Authentication Performance Research Articles

Anne Bogart: Despre aici și acum

The book A Director Prepares by Anne Bogart offers an engaging look at the world of theater, combining valuable insights and practical ideas for directors. Structured into seven key chapters (Memory, Violence, Eroticism, Terror, Stereotype, Embarrassment, Resistance), the book explores fundamental aspects of the human experience and its links to the art of theater. The memory chapter discusses the impact of memories on the creative process, emphasizing the importance of using them for authentic performance. Violence is analyzed in terms of its necessity in art, with the role of evoking strong emotional responses. Eroticism is discussed as a driving force in the creative act, while terror is seen as a stimulus for creativity and intensity in the stage event. Stereotypes are reinterpreted as tools for exploring and challenging social perceptions, while embarrassment is presented as a source of artistic vulnerability and authenticity. In the end, resistance is described as a driving force that leads artists to overcome limitations and discover their voices. Anne Bogart's book is not only about directing but also about the psychology and philosophy of theater art, making it essential reading for aspiring and experienced directors alike.

Large-scale observational study of AI-based patient and surgical material verification system in ophthalmology: real-world evaluation in 37 529 cases

BackgroundSurgical errors in ophthalmology can have devastating consequences. We developed an artificial intelligence (AI)-based surgical safety system to prevent errors in patient identification, surgical laterality and intraocular lens (IOL) selection....

Cross-layer Authentication Mechanism Model Combined with 5G Converged Channel Fingerprint

In the actual communication environment, once the attacker successfully steals the legitimate channel information, the key information can be cracked according to the authentication response, so there is a security risk of key leakage. This paper combines the physical layer channel characteristics with the shared key, and combines it into a joint key to design a challenge-response physical layer authentication mechanism based on interpolation polynomial. Then, this method is applied to the EAP-AKA’ authentication protocol of 5G network, and a cross-layer authentication mechanism for 5G converged channel fingerprint is proposed. Finally, using the captured high-level authentication challenge response data, a simulation environment is built in MATLAB and the feasibility and security of the scheme are verified. The experimental results show that the authentication mechanism has better authentication performance and security performance. Compared with the traditional high-level authentication mechanism, it has certain advantages in computing overhead and can meet the 5G application scenarios of large-scale IoT terminals. Moreover, it combines physical layer authentication with high-level authentication, realizes mutual complementarity and mutual enhancement, and enhances the security performance of authentication.

Adaptive Probabilistic Neural Network based Edge Data Center Authentication for Secure Load Balancing in Fog Computing

The growth of current technological advances has promising of fog computing, that increases the processing capacity of devices and provides fresh approaches for conventional applications in industry. Fog computing has become increasingly vital given the huge and sophisticated system of sensors to manage the information stream. Node authentication and secure load balancing are important in fog computing. The rationale behind developing this model is that the existing deep learning models are affected by the node authentication performance and also generate issues like high latency developed by the cloud-based architecture while processing huge amount of data, and node employment. Thus, the objective of the developed model is to implement an advanced deep learning method for better node authentication and an appropriate enhanced optimization scheme for performing optimal resource allocation. Additionally, secure load balancing is done at edge node centers in fog computing to allocate the workload for each node securely. Initially, the network initialization attributes in fog computing are provided to the authentication process. Here, the deep learning-based Edge Data Centre (EDC) authentication takes place with the help of the Adaptive Probabilistic Neural Network (A-PNN) model. The major intention of introducing an A-PNN method is to check whether the node is authenticated or not. If the EDC node is authenticated, then the A-PNN model suggests the particular EDC node for the resource allocation process, or else the node will be ignored or removed from the process. Here, the new hybrid approaches as Hybrid Heap with African Buffalo Optimization Algorithm (HH-ABO) are suggested for execute the process of optimization. Here, the parameters of the A-PNN model are tuned for maximizing accuracy, precision, and NPV and also minimizing the False Positive Rate (FPR) rate. Then the authenticated node is subjected to the load-balancing scheme. The allocation of allocation is carried out in the load balancing scheme. Here, the diverse constraints like resource allocation, energy consumption, cost, the execution time of the task, security by authentication, latency, and task completion time are considered for formulating the objective function using the HH-ABO algorithm. At last, it is performed well than the traditional methods using diverse parameters. The outcome demonstrate that the developed model achieved better results on load balancing at edge nodes in fog computing. The proposed model effectively resolves the load balancing issues and also it distributes workload for each node accurately. It effectively resolves the latency and deployment issues. In addition, the modern applications based on the proposed scheme will allow million of workloads from one node to another in a fast and reliable manner.

Going against the grain? Appraising the aesthetic labour of male beauty influencers on Instagram

Despite the proliferation of scholarly studies on social media in recent years, not many have focused on male social media influencers in the beauty industry, which is often viewed as a feminine preserve with the vast majority of ‘beauty influencers’ being female. This study focuses on the discursive practices of two male beauty influencers to examine how their visual self-portrayal contributes to their popularity and influence. A total of 752 photographs collected from their Instagram accounts were examined using a visual appraisal framework adapted from Martin and White’s (2005) Appraisal Theory described in The Language of Evaluation, focusing on normality, capacity and authenticity. The authors’ findings show that both influencers, who are openly gay, portray themselves as gender norm transgressors whose performance of capacity and authenticity as social media celebrities is a highly contested and conflictual aspect of their identity construction as beauty influencers. These findings not only shed light on the visual discursive processes and practices through which beauty influencers perform their aesthetic labour to enhance their popularity and influence but they also contribute to our understanding of how they perpetuate mediatized and marketized stereotypes of beauty and being as part of the broader sociocultural phenomenon of the celebrification of social media influencers in contemporary society.

AffectiVR: A Database for Periocular Identification and Valence and Arousal Evaluation in Virtual Reality

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.

Performance Analysis of Authentication System: A Systematic Literature Review

Background: Data authentication is vital nowadays, as the development of the internet and its applications allow users to have all-time data availability, attracting attention towards security and privacy and leading to authenticating legitimate users. Methods: We have diversified means to gain access to our accounts, like passwords, biometrics, and smartcards, even by merging two or more techniques or various factors of authentication. This paper presents a systematic literature review of papers published from 2010 to 2022 and gives an overview of all authentication techniques available in the market. Results: Our study provides a comprehensive overview of all three authentication techniques with all performance metrics (Accuracy, Equal Error Rate (EER), False Acceptance Rate (FAR)), security, privacy, memory requirements, and usability (Acceptability by user)) that will help one choose a perfect authentication technique for an application. Conclusion: In addition, the study also explores the performance of multimodal and multifactor authentication and the application areas of authentication.

A Dynamic Selection Method for Touch-Based Continuous Authentication On Mobile Devices

Touch biometric is one of the promising modalities to realise continuous authentication (CA) on mobile devices by distinguishing between touch strokes performed by the legitimate and illegitimate users. While the benefit of the scheme is promising, the effectiveness of different classification methods is not thoroughly understood. Little consideration has been given to dynamic selection of classifiers. In this paper, we proposed a dynamic selection method to deal with the security and usability needs of touch-based CA. Instead of classifying all touch samples using the same classifier, the method classifies each touch sample using the most promising classifier(s) from a pool of classifiers based on a certain measure of competence. The classifiers that achieve the highest level of competence will be selected to perform the classification task. We compared the proposed method with other dynamic selection methods, well-known single classifiers, as well as static ensemble methods. The experimental results show the potential and feasibility of the proposed method to improve the authentication performance of touch-based CA against the benchmark methods. We found that the proposed method can produce promising results with relatively low equal error rate in many scenarios of the datasets, with relatively high consistency.

A New Subject-Sensitive Hashing Algorithm Based on Multi-PatchDrop and Swin-Unet for the Integrity Authentication of HRRS Image

Transformer-based subject-sensitive hashing algorithms exhibit good integrity authentication performance and have the potential to ensure the authenticity and convenience of high-resolution remote sensing (HRRS) images. However, the robustness of Transformer-based subject-sensitive hashing is still not ideal. In this paper, we propose a Multi-PatchDrop mechanism to improve the performance of Transformer-based subject-sensitive hashing. The Multi-PatchDrop mechanism determines different patch dropout values for different Transformer blocks in ViT models. On the basis of a Multi-PatchDrop, we propose an improved Swin-Unet for implementing subject-sensitive hashing. In this improved Swin-Unet, Multi-PatchDrop has been integrated, and each Swin Transformer block (except the first one) is preceded by a patch dropout layer. Experimental results demonstrate that the robustness of our proposed subject-sensitive hashing algorithm is not only stronger than that of the CNN-based algorithms but also stronger than that of Transformer-based algorithms. The tampering sensitivity is of the same intensity as the AGIM-net and M-net-based algorithms, stronger than other Transformer-based algorithms.

Mathematical Modelling for Neural Network-Based Textural Feature Extraction from Normalized Ocular Images for Improved Biometric Authentication

Biometric authentication systems have become increasingly prevalent in security and identity verification applications. Among various biometric traits, the ocular region, specifically the iris, offers a unique and stable source for individual identification. The intricate patterns within the iris are highly distinctive and remain consistent over a person's lifetime, making them ideal for biometric applications. In the field of biometric authentication, the ocular region has emerged as a robust and reliable source for identity verification due to its unique and stable features. This study focuses on enhancing the accuracy of biometric systems through neural network-based textural feature extraction from normalized ocular images. The proposed approach utilizes Mask Image Extraction (Hough circle) and an efficient U-Net model to achieve superior performance in biometric authentication. Initially, the ocular images are pre-processed to normalize lighting and orientation, ensuring consistency across the dataset. Mask Image Extraction, particularly using the Hough circle transform, is employed to isolate the region of interest (ROI) within the ocular images. This method accurately detects and segments the circular iris region, which is crucial for subsequent feature extraction. The core of the proposed system is the Efficient U-Net model, a variant of the standard U-Net architecture designed for enhanced efficiency and accuracy. The U-Net model is trained to extract intricate textural features from the segmented ocular regions. By leveraging its encoder-decoder structure, the U-Net captures both local and global features, resulting in a detailed and discriminative representation of the ocular texture. Experimental results demonstrate that the integration of Mask Image Extraction and the Efficient U-Net model significantly improves the accuracy of biometric authentication systems. The proposed method outperforms traditional approaches by providing more precise and reliable feature extraction, thereby enhancing the overall robustness and reliability of ocular-based biometric systems.

Backward design and authentic performance tasks to foster English skills: Perspectives of Hungarian teacher candidates

Backward design and authentic performance tasks to foster English skills: Perspectives of Hungarian teacher candidates

Analyzing the Artistic Style and Performance Technique of Chopin’s Narrative in G Minor

This paper analyzes the artistic style and performance techniques in Chopin's "Narrative No. 1 in G Minor." It explores how Chopin's compositional methods reflect his unique stylistic elements and the influence of his cultural and historical context. By examining the structural and thematic development of the piece, the paper highlights the intricate balance between technical skill and emotional expression necessary for an authentic performance. This analysis also delves into Chopin's use of melodic lines, chordal extensions, rhythmic patterns, and dynamic variations, providing insights into the complexities of performing his work. Through a combination of theoretical research and practical performance perspectives, this study aims to enhance the understanding and interpretation of Chopin's music, offering valuable guidance for musicians and scholars alike.

Testing Understanding by Design

Understanding by Design (UbD) is a well-known curricular methodology aiming at leading students to develop deep understanding of the arguments proposed by teachers. Through a path including stimulating questions, student motivation, deepening of the argument, reflection, design and assessment of authentic performances, the students develop a broad understanding of the learned argument. While there is plenty of articles describing various experiences in classrooms and schools showing the benefits of UbD, rarely this methodology has been the object of experimental or quasi-experimental studies. A research conforming to quasi-experimental criteria has been performed in a Swiss vocational school, where three groups of students have been submitted to teaching based on UbD principles (the first two) and to a more traditional teaching approach (the remaining one). The argument was the study of mathematical Relations and Functions. The results, collected during one school year, showed that the first two groups outperformed the third one and that the effect lasted at least for one school year. Since the teacher remained the same, it is likely that the outcome can be attributed to the use of the methodology.

Scaffolding Critical Thinking Among Interns Through The Use Of Prompts For Reflective Writing

With the arrival of the Industrial Revolution 4.0 (IR 4.0), the elimination of some of the existing ones, specifically those that involve repetitive and predictable manual tasks, has become a threat to job seekers and those already employed. With the advancement of technology, employers are currently seeking individuals with essential skills not replaceable by machines. Among the most in-demand skills are critical thinking skills, which are also transferrable skills across different industries and roles. Higher education institutions play a crucial role in moulding the potential of undergraduates to meet the current needs. The tertiary curricula should be tailored to cultivate strong critical thinking abilities. Among the teaching approaches that have been explored and found helpful for the development of critical thinking is through writing reflections. Instead of writing descriptively, writing reflections requires that writers be critical and analytical in their writing. However, writing reflections is very challenging to many student writers, thus, there is a need to scaffold their reflections to help elevate their critical thinking skills. This conceptual paper presents the arguments for scaffolding critical thinking prompts for reflective writing. As assessing higher-order thinking skills involves measuring reasoning over recall and necessitates assessing authentic performances, the scaffold is situated within the internship context. This experience closely mirrors their impending entry into the working world.

Secure authentication in MIMO systems: exploring physical limits

Multiple-input multiple-output (MIMO) technology is employed to improve the reliability and capacity of wireless communication systems. However, the wireless communication environment creates vulnerabilities to spoofing attacks. Furthermore, the authentication challenges posed by the heterogeneous characteristics of wireless applications increase as diverse technologies facilitate the growing number of Internet of Things (IoT) devices. To address these challenges, adaptive physical-layer authentication (PLA) leveraging the inherent antenna diversity in MIMO systems is examined, and an information-theoretic perspective on PLA in MIMO systems is given. The real and imaginary components of the received reference signals are used as attributes with a single-class classification support vector machine (SCC-SVM). It is shown that the authentication performance improves with the number of antennas, and the proposed scheme provides robust authentication.

Enhancing Resilience in Biometric Research: Generation of 3D Synthetic Face Data Using Advanced 3D Character Creation Techniques from High-Fidelity Video Games and Animation.

Biometric authentication plays a vital role in various everyday applications with increasing demands for reliability and security. However, the use of real biometric data for research raises privacy concerns and data scarcity issues. A promising approach using synthetic biometric data to address the resulting unbalanced representation and bias, as well as the limited availability of diverse datasets for the development and evaluation of biometric systems, has emerged. Methods for a parameterized generation of highly realistic synthetic data are emerging and the necessary quality metrics to prove that synthetic data can compare to real data are open research tasks. The generation of 3D synthetic face data using game engines' capabilities of generating varied realistic virtual characters is explored as a possible alternative for generating synthetic face data while maintaining reproducibility and ground truth, as opposed to other creation methods. While synthetic data offer several benefits, including improved resilience against data privacy concerns, the limitations and challenges associated with their usage are addressed. Our work shows concurrent behavior in comparing semi-synthetic data as a digital representation of a real identity with their real datasets. Despite slight asymmetrical performance in comparison with a larger database of real samples, a promising performance in face data authentication is shown, which lays the foundation for further investigations with digital avatars and the creation and analysis of fully synthetic data. Future directions for improving synthetic biometric data generation and their impact on advancing biometrics research are discussed.

Using Scenario-Based Assessment in the Development of Students’ Digital Communication Skills and Professional Competence

In this discussion, we consider how the use of scenario-based assessment (SBA) can provide students with a way of developing the digital communication skills that business communication research has found they will need for the workplace, alongside other aspects of professional competence. This is because SBA can be employed to engage learners in the same types of authentic performance tasks in a situated context that they will likely encounter in their professional lives. In addition, SBA can also be used to maximize the integrity of an assignment by harnessing the positive effects of using generative Artificial Intelligence (AI) tools, while simultaneously mitigating against the misappropriation of AI by students. SBA allows learners to practice both their digital, and other, communication skills as well as contributing to their understanding of professional practice, and it also provides instructors with a powerful form of formative assessment. Our aim is to put forward a motivating and effective way of helping our students to develop the skills that they will need to become successful communicators in a postpandemic professional world.

Odevzdání bez přijetí? K motivu existence mezi Jaspersem a Patočkou

The essay presents Patočka’s intellectual relationship to Karl Jaspers on the basis of his texts from the 1960s and 1970s, with a starting point in the 1969 study What is Existence?. After introducing some general connections and differences, based in particular on the forms of heresy in both philosophers, the basic contours of the notion of existence are examined more closely. Patočka understands existence as the performance of a threefold movement that is in many respects problematic. The virtue of Jaspers’ position, on the other hand, lies in its focused clarity. While both thinkers emphasize the concept of authentic performance, the key issue becomes its one-sidedness, which can be described as surrender without acceptance.

Аксиологические основы современного этновокального образования

The problems of modern ethno-vocal education considered in the article are related to its axiological founda-tions, as well as to the value potential of folk vocal pedagogy and the use of its principles and methods in mod-ern pedagogical practice. Based on the author’s own scientific research of modern authentic performance, the developed innovative method, techniques of formation of a modern carrier of ethno-vocal culture, capable of transmitting culture through singing activity in the cultural, pedagogical and living environment is revealed. The aim of this work is to substantiate the author’s proposed axiological model of modern ethno-vocal education, which contributes both to the formation and development of the ethno-vocal culture of the individual and to the preservation and development of society’s ethno-vocal culture. The article outlines the ways of activating im-plicit knowledge for the accumulation of singing experience through a specially developed by the author set of exercises and vocal work on mastering specific folk-singing techniques in practical educational disciplines: Choral Class, Ensemble and Solo Singing, Folklore Ensemble. The author concludes that currently the pro-spects of ethnocultural education are associated with the need for its axiologization and the formation of a modern teacher-performer of folk songs.

Multiobservation-Multichannel-Attribute-Based Multiuser Authentication for Industrial Wireless Edge Networks

In order to truly promote the further development of the Industrial Internet of Things (IIoT), terminal authentication of the IIoT is essential. Physical-Layer Authentication (PLA) has recently attracted much attention for its high security and lightweight. Nevertheless, most existing PLA schemes in conjunction only the observation of a single receiver will lead to low-reliability and low-robustness of authentication, especially in hostile time-varying wireless channels. To tackle this issue, we developed a multi-observation-multi-channel-attribute (MOMCA)-based multiuser authentication architecture, which considers both the observations of multi-receivers and multiple channel attributes of each observation to enhance wireless security. Specifically, the proposed architecture can provide additional spatial recognition characteristics for multi-users. To better fit the channel features of multi-observations, we proposed two Gradient Boosting Optimization (GBO)-based schemes. One uses Taylor expansion to approximate objective functions and adds the regularization term to avoid over-fitting issues. The other can obtain higher authentication performance by sampling the signal data with small gradient characteristics. The simulations on real industrial indoor and outdoor datasets verify the superiority of the proposed schemes in authentication accuracy over six baseline authentication schemes