Signature Pairs Research Articles

Revocable identity-based designated verifier proxy re-signature with signature evolution

Identity-based proxy re-signature (IDPRS) enables a semi-trusted proxy to convert one user’s signature to another user’s for the same message without revealing any secret information of both users’ signing keys. In IDPRS, user revocation is a necessary problem arising from privilege expiry or private key compromise. A revoked user cannot produce new signatures including re-signatures from the proxy. There is a need for some applications like a contract management system to keep expired message and signature pairs valid in the next period. The conventional solution is to require the unrevoked users to sign these messages again, which burdens users heavily. Therefore, establishing an IDPRS system with revocation and efficient signature updating is crucial. This paper introduces the concept of revocable IDPRS with signature evolution, facilitating efficient signature updating. The signature evolution enables the proxy to batch update non-revoked users’ signatures from old ones to new ones, while also offering precise execution of signature updates on specific messages. Moreover, to further protect the privacy of signature validity, we employ designated verifier in our scheme. Formal security proofs are provided, and performance evaluation demonstrates our scheme’s lower computation costs for signature updating and its comprehensive security attributes.

A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders

The clinical burden of mental illness, in particular schizophrenia and bipolar disorder, are driven by frequent chronic courses and increased mortality, as well as the risk for comorbid conditions such as cardiovascular disease and type 2 diabetes. Evidence suggests an overlap of molecular pathways between psychotic disorders and somatic comorbidities. In this study, we developed a computational framework to perform comorbidity modeling via an improved integrative unsupervised machine learning approach based on multi-rank non-negative matrix factorization (mrNMF). Using this procedure, we extracted molecular signatures potentially explaining shared comorbidity mechanisms. For this, 27 case–control microarray transcriptomic datasets across multiple tissues were collected, covering three main categories of conditions including psychotic disorders, cardiovascular diseases and type II diabetes. We addressed the limitation of normal NMF for parameter selection by introducing multi-rank ensembled NMF to identify signatures under various hierarchical levels simultaneously. Analysis of comorbidity signature pairs was performed to identify several potential mechanisms involving activation of inflammatory response auxiliarily interconnecting angiogenesis, oxidative response and GABAergic neuro-action. Overall, we proposed a general cross-cohorts computing workflow for investigating the comorbid pattern across multiple symptoms, applied it to the real-data comorbidity study on schizophrenia, and further discussed the potential for future application of the approach.

Distribution of local curvature values as a structural feature for off-line handwritten signature verification

In the paper, a new feature for describing a digital image of a handwritten signature based on the frequency distribution of the values of the local curvature of the signature contours, is proposed. The calculation of this feature on the binary image of a signature is described in detail. A normalized histogram of distributions of local curvature values for 40 bins is formed. The frequency values recorded as a 40-dimensional vector are called the local curvature code of the signature.During verification, the proximity of signature pairs is determined by correlation between curvature codes and LBP codes described by the authors in [23]. To perform the signature verification procedure, a two-dimensional feature space is constructed containing images of the proximity of signature pairs. When verifying a signature with N authentic signatures of the same person, N(N-1)/2 patterns of the proximity of pairs of genuine signatures and N images of pairs of proximity of the analyzed signature with genuine signatures are presented in the feature space. The Support Vector Machine (SVM) is used as a classifier.Experimental studies were carried out on digitized images of genuine and fake signatures from two databases. The accuracy of automatic verification of signatures on the publicly available CEDAR database was 99,77 % and on TUIT was 88,62 %.

FBN: Federated Bert Network with client-server architecture for cross-lingual signature verification

Online signature verification has a great challenge due to the poor performance of deep learning techniques on cross-lingual datasets under privacy constraints. In this paper, we propose a novel Federated Bert Network (FBN) by embedding the Bidirectional Encoder Representations from Transformers (Bert) into a Federated Learning (FL) framework with client-server architecture. A new Length Alignment Algorithm is employed to unify the signature pairs’ sequence length, and the input representations are fed into the different clients to complete the independent learning of local-models. In addition, the server (coordinator) uses the improved Federated Average Algorithm with Reward-Punishment Mechanism (FedAvgRP) to aggregate these local-models and further generate a global-model. After multiple iterations, the optimal model can be obtained and cross-tested on four datasets (SVC 2004, MCYT-330, BioecurID, and Ours) with skilled forged (random forged) EERs of 7.65% (4.76%), 10.73% (8.46%), 10.09% (7.13%), and 8.28% (5.74%), respectively, far higher than that of the independent learning of state-of-the-art methods. Compared with the domain adaptation and improved FL models, our FBN model performs best in random and skilled forgery scenarios. Moreover, the FedAvgRP algorithm helps our model maintain high performance in the face of data attacks.

Alterations of the Composition and Neurometabolic Profile of Human Gut Microbiota in Major Depressive Disorder.

Major depressive disorder (MDD) is among the most prevalent mental disorders worldwide. Factors causing the pathogenesis of MDD include gut microbiota (GM), which interacts with the host through the gut–brain axis. In previous studies of GM in MDD patients, 16S rRNA sequencing was used, which provided information about composition but not about function. In our study, we analyzed whole metagenome sequencing data to assess changes in both the composition and functional profile of GM. We looked at the GM of 36 MDD patients, compared with that of 38 healthy volunteers. Comparative taxonomic analysis showed decreased abundances of Faecalibacterium prausnitzii, Roseburia hominis, and Roseburia intestinalis, and elevated abundances of Escherichia coli and Ruthenibacterium lactatiformans in the GM of MDD patients. We observed decreased levels of bacterial genes encoding key enzymes involved in the production of arginine, asparagine, glutamate, glutamine, melatonin, acetic, butyric and conjugated linoleic acids, and spermidine in MDD patients. These genes produced signature pairs with Faecalibacterium prausntizii and correlated with decreased levels of this species in the GM of MDD patients. These results show the potential impact of the identified biomarker bacteria and their metabolites on the pathogenesis of MDD, and should be confirmed in future metabolomic studies.

Distributed Functional Signature with Function Privacy and Its Application

We introduce a novel notion of distributed functional signature. In such a signature scheme, the signing key for function f will be split into n shares sk f i and distributed to different parties. Given a message m and a share sk f i , one can compute locally and obtain a pair signature f i m , σ i . When given all of the signature pairs, everyone can recover the actual value f m and corresponding signature σ . When the number signature pairs are not enough, nobody can recover the signature f m , σ . We formalize the notion of function privacy in this new model which is not possible for the standard functional signature and give a construction from standard functional signature and function secret sharing based on one-way function and learning with error assumption. We then consider the problem of hosting services in multiple untrusted clouds, in which the verifiability and program privacy are considered. The verifiability requires that the returned results from the cloud can be checked. The program privacy requires that the evaluation procedure does not reveal the program for the untrusted cloud. We give a verifiable distributed secure cloud service scheme from distributed functional signature and prove the securities which include untrusted cloud security (program privacy and verifiability) and untrusted client security.

System-Level Counterfeit Detection Using On-Chip Ring Oscillator Array

Counterfeiting has unfortunately become a worldwide epidemic affecting electronic systems from consumer goods to military equipment, which seriously jeopardizes system security, reliability, and electronic vendors’ reputation. The counterfeit parts, e.g., integrated circuits (ICs) and printed circuit boards (PCBs), have shown a significant increase in type and number over the recent years. However, the existing counterfeit detection techniques deal with IC or PCB separately, and hence, they cannot verify the authenticity of an electronic system as a whole. In this paper, we propose concurrent IC and PCB authentication (CIPA), a novel methodology that concurrently verifies the authenticity of both IC and PCB through extracting the signature pairs generated by a ring oscillator (RO) array without/with PCB cavity resonance. With CIPA, remote authentication is allowable by transmitting the signatures between the verifier and the system vendor. The CIPA structure has shown insignificant area overhead (0.945% on average) when implemented on a number of benchmarks. Both CIPA and the benchmarks have been implemented on the authentic and counterfeit FPGA systems, and the results give 100% confidence in detecting counterfeit ones. Furthermore, the authenticity of PCB and IC (i.e., authentic or counterfeit) of the system under test can also be mined from CIPA signatures. According to the experimental results, systems composed of different authenticity states of PCB and IC are differentiated from each other with the confidence of 97.62%. The overall authentication time is $40.2~\mu \text{s}$ considering 50-MHz system clock.

Online handwritten signature verification using feature weighting algorithm relief

Online handwritten signatures are widely used as a reliable identity authentication technology in industries such as banking, insurance and hospitals. Most existing online handwritten signature verification schemes usually choose the same feature sets for different users while comparing different signatures, which may reduce the stability of the dynamic characteristics. To avoid this problem, we propose a novel writer-dependent online signature verification technique based on Relief. For each user, our verification scheme falls into two phases: the training phase and the test phase. In the training phase, we select a signature as the base signature from real signatures and construct two different kinds of signature pairs: the real signature pairs and the forged signature pairs. Each pair of real signatures consists of two real signatures, and each pair of forgeries is made up of a real signature and a forgery. A group of combined features are defined for each pair of signatures. Then, we select more stable combined features by means of the Relief algorithm and compute a matrix used as a matching template, which contains the value of each combined feature for each pair of signatures. In the test phase, the classification of the pair of signatures composed of the test signature and the base reference signature is determined by the K-nearest neighbor. Experiments are conducted on our own database and SVC2004, and the results indicate that the method we propose here shows lower FAR and FRR than do traditional methods on the same databases.

An enhanced handwritten signature verification model applied on off-line benchmark data sets

ABSTRACTThe automatic, highly accurate and instantaneous authentication of a static off-line handwritten signature is highly required in financial, legal and governmental sectors. The genuine signature features are extracted by models like scale-invariant feature transform (SIFT) and speeded-up robust features (SURF), then the common patterns among these features are recognised for verifying new signatures. Although SIFT/SURF-based matching models are commonly used because of their robustness and low computational complexity, their tolerance to the unconstrained signature styles of large variations is not very satisfactory. This work presents three main drawbacks of the traditional application of this model in signature verification and proposes a set of modifications to customise this model to fit the nature of the handwriting domain. These modifications consider initially the limited size of the descriptor window in SIFT/SURF model, then consider the single point-to-point and the crisp matching of the points-of-interest in the tested signature pairs. The experimental work is applied on two benchmark data sets that contain a set of genuine and skilled forged signatures of multiple users. Furthermore, a comparative analysis is applied to show the reflection of each modification in enhancing the accuracy percentage of the signature verification.

Indoor human response to blast noise measured in situ.

As a result of suburban sprawl, the number of people living near military installations is drastically increasing. Coupled with an escalation of military activities and preparedness, the potential for noise generated by an installation to impact the surrounding communities has grown, especially for large amplitude impulsive events such as those generated during artillery training exercises. To assess the effect of blast noise on individuals living near installations, a large scale in situ study has been performed. The homes of study participants were instrumented and outdoor/indoor blast signature pairs of routine installation activities were captured over the course of 1 year. Participants filled out short questionnaires whenever they heard blast noise events. Measurements of single events at subjects’ homes along with their responses present unique data with which to investigate the human response to blast noise on an event‐by‐event basis. In this presentation, the characteristics of the noise typically experienced by residents in their own homes will be examined and used to create dose‐response relationships. Comparison will be made of dose‐response curves based on annoyance, interference, and loudness as a function of level‐based metrics, and as a function of a variety of psychoacoustic metrics. [Work supported by the Strategic Environmental Research and Development Program.]

Hermitian Symmetric Polynomials and CR Complexity

Properties of Hermitian forms are used to investigate several natural questions from CR geometry. To each Hermitian symmetric polynomial we assign a Hermitian form. We study how the signature pairs of two Hermitian forms behave under the polynomial product. We show, except for three trivial cases, that every signature pair can be obtained from the product of two indefinite forms. We provide several new applications to the complexity theory of rational mappings between hyperquadrics, including a stability result about the existence of non-trivial rational mappings from a sphere to a hyperquadric with a given signature pair.

Modular composition predicts kinase/substrate interactions

BackgroundPhosphorylation events direct the flow of signals and metabolites along cellular protein networks. Current annotations of kinase-substrate binding events are far from complete. In this study, we scanned the entire human protein sequences using the PROSITE domain annotation tool to identify patterns of domain composition in kinases and their substrates. We identified statistically enriched pairs of strings of domains (signature pairs) in kinase-substrate couples presented in the 2006 version of the PTM database.ResultsThe signature pairs enriched in kinase - substrate binding interactions turned out to be highly specific to kinase subtypes. The resulting list of signature pairs predicted kinase-substrate interactions in validation dataset not used in learning with high statistical accuracy.ConclusionsThe method presented here produces predictions of protein phosphorylation events with high accuracy and mid-level coverage. Our method can be used in expanding the currently available drafts of cell signaling pathways and thus will be an important tool in the development of combination drug therapies targeting complex diseases.

Band structures extending to very high spin inXe126

High-spin states in $^{126}\mathrm{Xe}$ have been populated in the $^{82}\mathrm{Se}$($^{48}\mathrm{Ca}$,4$n$)$^{126}\mathrm{Xe}$ reaction in two experiments, one at the VIVITRON accelerator in Strasbourg using the Euroball detector array, and a subsequent one with ATLAS at Argonne using the Gammasphere Ge-detector array. Levels and assignments made previously for $^{126}\mathrm{Xe}$ up to $I=20$ have been confirmed and extended. Four regular bands extending to a spin of almost $I=60$, which are interpreted as two pairs of signature partners with opposite parity, are identified for the first time. The \ensuremath{\alpha} = 0 partner of each pair is connected to the lower-lying levels, whereas the two \ensuremath{\alpha} = 1 partners remain floating. A fractional Doppler shift analysis of transitions in the strongest populated $(\ensuremath{\pi},\ensuremath{\alpha})=(\ensuremath{-},0)$ band provides a value of ${5.2}_{0.5}^{0.4}$ b for the transition quadrupole moment, which can be related to a minimum in the potential-energy surface calculated by the ULTIMATE CRANKER cranked shell-model code at $\ensuremath{\epsilon}\ensuremath{\approx}0.35$ and $\ensuremath{\gamma}\ensuremath{\approx}{5}^{\ifmmode^\circ\else\textdegree\fi{}}$. The four lowest bands calculated for this minimum compare well with the two signature pairs experimentally observed over a wide spin range. A sharp upbend at $\ensuremath{\hbar}\ensuremath{\omega}~1170$ keV is interpreted as a crossing with a band involving the ${j}_{15/2}$ neutron orbital, for which pairing correlations are expected to be totally quenched. The four long bands extend to within \ensuremath{\sim}5 spin units of a crossing with an yrast line defined by calculated hyperdeformed transitions and will serve as important stepping stones into the spin region beyond 60\ensuremath{\Elzxh} for future experiments.

Using Landmarks to Establish a Point-to-Point Correspondence between Signatures

This paper introduces a new approach for point-to-point correspondence finding, which can be used as pre-processing stage of a handwritten signature verification procedure. This approach provides a solid basis for comparing function features of two handwritten signatures. Corner points of the signatures are first extracted based on velocity information. The characteristics of curvilinear velocity and angular velocity are combined successfully by functions based on membership criteria. The signatures to be compared are then segmented at landmarks obtained by corner matching based on similarity measures. In the last step, the corresponding pairs of segments are mapped by a point-to-point matching algorithm, minimising a curve deformation energy. The techniques described were applied to a set of 188 signatures from 19 volunteers. The resulting point-to-point matching of signature pairs was satisfactory in all cases where there was a visual agreement between the signatures.