Authentication Model Research Articles

Cognitive tuning in the STEM classroom: communication processes supporting children’s changing conceptions about data

The teaching and learning of statistical thinking begins at a young age in Australia, with a focus on data representation and interpretation from Foundation Year (age 5), and the collection, sorting and categorising of items from the natural environment starting even earlier. The intangible concept of data, as part of statistical literacy, can be complex for children to grasp, especially when applying the notion of data to the everyday world or when data are explored in isolation to an investigation process. Authentic data modelling experiences present meaningful opportunities to apply statistical thinking although expert STEM knowledge is not always accessible to primary classroom teachers, nor is it always obvious how to implement such authentic problems within a classroom context. In this exploratory case study, we present data from a Year 4 classroom (age 9) statistical investigation addressing, ‘How big is a leaf?’ linking data to the real-life STEM context they represented. The authors were interested in how the teacher’s communication processes supported her students’ emerging understandings about data. Wit’s (2018) cognitive tuning framework offered a way to capture how the communication processes in a group build to a commonly shared frame of reference. Findings revealed a pattern of communication between the teacher and students, supporting students’ changing conceptions of data and related statistical thinking processes, throughout the investigation.

PIRAP: AASM: An Advanced Attribute-Based Security Model for Enhancing Trustworthy Data Sharing in Cloud Computing

In today’s world, cloud computing is widely used in various applications and services, which refers to the utilization of computational resources as per user requirements through the Internet. However, despite the numerous advancements in cloud services and applications, there are various security threats associated with it, mainly due to data outsourcing to third-party controlled data centers. In this context, this paper introduces a new model called Attribute-based Advanced Security Model (AASM) for Reliable Data Sharing in the Cloud. This model combines the Advanced Encryption Technique (AET) with Attribute-Based Signature (ABS) to ensure secure data sharing in the Cloud while efficiently controlling data access. The model enables encrypted access control from the data owner’s side with advanced access privileges, ensuring user privacy with an anonymous authentication model using ABS. By implementing these measures, the model provides security for cloud providers and users while safeguarding against malicious attacks. The effectiveness of the proposed model is evaluated based on factors such as time complexities, security and accountability.

Volatile profile characterization during the drying process of black tea by integrated volatolomics analysis

Drying plays an important role in the development of high-quality aroma black tea. In this study, changes in the volatile profiles of Congou black tea samples subjected to different drying times were comprehensively characterized via gas chromatography-electronic nose, gas chromatography-mass spectroscopy, and gas chromatography-ion mobility spectroscopy (GC-IMS) analyses. Different drying times generated different aroma types (e.g., floral and sweet aromas, etc.). Forty-five minutes of drying treatment was identified as a critical time point for the formation of tea aroma quality, since at this point additional drying caused a decline in aroma quality. Here we identified a total of 90 volatile organic compounds, mainly alcohols and aldehydes, using GC-IMS. Based on the volatile fingerprints generated by GC-IMS, an authentic orthogonal partial least squares discriminant analysis model with credible parameters (R2Y = 0.921, Q2 = 0.817) was established. Moreover, using variable importance in projection > 1.35 and p < 0.05 as filtering criteria, six variables including four identified key differential components were screened out. This study provides a new perspective for understanding the dynamic changes occurring during drying and lays a theoretical foundation for precise and quality-oriented processing of Congou black tea.

Efficient thermal face recognition method using optimized curvelet features for biometric authentication.

Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition. However, the literature did not thoroughly analyse the impact of feature selection on the accuracy and computational cost of face recognition which is an important aspect for limited resources applications like IoT ones. Also, the literature did not thoroughly evaluate the performance metrics of the proposed methods/solutions which are needed for the optimal configuration of the biometric authentication systems. This paper proposes a thermal face-based biometric authentication system. The proposed system comprises five phases: a) capturing the user's face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, c) feature extraction using wavelet and curvelet transform, d) feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB). Upon the public dataset, Terravic Facial IR, the proposed system was evaluated using the metrics: accuracy, precision, recall, F-measure, and receiver operating characteristic (ROC) area. The results showed that the curvelet features optimized using the GWO and classified with random forest could help in authenticating users through thermal images with performance up to 99.5% which is better than the results of wavelet features by 10% while the former used 5% fewer features. In addition, the statistical analysis showed the significance of our proposed model. Compared to the related works, our system showed to be a better thermal face authentication model with a minimum set of features, making it computational-friendly.

Методические подходы к определению географического происхождения вин. Обзор

Wines with a clear geographical origin are more attractive for customers. The geographical origin of wine is stated on its label, but no official standards guarantee its reliability. The present research objective was to analyze the existing methodological approaches to wine authentication.&#x0D; The study featured domestic and foreign publications indexed in Dimensions and Elibrary in 2017–2022 with such keywords as wine authentication, geographical origin of wine, and chemometrics.&#x0D; The research revealed no single methodological solution to wine authentication because food science knows a wide range of parameters, methods, analytical equipment, and data processing models. Chemometric methods are reliable because they are able to process large arrays of analytical research results structured in a data bank using the so-called fingerprint principle. They involve 2–65 markers that are individual for each geographical region, country, zone, or terroir. Another promising method is the quantitative and qualitative nuclear magnetic resonance spectroscopy (qNMR) of protons 1H and deuterium 2H(D) nuclei, as well as other elements (13C, 17O, 31P, 14N). The review resulted in an integrated approach based on a combination of isotopic testing with cation-anionic profiling. The analytical support involved the methods of atomic absorption and atomic emission spectroscopy, spectrometry with inductively coupled plasma, isotope ratio mass-spectrometry, and quantitative and qualitative nuclear magnetic resonance spectroscopy. This combined approach could provide background for an all-Russian state standard with a single algorithm for wine authentication tests. &#x0D; The new approach will be used to develop enochemical profiles of wines from a particular region, as well as to choose the most effective chemometric models for geographical authentication.

Genetic ablation of Saposin-D in Krabbe disease eliminates psychosine accumulation but does not significantly improve demyelination.

Krabbe disease is an inherited demyelinating disease caused by a genetic deficiency of the lysosomal enzyme galactosylceramide (GalCer) β-galactosidase (GALC). The Twitcher (Twi) mouse is a naturally occurring, genetically and enzymatically authentic mouse model that mimics infantile-onset Krabbe disease. The major substrate for GALC is the myelin lipid GalCer. However, the pathogenesis of Krabbe disease has long been explained by the accumulation of psychosine, a lyso-derivative of GalCer. Two metabolic pathways have been proposed for the accumulation of psychosine: a synthetic pathway in which galactose is transferred to sphingosine and a degradation pathway in which GalCer is deacylated by acid ceramidase (ACDase). Saposin-D (Sap-D) is essential for the degradation of ceramide by ACDase in lysosome. In this study, we generated Twi mice with a Sap-D deficiency (Twi/Sap-D KO), which are genetically deficient in both GALC and Sap-D and found that very little psychosine accumulated in the CNS or PNS of the mouse. As expected, demyelination with the infiltration of multinucleated macrophages (globoid cells) characteristic of Krabbe disease was milder in Twi/Sap-D KO mice than in Twi mice both in the CNS and PNS during the early disease stage. However, at the later disease stage, qualitatively and quantitatively comparable demyelination occurred in Twi/Sap-D KO mice, particularly in the PNS, and the lifespans of Twi/Sap-D KO mice were even shorter than that of Twi mice. Bone marrow-derived macrophages from both Twi and Twi/Sap-D KO mice produced significant amounts of TNF-α upon exposure to GalCer and were transformed into globoid cells. These results indicate that psychosine in Krabbe disease is mainly produced via the deacylation of GalCer by ACDase. The demyelination observed in Twi/Sap-D KO mice may be mediated by a psychosine-independent, Sap-D-dependent mechanism. GalCer-induced activation of Sap-D-deficient macrophages/microglia may play an important role in the neuroinflammation and demyelination in Twi/Sap-D KO mice.

Alternative Philosophical Models of Experience and Authenticity and their Relevance to Marketing Practices

Alternative Philosophical Models of Experience and Authenticity and their Relevance to Marketing Practices

Development of a variety and quality evaluation method for Amomi fructus using GC, electronic tongue, and electronic nose.

Amomi fructus is rich in volatile components and valuable as a medicine and edible spice. However, the quality of commercially available A. fructus varies, and issues with mixed sources and adulteration by similar products are common. In addition, due to incomplete identification methods, rapid detection of the purchased A. fructus quality is still an issue. In this study, we developed qualitative and quantitative evaluation models to assess the variety and quality of A. fructus using GC, electronic tongue, and electronic nose to provide a rapid and accurate variety and quality evaluation method of A. fructus. The models performed well; the qualitative authenticity model had an accuracy of 1.00 (n = 64), the accuracy of the qualitative origin model was 0.86 (n = 44), and the quantitative model was optimal on the sensory fusion data from the electronic tongue and electronic nose combined with borneol acetate content, with R 2 = 0.7944, RMSEF = 0.1050, and RMSEP = 0.1349. The electronic tongue and electronic nose combined with GC quickly and accurately evaluated the variety and quality of A. fructus, and the introduction of multi-source information fusion technology improved the model prediction accuracy. This study provides a useful tool for quality evaluation of medicine and food.

Deep learning based graphical password authentication approach against shoulder-surfing attacks

The password used to authenticate users is vulnerable to shoulder-surfing assaults, in which attackers directly observe users and steal their passwords without using any other technical upkeep. The graphical password system is regarded as a likely backup plan to the alphanumeric password system. Additionally, for system privacy and security, a number of programs make considerable use of the graphical password-based authentication method. The user chooses the image for the authentication procedure when using a graphical password. Furthermore, graphical password approaches are more secure than text-based password methods. In this paper, the effective graphical password authentication model, named as Deep Residual Network based Graphical Password is introduced. Generally, the graphical password authentication process includes three phases, namely registration, login, and authentication. The secret pass image selection and challenge set generation process is employed in the two-step registration process. The challenge set generation is mainly carried out based on the generation of decoy and pass images by performing an edge detection process. In addition, edge detection is performed using the Deep Residual Network classifier. The developed Deep Residual Network based Graphical Password algorithm outperformance than other existing graphical password authentication methods in terms of Information Retention Rate and Password Diversity Score of 0.1716 and 0.1643, respectively.

Эффективные стратегии обучения в XXI веке: концепция А.А. Оздогру

The article analyzes the section "Revising Effective Learning Strategies for 21st Century Students" of the collective monograph "Theory of Education in the 21st Century" (Springer, 2022), author A.A. Ozdogru. This section presents the author's model of the most effective educational strategies, as a result of which the skills necessary for people living in the 21st century are acquired. Strategies such as digital learning, inquiry-based learning, cooperative and collaborative learning, systems and support for learners, social and emotional learning are consistently considered. A.A. Ozdogru believes that the time has come to integrate these strategies on a global scale, since the tasks of forming new skills have been set in international documents at the global level. The value of the author's model A.A. Ozdogru lies in the fact that this model relies only on those results that have undergone serious empirical testing. The concept of Ozdogru is of particular importance due to the fact that the most effective educational strategies are considered here not separately from each other, but in integration. Thus, the scientific model of Ozdogru is also a research program of pedagogical sciences.

PROJECT TECHNOLOGIES IN THE CONDITIONS OF THE DISTANCE FORMAT OF THE PROFESSIONAL TRAINING OF THE TEACHER

The article deals with the issues of the relevance of distance education and analyzes the problems of ensuring its effectiveness in the context of the transition to competency-oriented/personality-oriented technologies. The essence of distance education and the problems that reduce its effectiveness in the conditions of modern realities and challenges of the time are analyzed. In particular, attention is focused on the practice of formal application of innovative technologies, which include project-based technologies characterized by a discrepancy in form and content (innovative form and traditionally informative and reproductive method of organizing educational activities in its context). The authors argue that educational activity systems of schools and professional training of teachers in higher education institutions should be studied in the context of common approaches to the functioning of interdependent systems. It is proved that an adequate personality-oriented system of organizing project activities of schoolchildren/students will comply with the basic principles of competency-based education and provide the basis for its implementation in the system of competency-based education. The article offers the author's model of organizing project-based learning in the distance education system and describes algorithms for its implementation with a focus on the criteria of competency-based education and distance learning conditions. The model is equally relevant for the system of organizing educational activities of schoolchildren and professional training, as well as post-graduate education of teachers. Therefore, the problem is to systematically and gradually move towards innovative education, updating all the links and dependencies that ensure the systematic approach.

Bridge of Trust: Cross Domain Authentication for Industrial Internet of Things (IIoT) Blockchain over Transport Layer Security (TLS)

The Industrial Internet of Things (IIoT) holds significant potential for improving efficiency, quality, and flexibility. In decentralized systems, there are no trust-based centralized authentication techniques, which are unsuitable for distributed networks or subnets, as they have a single point of failure. However, in a decentralized system, more emphasis is needed on trust management, which presents significant challenges in ensuring security and trust in industrial devices and applications. To address these issues, industrial blockchain has the potential to make use of trustless and transparent technologies for devices, applications, and systems. By using a distributed ledger, blockchains can track devices and their data exchanges, improving relationships between trading partners, and proving the supply chain. In this paper, we propose a model for cross-domain authentication between the blockchain-based infrastructure and industrial centralized networks outside the blockchain to ensure secure communication in industrial environments. Our model enables cross authentication for different sub-networks with different protocols or authentication methods while maintaining the transparency provided by the blockchain. The core concept is to build a bridge of trust that enables secure communication between different domains in the IIoT ecosystem. Our proposed model enables devices and applications in different domains to establish secure and trusted communication channels through the use of blockchain technology, providing an efficient and secure way to exchange data within the IIoT ecosystem. Our study presents a decentralized cross-domain authentication mechanism for field devices, which includes enhancements to the standard authentication system. To validate the feasibility of our approach, we developed a prototype and assessed its performance in a real-world industrial scenario. By improving the security and efficiency in industrial settings, this mechanism has the potential to inspire this important area.

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System.

Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.

A Rubik's Cube Cryptosystem-based Authentication and Session Key Generation Model Driven in Blockchain Environment for IoT Security

Over the past decade, IoT has gained huge momentum in terms of technological exploration, integration, and its various applications even after having a resource-bound architecture. It is challenging to run any high-end security protocol(s) on Edge devices. These devices are highly vulnerable toward numerous cyber-attacks. IoT network nodes need peer-to-peer security, which is possible if there exists proper mutual authentication among network devices. A secure session key needs to be established among source and destination nodes before sending the sensitive data. To generate these session keys, a strong cryptosystem is required to share parameters securely over a wireless network. In this article, we utilize a Rubik's cube puzzle-based cryptosystem to exchange parameters among peers and generate session key(s). Blockchain technology is incorporated in the proposed model to provide anonymity of token transactions, on the basis of which the network devices exchange services. A session key pool randomizer is used to avoid network probabilistic attacks. Our hybrid model is capable of generating secure session keys that can be used for mutual authentication and reliable data transferring tasks. Cyber-attacks resistance and performance results were verified using standard tools, which gave industry level promising results in terms of efficiency, light weightiness, and practical applications.

ActiveGuard: An active intellectual property protection technique for deep neural networks by leveraging adversarial examples as users' fingerprints

AbstractThe intellectual properties (IP) protection of deep neural networks (DNN) models has raised many concerns in recent years. To date, most of the existing works use DNN watermarking to protect the IP of DNN models. However, the DNN watermarking methods can only passively verify the copyright of the model after the DNN model has been pirated, which cannot prevent piracy in the first place. In this paper, an active DNN IP protection technique against DNN piracy, called ActiveGuard, is proposed. ActiveGuard can provide active authorisation control, users' identities management, and ownership verification for DNN models. Specifically, for the first time, ActiveGuard exploits well‐crafted rare and specific adversarial examples with specific classes and confidences as users' fingerprints to distinguish authorised users from unauthorised ones. Authorised users can input their fingerprints to the DNN model for identity authentication and then obtain normal usage, while unauthorised users will obtain a very poor model performance. In addition, ActiveGuard enables the model owner to embed a watermark into the weights of the DNN model for ownership verification. Compared to the few existing active DNN IP protection works, ActiveGuard can support both users' identities identification and active authorisation control. Besides, ActiveGuard introduces lower overhead than these existing active protection works. Experimental results show that, for authorised users, the test accuracy of LeNet‐5 and Wide Residual Network (WRN) models are 99.15% and 91.46%, respectively, while for unauthorised users, the test accuracy of LeNet‐5 and WRN models are only 8.92% and 10%, respectively. Besides, each authorised user can pass the fingerprint authentication with a high success rate (up to 100%). For ownership verification, the embedded watermark can be successfully extracted, while the normal performance of DNN models will not be affected. Furthermore, it is demonstrated that ActiveGuard is robust against model fine‐tuning attack, pruning attack, and three types of fingerprint forgery attacks.

Horizontal vibration behavior of a rigid disk resting on a viscoelastic soil layer overlying rigid bedrock

The soil overlying bedrock is usually a finite thickness medium in engineering practice, but it is often considered a half-space medium in the analysis of the dynamic interaction between foundation structures and subsoil. To address this shortcoming, an authentic dynamic model for a rigid disk subjected to time-harmonic horizontal-only loads resting on a viscoelastic soil layer of finite thickness is proposed in this paper. Based on the Hankel transform method and dual integral equations, the mixed boundary value problem is transformed into Fredholm integral equations of the second kind, and thus the displacement and stress field of the disk–soil system is obtained. Then, the rationality and accuracy of the proposed solution are verified in comparison with the available results. Finally, a parameter analysis of the thickness, Poisson’s ratio, damping ratio and shear modulus of the subsoil is carried out in detail to investigate the horizontal dynamic response of the rigid disk–soil system. The results showed that as the soil layer thickness increases, the horizontal dynamic compliance coefficient and soil displacements decrease. Specifically, when the normalized thickness of the soil layer is greater than 100, changing the thickness has a negligible effect on the computational results.

AUTHENTICITY AND THE POOR IMAGE IN THE AGE OF DEEP LEARNING

Deep learning techniques are increasingly used to automate categorization and identification tasks for large datasets of digital photographs. For rasterized images formats, such as JPEGs, GIFs, and PNGs, the analysis happens on the level of individual pixels. Given this, digital images used in deep learning applications are typically restricted to relatively low-resolution formats to conform to the standards of popular pre-trained neural networks. Using Hito Steyerl’s conception of the ‘poor image’ as a theoretical frame, this article investigates the use of these relatively low-resolution images in automated analysis, exploring the ways in which they may be deemed preferable to higher-resolution images for deep learning applications. The poor image is rich in value in this context, as it limits the undesirable ‘noise’ of too much detail. In considering the case of automated art authentication, this article argues that a notion of authenticity is beginning to emerge that raises questions around Walter Benjamin’s often-cited definition in relation to mass image culture. Copies or reproductions are now forming the basis for a new model of authenticity, which exists latently in the formal properties of a digital image.

A Cross-Domain Authentication and Authorization Model for New Power System Based on Trust Management

The access of new power service terminals brings about a new demand for cross-domain and cross-system security access. Therefore, this paper proposes a cross-domain authentication and authorization model for a new power system based on trust management, which is realized through the cooperation of certificate authority, authentication module, authorization module, and trust management module. Firstly, the model proposed in this paper includes a cross-domain authentication method, which realizes authentication between domains by issuing and verifying tokens for terminals with sufficient trust. Secondly, the model includes an authorization method based on attribute and trust value, which realizes the authorization of the terminal through attribute policy and trust evaluation. Finally, a trust value calculation method for terminal cross-domain access is included in the model. In this paper, the proposed model and method are simulated to demonstrate the correctness and effectiveness of the model. The experimental results show that the model has the advantages of universality, efficiency, and ease of management, and the terminal can access resources safely and efficiently across domains.

A novel honey authenticity detection system based on external cavity quantum cascade laser

Limited by the flexibility of the detection instrument, the current research reports on rapid detection of honey authenticity are mainly about algorithm research under the laboratory conditions. External cavity quantum cascade laser (EC-QCL) is a new sensor technology, has the advantages of high power, high flux and high flexibility, which is very suitable for the development of in-situ detection instrument. In this study, we developed a novel miniaturized detection system based on EC-QCL for detecting sweeteners in Vitex honey, and established the discrimination model of honey authenticity based on the EC-QCL detection system combined with partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM), respectively. The accuracy, sensitivity and specificity of the validation set of the best model were both 100%, and the area under the receiver operating characteristic curves (AUROC) is greater than 0.99. In addition, the performance of the discrimination model trained using data obtained by EC-QCL system are better than that established by traditional detection equipment under the same conditions. The results show that EC-QCL detection system provides a potential solution for the rapid in-situ detection of honey authenticity.

EyeDrive: A Deep Learning Model for Continuous Driver Authentication

Eye movement (EM) is considerably a new behavioral modality for biometric authentication. In this work, we use this modality in the context of continuous driver authentication. Existing models rely on different modalities that limit their usage or are inconvenient to drivers. We propose an end-to-end learning model that takes the remote eye movement profiles solely and produces embeddings for driver authentication scenarios. The model is based on Long short-term memory (LSTM) and dense networks to learn temporal characteristics from the EM profiles. We focus on low-rate devices because of their affordability. Yet, they present a challenge because of their limited ability to capture quality measurements. To evaluate our model, two low frame-rate devices are used to build our datasets, which are Autocruis and GazePoint. The authentication performance outperforms state-of-the-art with as low as 30 seconds frame length with both devices. The best authentication performances for the identification/verification modes are 92.38/0.76% and 91.05/0.11% for the first and the second datasets, respectively.