Verification Of Solutions Research Articles

Proof of retrievability with flexible designated verification for cloud storage

With numerous applications in cloud storage, Proof of Retrievability (PoR) is an efficient solution for verification and retrieval of big data on a remote server. Existing PoR schemes can support two mutually exclusive verification modes, namely either private verifiability or public verifiability, depending on whether the outsourced data is verifiable by the data owner (user) only or by everyone. In order to allow the user to flexibly designate the capability of verification, we propose a novel framework for PoR schemes that enables fine-grained control of remote data verification for cloud storage. Our idea is enabling the user to dynamically release some tokens (verification keys) to one or more third parties (designated verifiers), such that only the user and the designated verifiers can verify and retrieve the outsourced data from the server. We formalize this notion as PoR with Flexible Designated Verification (PoR-FDV), and define an extended adversarial model for PoR-FDV based on Juel et al.'s PoR model. Under the new framework, we propose a generic, simple and elegant construction of PoR-FDV from public verifiable PoR and identity-based KEM/DEM, which then gives birth to a concrete and efficient PoR-FDV scheme. Compared to existing PoR schemes, PoR-FDV provides higher flexibility and scalability, as well as many useful features.

Solution verification of WECs: comparison of methods to estimate numerical uncertainties in the OES wave energy modelling task

High-fidelity models become more and more used in the wave energy sector. They offer a fully nonlinear simulation tool that in theory should encompass all linear and nonlinear forces acting on a wave energy converter (WEC). Studies using high-fidelity models are usually focusing on validation of the model. However, a validated model does not necessarily give reliable solutions. Solution verification is the methodology to estimate the numerical uncertainties related to a simulation. In this work we test four different approaches: the classical grid convergence index (GCI); a least-square version (LS-GCI); a simplified version of the least-square method (SLS-GCI); and the ITTC recommended practice. The LS-GCI requires four or more solutions whereas the other three methods only need three solutions. We apply these methods to four different high-fidelity models for the case of a heaving sphere. We evaluate the numerical uncertainties for two parameters in the time-domain and two parameters in the frequency domain. It was found that the GCI and ITTC were hard to use on the frequency domain parameters as they require monotonic convergence which sometimes does not happen due to the differences in the solutions being very small. The SLS-GCI performed almost as well as the SL-GCI method and will be further investigated.

Hardware/Software Co-Design of Cryptographic SoC Based on RISC-V Virtual Prototype

Embedded hardware accelerator with limited resources is increasingly employed in security areas. To accelerate system-on-chip (SoC) design, an efficient HW/SW co-design approach and validation platform become extremely important. The Electronic System Level Simulator (ESL) based on SystemC is the primary solution for fast hardware modeling and verification. However, most existing simulators cannot achieve a better trade-off between accuracy and performance, and none of the specific ESL simulators are proposed for cryptographic SoCs. To this end, this brief proposes a virtual prototype (VP) with integrated cryptographic accelerators for a cryptographic SoC based on RISC-V to accelerate the functional and performance simulation of the SoC. The VP is designed as an extensible and configurable platform dedicated to cryptographic SoC using an efficient HW/SW co-design approach. To accurately emulate real hardware, the flexible AHB-TLM interface and core timing model are presented. Compared to the RTL simulation, our custom VP performs about 10-450 times faster than the RTL simulation, and the simulation error is only about 4%. Our code is available at https://github.com/LX-IC/VP.

Verification of classification modernization solutions (the case study of Library Bibliographic Classification)

Efficiency of library classifications depends on their capacity to embrace current knowledge and social practices. Therefore, the verification (late Latin verificatio – evidence, confirmation, from the Latin verus – true and facio – to make) of upgrade solutions is an essential part of classification system operation and development. The problem of library classification verification is often out of researchers view. The verification mechanism (tools) for upgrade solutions has not been described yet. Therefore, the reliability and relevance of classification upgrade solutions is of scientific and practical value.The purpose of the paper is to summarize the related experience of Research LBC Center of the Russian State Library. Based on s tructured system analysis and the long practice of LBC development and application, the authors examine the mechanism for monitoring publications and identifying innovations, including new terms, concepts, social phenomena, etc., and for assessing and processing data for continuing update of the classification system. Using the example of LBC modernization solutions, the authors discuss advantages and disadvantages of individual methods. They conclude that the classification upgrade verification – essential to the nature of the library classification systems – has to become the key element of the classifying culture. The researchers review and suggest the ways to improve the verification mechanism adopted at the RSL LBC Research Center. The integrated approach to LBC verification is of applied relevance both for the theory of classifications and librarianship.

A Survey : Deep Learning Approaches for Signature Verification

Since a person's signature serves as the primary authentication and authorization method in legal transactions, there is a greater need than ever for effective auto-mated signature verification solutions. The fact that signatures are already recognized as a popular way of identity verification gives signature verification systems a significant edge over other types of technologies. The methods used to solve this problem and a signature verification system can be categorized into two categories: online and offline. An electronic tablet and pen that are connected to a computer are used in the online technique to extract information about a signature and collect dynamic data for verification purposes, such as pressure, velocity, and writing speed. Offline signature verification, on the other hand, employs signature images that have been recorded by a scanner or camera and involve less electronic management. Extracted features taken from the scanned signature image are used in an offline signature verification system. The main contribution of this study is how we can use deep learning approaches/networks for the task of offline signature verification systems.

Cerberus: A Blockchain-Based Accreditation and Degree Verification System

Credential fraud is a widespread practice that undermines investment and confidence in higher education systems and bears significant economic and social costs. Legacy credential verification systems are typically time-consuming, costly, and bureaucratic, and struggle against certain classes of credential fraud. In this article, we propose a comprehensive blockchain-based credential verification solution, Cerberus, which is considerably more efficient, easy, and intuitive to use, and effectively mitigates widespread manifestations of credential fraud. Cerberus also improves significantly upon other blockchain-based solutions in the research literature: it adheres closely to the existing credential verification ecosystem and addresses real-world fraud scenarios. Moreover, Cerberus uses on-chain smart contracts for credential revocation, and it does not entail students or employers to manage digital identities or cryptographic credentials to use the system. We prototype our solution and describe our attempt to design an online verification service with a rich feature set, including data privacy, transcript verification, and selective disclosure of data. We hope this effort contributes positively to alleviating the problem of fake credentials.

A volume-of-fluid vortex sheet method for multiphase flows

An Eulerian method to solve the vortex sheet dynamics of an interface captured by a Volume-of-Fluid (VoF) transport scheme is presented. The VoF scalar, vortex sheet strength, and interfacial area are numerically transported using an unsplit geometric scheme and source terms are evaluated with finite differences. Several methods are proposed to generate the self-induced vortex sheet velocities and the methods are assessed based on their computational feasibility and solution verification properties. A grid convergence study is conducted to evaluate the convergence properties of the VoF vortex sheet method and several classical hydrodynamic instabilities are presented to highlight the capabilities of the method in 2D and 3D. Results are compared against prior literature and Direct Numerical Simulations.

ResNet and Yolov5-enabled non-invasive meat identification for high-accuracy box label verification

Compliance issues riddle the agricultural sector despite being an essential industry for the human race. Many factors contribute to compliance issues; however, meat cut label verification is one of the most critical concerns due to its erroneous nature. In addition, meat cut identification is complex for the human eye. Thus, access to a skilled labor force is challenging. Nevertheless, meat compliance is essential since it has export compliance ramifications. These factors, along with others, are pushing for a digital alternative. An alternative that can augment human decision-making in verifying meat cut box labels. Artificial Intelligence (AI) is a digital alternative that can boost quality assurance tasks. One of AI’s potential quality assurance solutions is a Meat Box Labeling Verification (BLV) solution that can verify the boxed meat type against the label to ensure no mismatch. Two major components make up the BLV solution: Meat Identification and Label Analysis. This work aims to solve the former component by exploring different meat-type identification techniques. It explores them by building, evaluating, and testing different computer vision solutions. Hence, the novel contribution of this work is three folds. We first build, test, and design computer vision solutions that detect meat boxes and pieces accurately. These solutions include deep learning methods in classification and object detection techniques. Following that, we evaluate these models and derive key insights. Such insights are valuable for prototyping such solutions in production environments. For example, classification models achieve a 99% testing accuracy in identifying box types. In contrast, object detection achieved 89% mAP@.5:.95 in identified individual meat cuts. Finally, we prototype an object detection model in a natural meat processing environment—the demonstration showed comparable object detection precision at 85% mAP@.5:.95.

FC-ResNet: A Multilingual Handwritten Signature Verification Model Using an Improved ResNet with CBAM

Offline signature verification is a widely used biometric method in finance, law, and administrative procedures. However, existing deep convolutional neural network models perform poorly on signature datasets that span different regions and ethnic people, while also suffering from problems such as large parameter counts and slow inference speeds. To address these issues, we propose an improved residual network model (FC-ResNet). This model introduces a convolutional block attention module into the classical residual network to adapt to the diversity and variability of signatures, while also compressing the model for lightweight deployment. Due to the lack of public, offline handwritten signature datasets for ethnic people, we collected a large-scale offline handwritten signature dataset, including genuine signatures and forged signatures in Chinese, Uyghur, Kazakh, and Kirgiz, totaling 38,400 images. Our FC-ResNet model achieved an accuracy of over 96% for each language in our self-built dataset, as well as accuracy rates of 96.21%, 98.42%, and 97.28% on the public datasets CEDAR, BHSig-B, and BHSig-H, respectively. Based on the above experimental results, our proposed model demonstrates great potential for both public and self-built signature datasets, while also exhibiting significant advantages in lightweight model deployment. We believe that this work can provide a feasible solution for ethnic people signature verification.

Computation of stagnation coating flow of electro-conductive ternary Williamson hybrid mathrm{GO}-mathrm{AU}-{mathrm{Co}}_{3}{mathrm{O}}_{4}/mathrm{EO} nanofluid with a Cattaneo–Christov heat flux model and magnetic induction

Modern smart coating systems are increasingly exploiting functional materials which combine multiple features including rheology, electromagnetic properties and nanotechnological capabilities and provide a range of advantages in diverse operations including medical, energy and transport designs (aerospace, marine, automotive). The simulation of the industrial synthesis of these multi-faceted coatings (including stagnation flow deposition processes) requires advanced mathematical models which can address multiple effects simultaneously. Inspired by these requests, this study investigates the interconnected magnetohydrodynamic non-Newtonian movement and thermal transfer in the Hiemenz plane's stagnation flow. Additionally, it explores the application of a transverse static magnetic field to a ternary hybrid nanofluid coating through theoretical and numerical analysis. The base fluid (polymeric) considered is engine-oil (EO) doped with graphene left(GOright), gold left(Auright) and Cobalt oxide left(C{o}_{3}{O}_{4}right) nanoparticles. The model includes the integration of non-linear radiation, heat source, convective wall heating, and magnetic induction effects. For non-Newtonian characteristics, the Williamson model is utilized, while the Rosseland diffusion flux model is used for radiative transfer. Additionally, a non-Fourier Cattaneo–Christov heat flux model is utilized to include thermal relaxation effects. The governing partial differential conservation equations for mass, momentum, energy and magnetic induction are rendered into a system of coupled self-similar and non-linear ordinary differential equations (ODEs) with boundary restrictions using appropriate scaling transformations. The dimensionless boundary value problem that arises is solved using the bvp4c built-in function in MATLAB software, which employs the fourth-order Runge–Kutta (RK-4) method. An extensive examination is conducted to evaluate the impact of essential control parameters on the velocity f{^{prime}}left(zeta right), induced magnetic field stream function gradient g{^{prime}}left(zeta right) and temperature theta left(zeta right) is conducted. The relative performance of ternary, hybrid binary and unitary nanofluids for all transport characteristics is evaluated. The inclusion of verification of the MATLAB solutions with prior studies is incorporated. Fluid velocity is observed to be minimized for the ternary mathrm{GO}–mathrm{Au}–{mathrm{Co}}_{3}{mathrm{O}}_{4} nanofluid whereas the velocity is maximized for the unitary cobalt oxide left({mathrm{Co}}_{3}{mathrm{O}}_{4}right) nanofluid with increasing magnetic parameter (beta ). Temperatures are elevated with increment in thermal radiation parameter (Rd). Streamlines are strongly modified in local regions with greater viscoelasticity i.e. higher Weissenberg number (We). Dimensionless skin friction is significantly greater for the ternary hybrid GO–Au–C{o}_{3}{O}_{4}/EO nanofluid compared with binary hybrid or unitary nanofluid cases.

A privacy threat model for identity verification based on facial recognition

The proliferation of different types of photographic and video cameras makes it relatively simple and non-intrusive to acquire facial fingerprints with sufficient quality to perform individuals’ identity verification. In most democratic societies, a debate has been occurring regarding using such techniques in different application domains. Discussions usually revolve around the tradeoffs between utility (security in access control, mobile phone unlocking, payment processing, etc.), usability or economic gain and risks to citizens’ rights and freedoms (privacy) or ethics. This paper identifies the common aspects of different solutions for identity verification based on facial recognition techniques within different application domains. It then performs a privacy threat modelling based on these common aspects to identify the most critical risk factors and a minimum set of safeguards to be considered for their management.

Advanced two-phase subchannel method via non-linear iteration

A fast-running, robust two-phase flow, sub-channel model is presented based on non-linear solution of the steady-state subchannel fluid flow equations. The drift-flux model solves for conservation of liquid and vapor mass, mixture energy, and axial and transverse mixture momentum as part of an efficient planar marching scheme and nonlinear, nested outer and inner iteration. Models based on mechanistic subcooled boiling, two-phase turbulent void mixing, and drift are included. Solution verification and mesh convergence studies were performed for modern GE 10 × 10 fuel geometry and are shown to have excellent convergence behavior. Run time performance for a 50 axial mesh model showed 2.2 seconds on a single CPU core to tightly converge all 3D distributions (flow, void, pressure) for the GE 10 × 10 fuel geometry, supporting its efficient use within the Virtual Environment for Reactor Applications boiling water reactor framework.

Fluid–structure interaction simulation of a flapping flag in a laminar jet

The flapping flag is a classical and important Fluid–Structure Interaction (FSI) problem with many applications in nature, industry, and biological systems. However, the interaction between the flow field and the flag response is not fully understood due to the complex and nonlinear nature of the problem. Although theoretical models qualitatively predict the flapping frequency and the onset velocity, there are still quantitative discrepancies between the theoretical predictions and the experimental data. In this study, numerical FSI simulations of a standard flag in a viscous uniform laminar flow are performed to characterize the flow field around the flapping flag and correlate the flow field with the flag response for six Reynolds numbers (Re= 4.5 ×104, Re= 5.3 ×104, Re= 5.7 ×104, Re= 5.9 ×104, Re= 6.6 ×104, and Re= 7.5 ×104). Two-way coupled FSI simulations are conducted using commercial software ANSYS®. Rigorous solution verification is conducted first on three Re to ensure that solutions are independent of mesh and time-step refinement. Then validation is achieved by comparing the simulation predictions with experimental data. Overall, results showed a good agreement with an error range between 0.7%–4.7% for oscillation amplitude (Am), 1.3%–6.41% for drag coefficient (Cd), and 1.3%–5.3% for frequency (f). Moreover, a comparison between inviscid and viscous models is performed to evaluate the accuracy of using inviscid theoretical models for the same flow. It is observed that the inviscid model accurately predicted f but underpredicted Am and Cd, and overpredicted lift coefficient Cl. In the case of the viscous model, it is observed that a flow separation bubble and a thin flow circulation region exist on the suction side of the flag during the limit cycle oscillation (LCO). Finally, an instantaneous negative drag is observed for a short time during the LCO, suggesting that flag reconfiguration and flow separation significantly affects the drag. These observations suggest that viscous models may be needed to improve the theoretical predictions.

Solution Set Augmentation for Knee Identification in Multiobjective Decision Analysis.

In multiobjective decision making, most knee identification algorithms implicitly assume that the given solutions are well distributed and can provide sufficient information for identifying knee solutions. However, this assumption may fail to hold when the number of objectives is large or when the shape of the Pareto front is complex. To address the above issues, we propose a knee-oriented solution augmentation (KSA) framework that converts the Pareto front into a multimodal auxiliary function whose basins correspond to the knee regions of the Pareto front. The auxiliary function is then approximated using a surrogate and its basins are identified by a peak detection method. Additional solutions are then generated in the detected basins in the objective space and mapped to the decision space with the help of an inverse model. These solutions are evaluated by the original objective functions and added to the given solution set. To assess the quality of the augmented solution set, a measurement is proposed for the verification of knee solutions when the true Pareto front is unknown. The effectiveness of KSA is verified on widely used benchmark problems and successfully applied to a hybrid electric vehicle controller design problem.

ПРОБЛЕМЫ ГЕОМЕТРО-ГРАФИЧЕСКОЙ ПОДГОТОВКИ СТУДЕНТОВ ВУЗОВ

It is noted that geometric and graphical disciplines traditionally cause difficulties in studying elementary students. It is pointed out that the requirements for the quality of education and ensuring academic performance are mutually inverse and are in conflict with the limited number of teaching hours, which has been steadily declining for many years. Data are given on the number of hours allocated for the study of geometric and graphic disciplines in Russian universities. The main reasons for the problems of geometric and graphic training of students are listed. The first reason is an attempt to give knowledge and skills from three different sections - mathematics (descriptive geometry), computer science (computer graphics) and engineering (engineering graphics) in conditions of shortage of classroom hours. Provides information about the content of classical textbooks of descriptive geometry, engineering graphics. The second reason is the complexity of the development of spatial thinking. It is noted that information technologies that facilitate the understanding of images do not solve the problem of the development of spatial thinking. Information is given on the heterogeneity of students in terms of the psychophysiological features of spatial thinking in general and in terms of various types of mathematical thinking. The third reason is the poor initial preparation of students. Features and reasons are indicated. The main ways of increasing the motivation of students to study geometric and graphic disciplines are given. The fourth reason is the unpreparedness of students for independent work. Reasons are given. The main ways of increasing the efficiency of students' independent work are given. The shortcomings of automatic control in the form of testing and automatic verification of solutions of graphic tasks are considered in detail. The fifth reason is the difference in the requirements for geometric and graphic training for different educational areas, specialties and profiles. The presence in the geometric-graphical cycle of disciplines of an invariant (common for different specialties and training profiles) core and subject settings is noted. The sixth reason is the high requirements for lecturers of geometric and graphic disciplines. The problem of training lecturing staff of higher education in the field of geometric and graphic education is noted. Conclusions are made about the need to create a methodological training system that takes into account and solves these problems.

Deep learning-based fast volumetric imaging using kV and MV projection images for lung cancer radiotherapy: A feasibility study.

The long acquisition time of CBCT discourages repeat verification imaging, therefore increasing treatment uncertainty. In this study, we present a fast volumetric imaging method for lung cancer radiation therapy using an orthogonal 2D kV/MV image pair. The proposed model is a combination of 2D and 3D networks. The proposed model consists of five major parts: (1) kV and MV feature extractors are used to extract deep features from the perpendicular kV and MV projections. (2) The feature-matching step is used to re-align the feature maps to their projection angle in a Cartesian coordinate system. By using a residual module, the feature map can focus more on the difference between the estimated and ground truth images. (3) In addition, the feature map is downsized to include more global semantic information for the 3D estimation, which is useful to reduce inhomogeneity. By using convolution-based reweighting, the model is able to further increase the uniformity of image. (4) To reduce the blurry noise of generated 3D volume, the Laplacian latent space loss calculated via the feature map that is extracted via specifically-learned Gaussian kernel is used to supervise the network. (5) Finally, the 3D volume is derived from the trained model. We conducted a proof-of-concept study using 50 patients with lung cancer. An orthogonal kV/MV pair was generated by ray tracing through CT of each phase in a 4D CT scan. Orthogonal kV/MV pairs from nine respiratory phases were used to train this patient-specific model while the kV/MV pair of the remaining phase was held for model testing. The results are based on simulation data and phantom results from a real Linac system. The mean absolute error (MAE) values achieved by our method were 57.5 HU and 77.4 HU within body and tumor region-of-interest (ROI), respectively. The mean achieved peak-signal-to-noise ratios (PSNR) were 27.6dB and 19.2dB within the body and tumor ROI, respectively. The achieved mean normalized cross correlation (NCC) values were 0.97 and 0.94 within the body and tumor ROI, respectively. A phantom study demonstrated that the proposed method can accurately re-position the phantom after shift. It is also shown that the proposed method using both kV and MV is superior to current method using kV or MV only in image quality. These results demonstrate the feasibility and accuracy of our proposed fast volumetric imaging method from an orthogonal kV/MV pair, which provides a potential solution for daily treatment setup and verification of patients receiving radiation therapy for lung cancer.

HLPW-4/GMGW-3: Mesh Adaptation for RANS Technology Focus Group Workshop Summary

The AIAA workshop series helps guide the computational fluid dynamics (CFD) community through publicly available test cases and impartial evaluation to establish the state-of-the-art in applied CFD. A summary of the Mesh Adaptation Technical Focus Group submissions for the joint Fourth CFD High Lift Prediction and Third Geometry & Mesh Generation Workshop is provided. The Common Research Model High-Lift variant (CRM-HL) is the target of this investigation. A 2D high-lift airfoil solution verification case was extracted from the 3D CRM-HL. Low variation is shown between 2D solutions with mesh adaptation to control estimated solution interpolation and output errors. As expected, variation for these mesh adaptation techniques is larger for the 3D CRM-HL than the 2D airfoil. More importantly, the 3D mesh-adapted results have a lower variation than expert-crafted meshes of equal or larger size. Suspected multiple solutions to Reynolds-averaged Navier–Stokes with the Spalart–Allmaras turbulence model appear to resolve into a common solution with mesh resolution.

Verification of numerical solutions of thermal radiation problems in participating and nonparticipating media

This article presents an application of the post-processing technique called Repeated Richardson Extrapolation in participating and nonparticipating media problems of radiative heat transfer. It allows us to achieve very accurate results, reducing the estimates of the discretization error of global variables with negligible additional computational time. The error estimates show to be accurate and reliable for code and solution verification. This work also presents equations that quantify the spatial discretization error inside the domain when the Discrete Ordinates Method is used to simulate participating media problems and when basic numerical integration rules are used to solve nonparticipating media problems.

Verification and benchmarking relativistic electron beam transport through a background gas

It is necessary to establish confidence in high-consequence codes containing an extensive suite of physics algorithms in the regimes of interest. Verification problems allow code developers to assess numerical accuracy and increase confidence that specific sets of model physics were implemented correctly in the code. The two main verification techniques are code verification and solution verification. In this work, we present verification problems that can be used in other codes to increase confidence in simulations of relativistic beam transport. Specifically, we use the general plasma code EMPIRE to model and compare with the analytical solution to the evolution of the outer radial envelope of a relativistic charged particle beam. We also outline a benchmark test of a relativistic beam propagating through a vacuum and pressurized gas cell, and present the results between EMPIRE and the hybrid code GAZEL. Further, we discuss the subtle errors that were caught with these problems and detail lessons learned.

Combining and evaluating function-oriented solutions in model-based systems engineering

Increasing complexity and shorter innovation cycles require a rethink in the development of mechatronic products. A function-oriented development process using model-based systems engineering (MBSE) methods promises to identify technical solutions via solution-neutral functional architectures. Since each function can typically be realized by multiple, alternative solutions the combinatorial number of overall system solutions explodes. Existing MBSE approaches enable to establish functional architectures as well as support the verification of solutions. However, evaluating different solution combinations with the current state of research involves repetitive and time-consuming modeling which leads to a heuristic selection of potentially suboptimal solutions. In this paper, we present a method for the systematic and function-oriented composition and model-based evaluation of solution combinations. Alternative solutions are structured based on a functional architecture, so that each possible solution combination can be composed and simulated with a minimum of effort, considering physical interactions and the efficiency. Based on simulation results, solution combinations can be verified against requirements and evaluated striving for the best solution combination. The elaborated method extends an existing MBSE approach and is illustrated by the running example of an electrical coolant pump of a passenger vehicle.