Template Scheme Research Articles

Boron-Doped Engineering for Carbon Quantum Dots-Based Memristors with Controllable Memristance Stability.

Carbon quantum dots-based memristors (CQDMs) have emerged as a rising star in data storage and computing. The key constraint to their commercialization is memristance variability, which mainly arises from the disordered conductive paths. Doping methodology can optimize electron and ion transport to help construct a stable conductive mode. Herein, based on boron (B)-doped engineering strategy, three kinds of comparable quantum dots are synthesized, including carbon quantum dots (CQDs), a series of boron-doped CQDs (BCQDs) with different B contents, and boron quantum dots. The corresponding device performances highlight the superiority of BCQDs-based memristors, exhibiting a ternary flash-type memory behavior with longer retention time and more controllable memristance stability. The comprehensive analysis results, including device performance, functional layer morphology, and material simulated calculation, illustrate that the doped B elements can directionally guide the migration of aluminum ions by enhancing the capture of free electrons, resulting in ordered conductive filaments and stable ternary memory behavior. Finally, the conceptual applications of logic display and logic gate are discussed, indicating a bright prospect for BCQDs-based memristors. This work proves that modest B doping can optimize memristance property, establishing a theoretical foundation and template scheme for developing effective and stable CQDMs.

Typology and Modeling Profiles of AS-Class Process-Entity Interactions: Structures, Constraints, Phases and Evolution of Process Phases

The situational nature, which is mostly resided in the procedures of conceptual data modeling today, does not allow either at the theoretical or practical levels to obtain functionally complete, and consistent conceptual and logical constructions for sufficiently capacious and isolated fragments of simulated systems. Moving from situational to pattern modeling is the one of the way to resolve this problem successfully. The maximum effect of using template schemes is achieved if the template component is selected correctly and all the relevant components of its life cycle are represented in a template. The pair "entity-generalized process" appears in the capacity of such component in the work. The choice of entities is quite obvious — any domain consists of entities. Adding processes as separate, self-sufficient objects and adhering them to entities along with concomitant objects setting locations and process subjects warrant a representation in the model of entities behavioral aspects involved to the generalized process. There is another positive effect from using "entity-process" pair — full coverage of all stages and interactions that accompany the generalized process. The work comprehensively and systematically examines interactions that occur in assembling composite objects of an arbitrary configuration. All significant stages for the vertical deployment of the generalized assembling process are allocated and investigated starting from the preprocess phases and "organization" phase and completing with the "implementation" phase. It is determined phase structures and is ascertained mechanisms providing a phase coherence. Standard constraints of As-processes have been considered for the obtained unified model schemes.

EU Cybersecurity Act and IoT Certification: Landscape, Perspective and a Proposed Template Scheme

The vulnerabilities in deployed IoT devices are a threat to critical infrastructure and user privacy. There is ample ongoing research and efforts to produce devices that are secure-by-design. However, these efforts are still far from translation into actual deployments. To address this, worldwide efforts towards IoT device and software certification have accelerated as a potential solution, including UK’s IoT assurance program, EU Cybersecurity Act and the US executive order 14028. In EU, the Cybersecurity Act was launched in 2019 which initiated the European cybersecurity certification framework for Internet and Communications Technology (ICT). The heterogeneity of the IoT landscape with devices ranging from industrial to consumer, makes it challenging to incorporate IoT devices in the certification framework or introduce a European cybersecurity certification scheme solely for IoT. This paper analyses the cybersecurity certification prospects for IoT devices and also places article 54 of the EU Cybersecurity Act in an international perspective. We conducted a comparative study of existing IoT certification schemes to identify potential gaps and extract requirements of a candidate IoT device security certification scheme. We also propose an approach that can be used as a template to instantiate an EU cybersecurity certification scheme for IoT devices. In the proposed template, we identify IoT-critical elements from the article 54 of the Cybersecurity Act. We also evaluate the proposed template using the ENISA qualification system for cybersecurity certification schemes and show its qualification on all criteria.

Robust Matching for SAR and Optical Images Using Multiscale Convolutional Gradient Features

Image matching is a key preprocessing step for the integrated application of synthetic aperture radar (SAR) and optical images. Due to significant nonlinear intensity differences between such images, automatic matching for them is still quite challenging. Recently, structure features have been effectively applied to SAR-to-optical image matching because of their robustness to nonlinear intensity differences. However, structure features designed by handcraft are limited to achieve further improvement. Accordingly, this letter employs the deep learning technique to refine structure features for improving image matching. First, we extract multiorientated gradient features to depict the structure properties of images. Then, a shallow pseudo-Siamese network is built to convolve the gradient feature maps in a multiscale manner, which produces the multiscale convolutional gradient features (MCGFs). Finally, MCGF is used to achieve image matching by a fast template scheme. MCGF can capture finer common features between SAR and optical images than traditional handcrafted structure features. Moreover, it also can overcome some limitations of current matching methods based on deep learning, which requires solving a huge number of model parameters by a large number of training samples. Two sets of SAR and optical images with different resolutions are used to evaluate the matching performance of MCGF. The experimental results show its advantage over other state-of-the-art methods.

Emblematic Mechanisms and Psychoanalysis

In the paper the parallels between the emblematic “mechanisms” of signification and the psychoanalytic theory of Sigmund Freud as well as Carl Gustav Jung have been studied. The Austrian psychiatrist has discovered template schemes that become a visual delineation, the blueprint for developing his scientific vocabulary, methodology, classification of psycho-emotional behavioral types in mythological plots. The Eros and Thanatos images handling, the exploitation of mythical tales about Oedipus and Electra, Prometheus, Narcissus, and many other ones to specify the behavioral complexes denote the presence of “emblematic methodology” in the formation of psychoanalytic conceptions and categories. His interpretations of famous mythological plots are boiled down to emblematic reduction.
 Carl Gustav Jung frequently selected symbolic notations as his research targets, which were a denotative space for expressing internal mental receptions and historic constellations of cultural axiology. In his writings we see the intention to assemble the concepts of image (iconic) and socio-cultural idea (conventional) into a sole compound that syncretically denote unity of meaning. Such an arrangement of iconic-conventional interdetermination is often significative elbowroom in Jung the decoding of which may allow to discern complex mental reflections. Notwithstanding the fact that he considers a symbol to be the standard unit of cognitive-cultural experience “conservation”, its functional semantics definition is fulfilled in emblematic patterns. This emblematic-cognitive form is not only a method of determining the initial images-ideas of the unconscious, “the mythological figures” of inner conflicts, typical experience of generations, but also the principle of justification and expression of his theory conceptual foundation. To a certain extent, it is an element of the Swiss psychologist’s scientific thinking style and language.

Research on Signature Verification Method Based on Discrete Fréchet Distance

This paper proposes a multi-feature signature template based on discrete Fréchet distance, which breaks through the limitation of traditional signature authentication using a single signature feature. It solves the online handwritten signature authentication signature global feature template extraction calculation workload, signature feature selection unreasonable problem. In this experiment, the false recognition rate (FAR) and false rejection rate (FRR) of the statistical signature are calculated and the average equal error rate (AEER) is calculated. The feasibility of the combined template scheme is verified by comparing the average equal error rate of the combination template and the original template.

An alignment-free cancelable fingerprint template for bio-cryptosystems

Biometric based authentication systems have been widely deployed to mobile devices. Security and privacy issues arise especially when the biometric templates stored in centralized database or personal portable devices are obtained by adversaries. To make matters worse, unlike password, biometric traits are irreplaceable once compromised. Despite the fact that a number of methods have been reported to protect biometric templates, there are rare solutions that satisfy all the criteria of template protection simultaneously (i.e. non-linkability, cancelability, non-invertibility and performance). In this paper, we propose an alignment-free cancelable template scheme to protect fingerprint minutiae. The proposed template scheme is the extended version of the polar grid based 3-tuple quantization with condensed feature length for lower computation cost. To enhance the non-invertibility, bit-toggling strategy is proposed to inject noise into the proposed fingerprint template. Furthermore, cancelability is also achieved by incorporating discrete Fourier transform and random projection on the proposed template. Due to the use of discrete Fourier transform, the resultant template will be converted into complex form which increases the difficulty of attacker in breaking its security. The proposed template scheme is evaluated on FVC2002 and FVC2004 databases (DB1, DB2 and DB3) and the experimental results demonstrate a comparable accuracy over the existing methods. The observation from the security and privacy analysis suggests that the proposed template is robust against the major attacks, e.g. template inversion attack, attack via record multiplicity, etc. Lastly, we demonstrate that the proposed template can be applied in the bio-cryptosystems.

Design and Application of Integrated Assembly Technology of FRG in Residential Ceiling

FRG material is a new environmentally friendly indoor decoration materials and popular in prefabricated construction, the paper introduces the performance and design of materials, and takes FRG in the residential ceiling integrated assembly process into a demonstration project, which showed that FRG in the prefabricated modules integrated ceiling of the whole template scheme has a great artistry and application effect. Meanwhile it provides reference for the integrated ceiling assembly modular process design of similar indoor decoration.

Analogy-based volume exploration using ellipsoidal Gaussian transfer functions

Much effort has been made on multidimensional transfer function, which is designed for effective exploration of 3D scalar datasets. But now, existing solution for designing transfer function typically focuses on exploring volume independently without any prior knowledge. It remains, however, a big challenge for us to reuse the explored knowledge, experience and results in scientific visualization. In this paper, we present a novel technique that employs an analogy-based approach. It aims to facilitate automatic volume exploration for multiple datasets which may share common context or features. The kernel of our approach is using the template scheme. With the introduction of the Gaussian Mixture Model, we adopt this new scheme to modeling, designing and transferring—they are processed in the data histogram space. Then, we integrate this scheme into two-dimensional transfer function design. The result shows that the interesting features can easily be captured with little user workload after adopting our approach.

Hierarchical template-based hexahedral mesh generation

This work describes a hexahedral mesh generation algorithm ideally suited for transition subdomain meshes in the context of any domain decomposition meshing strategy. The algorithm is based on an automatic hierarchical region decomposition in which, in the last level, it is possible to generate hexahedral elements with a conventional mapping strategy. Templates usually impose restrictions on meshes on boundary surfaces of a subdomain to be meshed. The proposed hierarchical template scheme eliminates many of these restrictions, creating more options in surface meshes and mesh refinement as input boundary data. This work implements four basic templates for volumes that should be simplified by a unit box. The volume decomposition is performance using a cube-shaped parametric space in the local coordinate system. The algorithm decomposes hierarchically a volume until all volumes should be mappable to generate elements by the 3D mapping approach. Examples demonstrate that this idea may result in structured meshes in complex geometries.

Finger vein secure biometric template generation based on deep learning

Leakage of unprotected biometric authentication data has become a high-risk threat for many applications. Lots of researchers are investigating and designing novel authentication schemes to prevent such attacks. However, the biggest challenge is how to protect biometric data while keeping the practical performance of identity verification systems. For the sake of tackling this problem, this paper presents a novel finger vein recognition algorithm by using secure biometric template scheme based on deep learning and random projections, named FVR-DLRP. FVR-DLRP preserves the core biometric information even with the user’s password cracked, whereas the original biometric information is still safe. The results of experiment show that the algorithm FVR-DLRP can maintain the accuracy of biometric identification while enhancing the uncertainty of the transformation, which provides better protection for biometric authentication.

PH-Activatable MnO-Based Fluorescence and Magnetic Resonance Bimodal Nanoprobe for Cancer Imaging.

Stimuli-responsive nanoprobes that combine both fluorescence and magnetic resonance imaging (MRI) are anticipated to be highly beneficial for tumor visualization with high imaging sensitivity. By employing an interfacial templating scheme, a pH-activatable fluorescence/MRI dual-modality imaging nanoprobe is successfully developed based on the coencapsulation of MnO nanoparticles and coumarin-545T inside a hybrid silica nanoshell. To promote cancer cell targeting with high-specificity, the nanoprobes are also conjugated with folic acid to establish a greater affinity for cancer cells that over-express folate receptors on their cell membrane. In the new nanosystem, MnO nanoparticles are shown to function as an efficient fluorescence quencher of coumarin-545T prior to cellular uptake. However, fluorescence recovery is achieved upon acidic dissolution of the MnO nanoparticles following receptor-mediated endocytosis into the low pH compartments of the cancer cells. Meanwhile, the Mn(2+) ions thus released are also shown to exert a strong T1 contrast enhancement in the cancer cells. Therefore, by demonstrating the dual-activatable MRI and fluorescence imaging in response to the low pH conditions, it is envisioned that these nanoprobes would have tremendous potential for emerging cancer-imaging modalities such as image-guided cancer therapy.

A Hybrid Silica Nanoreactor Framework for Encapsulation of Hollow Manganese Oxide Nanoparticles of Superior T1 Magnetic Resonance Relaxivity

A new hybrid nanoreactor framework with poly(ethylene oxide)‐perforated silica walls is designed to encapsulate hollow manganese oxide nanoparticles (MONs) of high distinctness and homogeneity. Achieved by an interfacial templating scheme, the nanoreactor ensures that acidic etching of MONs by an acetate buffer solution is highly controlled for precise control of the hollow interior. As such, hollow MONs with different nanostructures are developed successfully through a facile acetate buffer solution etching. The resultant hollow MONs are integrated within the hybrid nanoreactor and demonstrate superior r1 relativity of up to 2.58 mm−1 s−1 for T1 magnetic resonance imaging (MRI). By modifying the nanoreactor architecture, it is also demonstrated that the efficacy of MONs as T1 MRI contrast agents can be significantly improved if an optimal cluster of hollow MONs is encapsulated into the hybrid silica framework. The evolution of core morphology with time is studied to elucidate the etching mechanism. It is revealed that the hollow formation arises due to the surface stabilization of MONs by acetate ions and the subsequent acidic etching of the interior core in a sporadic manner. This is different from the commonly reported nanoscale Kirkendall effect or the selective etching of the core–shell MnO/Mn3O4 structure.

Hierarchical template-based quadrilateral mesh generation

In the context of any domain decomposition meshing strategy, this paper describes a quadrilateral mesh generation algorithm ideally suited for subdomains in which transition of mesh refinement is required. The algorithm is based on an automatic hierarchical region decomposition in which, in the last level, it is possible to generate quadrilateral elements with a conventional mapping strategy. In two dimensions, a subdomain is usually a triangle or a rectangle. In this algorithm, a subdomain with two boundary curves may also be allowed. Direct use of mapping algorithms imposes restrictions on the number of boundary curve segments of a subdomain to be meshed. The proposed hierarchical template scheme eliminates these restrictions, requiring only an even number of boundary segments. Other algorithms in the literature present similar characteristics. However, the implementation of the hierarchical decomposition and its templates presented here is quite simple compared to other approaches. Six high-level templates are considered for a subdomain, depending on the number of boundary curves and the number of segments on each curve. Some examples demonstrate that this simple idea may result in structured meshes of good quality. We also show that the quadrilateral elements can be used as input to generate hexahedral elements. Three-dimensional sweeping examples, which use the proposed meshing scheme in the source and target surfaces, are also shown.

A novel template protection scheme for multibiometrics based on fuzzy commitment and chaotic system

In recent years, biometrics template protection has been extensively studied and lots of schemes have been proposed. However, most of them have not considered the forgery, large difference of intra-class and the security of unimodal biometrics leakage. And there is no multibiometrics template scheme based on the fusion of dual iris, thermal and visible face images. In this paper, a novel multibiometrics template protection scheme based on fuzzy commitment and chaotic system, and the security analysis approach for unimodal biometrics leakage are proposed. Firstly, the thermal face images are captured to overcome the forgery. Then, the fuzzy commitment is generated from the corporation of error correcting code (ECC) and the fusion binary features. Additionally, the dual iris feature vectors are encrypted via the chaotic system, and the score level fusion based on Aczel-Alsina triangular-norm (AA T-norm) is implemented to acquire the final verification performance. Finally, the entropy of both mutlibiometrics and unimodal information leakage is analyzed to show the security of the proposed approach. The experimental tests are conducted on a virtual multibiometrics database, which merges the challenging CASIA-Iris-Thousand and the NVIE face database. The verification performance decreases from EER of \(3 \times 10^{-2}\) to \(1.163 \times 10^{-1}\) %, but the multibiometrics template security is enhanced from 80.53 to 167.80 bits based on BCH ECC (1,023, 123, 170).

An Ontology-Based Automatic Semantic Annotation Approach for Patent Document Retrieval in Product Innovation Design

Patent retrieval plays a very important role in product innovation design. However, current patent retrieval approaches lack semantic comprehension and association, and usually cannot capture the implicit useful knowledge at a semantic level. In order to improve the traditional patent search, this paper proposes a novel ontology-based automatic semantic annotation approach based on the thorough analysis of patent documents, which combines both structure and content characteristics, and integrates multiple techniques from various aspects. Multilayer semantic model is established to realize unified semantic representation. The approach first utilizes template schemes to extract the structure information from patent documents, and then identifies semantics of entities and relations between entities from the content based on natural language processing techniques and domain knowledge, and at last employs a heuristic pattern learning method to abstract patent technical features. Case study is provided to show that our approach can acquire multi-level patent semantic knowledge from multiple perspectives, and discover semantic correlations between patent documents, which can further promote the accurate patent semantic retrieval effectively.

Design of alignment-free cancelable fingerprint templates via curtailed circular convolution

Fraudulent use of stolen fingerprint data and privacy invasion by tracking individuals unlawfully with shared or stolen fingerprint data justify the significance of fingerprint template protection. With no a priori fingerprint image registration, alignment-free cancelable fingerprint templates do not suffer from inaccurate singular point detection. In this paper, we propose an effective alignment-free method for constructing cancelable fingerprint templates via curtailed circular convolution. The proposed method features an efficient one-way transform, which protects the input binary string such that it cannot be retrieved from the length-reduced, convolved output vector. The transformed template fulfills the requirements of non-invertibility, revocability and diversity for cancelable fingerprint templates. Evaluation of the proposed scheme over FVC2002 DB1, DB2 and DB3 shows that the new method demonstrates satisfactory performance compared to the existing alignment-free cancelable template schemes.

Attacks via record multiplicity on cancelable biometrics templates

ABSTRACTVarious template protection techniques have been developed in the past few years, among which cancelable biometrics is a very popular and efficient one. It uses a noninvertible transformation to map an original template to a transformed domain to prevent the recovery of the original template from a compromised transformed template. Generally, cross‐template attacks over cancellable template schemes are evaluated through statistic independence metrics. In this paper, we investigate approaches to launch attacks through cryptanalysis. Four typical cancelable fingerprint template design algorithms have been investigated by applying the attack via record multiplicity attack to retrieve the original template. Concrete attack examples are also given to make the demonstration more intuitive and comprehensive. The results show that all of them are vulnerable if an attacker can obtain multiple transformed templates and their corresponding transformation parameters. Copyright © 2013 John Wiley & Sons, Ltd.

Alignment-free cancelable fingerprint template design: A densely infinite-to-one mapping (DITOM) approach

Registration-based cancelable template schemes rely on accurate fingerprint image alignment, which is very difficult to achieve. In this paper, by exploiting pair-minutiae vectors, we develop a lightweight, alignment-free scheme for generating cancelable fingerprint templates. The proposed mathematical model is based on a densely infinite-to-one mapping (DITOM) aiming to achieve the non-invertible property. The transformation designed describes the intersection of a collection of hyperplanes and effectively realizes infinite-to-one mapping. The proposed scheme has the properties of non-invertibility, revocability and multiple template independence. Evaluation of the proposed scheme over FVC2002 DB1, DB2 and DB3 shows that the new method exhibits satisfactory performance compared to existing methods.

An Ontology-based Automatic Semantic Annotation Approach for Patent Document Retrieval in Product Innovation Design

Patent retrieval plays a very important role in product innovation design. However, current patent retrieval approaches lack semantic comprehension and association, and usually cannot capture the implicit useful knowledge at a semantic level. In order to improve the traditional patent search, this paper proposes a novel ontology-based automatic semantic annotation approach based on the thorough analysis of patent documents, which combines both structure and content characteristics, and integrates multiple techniques from various aspects. Multilayer semantic model is established to realize unified semantic representation. The approach first utilizes template schemes to extract the structure information from patent documents, and then identifies semantics of entities and relations between entities from the content based on natural language processing techniques and domain knowledge, and at last employs a heuristic pattern learning method to abstract patent technical features. Case study is provided to show that our approach can acquire multi-level patent semantic knowledge from multiple perspectives, and discover semantic correlations between patent documents, which can further promote the accurate patent semantic retrieval effectively.