataset, comprising over 5.5 million action points, serves as a valuable resource for behavioral biometric research. Our evaluation involved two fusion scenarios, feature-level fusion and decision-level fusion, that play a pivotal role in elevating authentication performance. These fusion approaches effectively mitigate challenges associated with noise and variability in behavioral data, enhancing the robustness of the system. We found that the decision-level fusion outperforms the feature level, reaching a 99.98% authentication success rate and an EER reduced to 0.84%, highlighting the robustness of M2auth in real-world scenarios. [ABSTRACT FROM AUTHOR]

Today, many businesses use near-field communications (NFC) payment solutions, which allow them to receive payments from customers quickly and smoothly. However, this technology comes with cyber security risks which must be analyzed and mitigated. This study explores the cyber risks associated with NFC transactions and examines strategies for mitigating these risks, focusing on payment devices. This paper provides an overview of NFC technology, related security vulnerabilities, privacy concerns, and fraudulent activities. It then investigates payment devices such as smartphones, contactless cards, and wearables, highlighting their features and vulnerabilities. The study also examines encryption, authentication, tokenization, biometric authentication, and fraud detection methods as risk mitigation strategies. The paper synthesizes theoretical frameworks to provide insights into NFC transaction security and offers stakeholder recommendations. [ABSTRACT FROM AUTHOR]

Background/Objectives: In contrast to traditional biometric modalities, such as facial recognition, fingerprints, and iris scans or even DNA, the research orientation towards chest X-ray recognition has been spurred by its remarkable recognition rates. Capturing the intricate anatomical nuances of an individual's skeletal structure, the ribcage of the chest, lungs, and heart, chest X-rays have emerged as a focal point for identification and verification, especially in the forensic field, even in scenarios where the human body damaged or disfigured. Discriminative feature embedding is essential for large-scale image verification, especially in applying chest X-ray radiographs for identity identification and verification. This study introduced a self-residual attention-based convolutional neural network (SRAN) aimed at effective feature embedding, capturing long-range dependencies and emphasizing critical spatial features in chest X-rays. This method offers a novel approach to person identification and verification through chest X-ray categorization, relevant for biometric applications and patient care, particularly when traditional biometric modalities are ineffective. Method: The SRAN architecture integrated a self-channel and self-spatial attention module to minimize channel redundancy and enhance significant spatial elements. The attention modules worked by dynamically aggregating feature maps across channel and spatial dimensions to enhance feature differentiation. For the network backbone, a self-residual attention block (SRAB) was implemented within a ResNet50 framework, forming a Siamese network trained with triplet loss to improve feature embedding for identity identification and verification. Results: By leveraging the NIH ChestX-ray14 and CheXpert datasets, our method demonstrated notable improvements in accuracy for identity verification and identification based on chest X-ray images. This approach effectively captured the detailed anatomical characteristics of individuals, including skeletal structure, ribcage, lungs, and heart, highlighting chest X-rays as a viable biometric tool even in cases of body damage or disfigurement. Conclusions: The proposed SRAN with self-residual attention provided a promising solution for biometric identification through chest X-ray imaging, showcasing its potential for accurate and reliable identity verification where traditional biometric approaches may fall short, especially in postmortem cases or forensic investigations. This methodology could play a transformative role in both biometric security and healthcare applications, offering a robust alternative modality for identity verification. [ABSTRACT FROM AUTHOR]

Critical applications ranging from sensitive military data to restricted area access demand selective user authentication. The prevalent methods of tokens, passwords, and other commonly used techniques proved deficient as they can be easily stolen, lost, or broken to gain illegitimate access, leading to data spillage. Since data safety against tricksters is a significant issue nowadays, biometrics is one of the unique human characteristic-based techniques that may give better solutions in this regard. The technique entails biometric authentication of users based on an individual's inimitable physiological or behavioral characteristics to provide access to a specific application or data. This paper provides a detailed description of authentication and its approaches, focusing on biometric-based authentication methods, the primary challenges they encounter, and how they have been addressed. The tabular view shows the benefits and downsides of various multimodal biometric systems, and open research challenges. To put it another way, this article lays out a roadmap for the emergence of multimodal biometric-based authentication, covering both the challenges and the solutions that have been proposed. Further, the urge to develop various multi-trait-based methods for secure authentication and data privacy is focused. Lastly, some multimodal biometric systems comprising fingerprint and iris modalities have been compared based on False Accept Rate (FAR), False Reject Rate (FRR), and accuracy to find the best secure model with easy accessibility. [ABSTRACT FROM AUTHOR]

The biometry and reproduction of the Whiskered tern Chlidonias hybrida hybrida were studied in Lake Tonga (El-Kala, North-east Algeria), the only North African nesting site offering optimal conditions. Fifty-four nests were studied during the nesting period (2021). The results show that the nests are built on two plant supports (Sparganium erectum and Nymphaea alba). Nymphaea alba nests were deeper (106.360 cm) than Sparganium erectum nests (43.244 cm). The mean external nest diameter was greater for Nymphaea alba (37.364 cm) than for Sparganium erectum (32.186 cm). The laying period spanned five weeks, specifically from mid-June to the end of the second decade of July, totaling thirty-three days. Conversely, the laying period for Nymphaea alba was restricted to two weeks, starting from the commencement of July and concluding at the termination of the first decade of July, totaling nine days. The mean dates for laying were June 28 and July 04 for Sparganium erectum and Nymphaea alba, respectively. The mean clutch size was 1.93 eggs per brood and 1.82 eggs per brood for Sparganium erectum and Nymphaea alba, respectively. The mean width of the eggs was greater for Sparganium erectum (27.645 mm) than for Nymphaea alba (27.075 mm). These findings are consistent with those of the European population. [ABSTRACT FROM AUTHOR]

In the field of animal husbandry, the process of animal identification and recognition is challenging, timeconsuming, and costly. In Türkiye, the ear tagging method is widely used for animal identification. However, this traditional method has many significant disadvantages such as lost tags, the ability to copy and replicate tags, and negative impacts on animal welfare. Therefore, in some countries, biometric identification methods are being developed and used as alternatives to overcome the disadvantages of traditional methods. Retina vessel patterns are a biometric identifier with potential in biometric identification studies. Preprocessing steps and vessel segmentation emerge as crucial steps in image processing-based identification and recognition systems. In this study, conducted in the Kars region of Türkiye, a series of preprocessing steps were applied to retinal images collected from cattle. Fuzzy c-means, k-means, and level-set methods were utilized for vessel segmentation. The segmented vascular structures obtained with these methods were comparatively analyzed. As a result of the comparison, it was observed that all models successfully performed retinal main vessel structure segmentation, fine vessels were successfully identified with fuzzy c-means, and spots in retinal images were detected only by the level-set method. Evaluating the success of these methods in identification, recognition, or disease detection will facilitate the development of successful systems. [ABSTRACT FROM AUTHOR]

Artificial intelligence technologies are improving the marketing toolkit, making it possible to process large amounts of data faster and more efficiently than ever before. Machine learning, a subset of AI, uses algorithms that can predict which ads will be most effective in specific situations, allowing for optimized ad targeting. This research explores the issues of coevolution and distribution of machine and human intelligence in various social practices, including marketing and advertising. The authors describe the key approaches to studying the visual component of advertising and suggest revising traditional methods of analyzing advertising messages. The tracking of biometric data combined with AI-based methods that capture human emotions while viewing video content is proposed as a promising direction for such analysis. This paper presents the results of a pilot study based on analytical face-tracking technology using AI, where the subject of the experiment was the analysis of video fragments that may have an impact on the emotional state of the viewer. The AI software platform used was Amazon Rekognition, and the results show that AI analytics provide the ability to track the level of audience engagement in perceiving video content, which helps to improve communication effectiveness. This allows the use of AI to make recommendations for the development of more directed and engaging advertising messages. [ABSTRACT FROM AUTHOR]