Integer Factorization with a Neuromorphic Sieve

The bound to factor large integers is dominated by the computational effort to discover numbers that are smooth, typically performed by sieving a polynomial sequence. On a von Neumann architecture, sieving has log-log amortized time complexity to check each value for smoothness. This work presents a neuromorphic sieve that achieves a constant time check for smoothness by exploiting two characteristic properties of neuromorphic architectures: constant time synaptic integration and massively parallel computation. The approach is validated by modifying msieve, one of the fastest publicly available integer factorization implementations, to use the IBM Neurosynaptic System (NS1e) as a coprocessor for the sieving stage.Comment: Fixed typos in equation for modular roots (Section II, par. 6; Section III, par. 2) and phase calculation (Section IV, par 2

General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv

The R package frailtySurv for simulating and fitting semi-parametric shared frailty models is introduced. Package frailtySurv implements semi-parametric consistent estimators for a variety of frailty distributions, including gamma, log-normal, inverse Gaussian and power variance function, and provides consistent estimators of the standard errors of the parameters' estimators. The parameters' estimators are asymptotically normally distributed, and therefore statistical inference based on the results of this package, such as hypothesis testing and confidence intervals, can be performed using the normal distribution. Extensive simulations demonstrate the flexibility and correct implementation of the estimator. Two case studies performed with publicly available datasets demonstrate applicability of the package. In the Diabetic Retinopathy Study, the onset of blindness is clustered by patient, and in a large hard drive failure dataset, failure times are thought to be clustered by the hard drive manufacturer and model

Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.Comment: arXiv admin note: text overlap with arXiv:2004.0362

Developing a keystroke biometric system for continual authentication of computer users,”

Abstract-Data windows of keyboard input are analyzed to continually authenticate computer users and verify that they are the authorized ones. Because the focus is on fast intruder detection, the authentication process operates on short bursts of roughly a minute of keystroke input, while the training process can be extensive and use hours of input. The biometric system consists of components for data capture, feature extraction, authentication classification, and receiveroperating-characteristic curve generation. Using keystroke data from 120 users, system performance was obtained as a function of two independent variables: the user population size and the number of keystrokes per sample. For each population size, the performance increased (and the equal error rate decreased) roughly logarithmically as the number of keystrokes per sample was increased. The best closed-system performance results of 99 percent on 14 participants and 96 percent on 30 participants indicate the potential of this approach

One-handed keystroke biometric identification competition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. V. Monaco, G. Perez, C. C. Tappert, P. Bours, S. Modal, S. Rajkumar, A. Morales, J. Fierrez, and J. Ortega-Garcia, "One-handed Keystroke Biometric Identification Competition", in International Conference on Biometrics, ICB 2015, 58-64This work presents the results of the One-handed Keystroke Biometric Identification Competition (OhKBIC), an official competition of the 8th IAPR International Conference on Biometrics (ICB). A unique keystroke biometric dataset was collected that includes freely-typed long-text samples from 64 subjects. Samples were collected to simulate normal typing behavior and the severe handicap of only being able to type with one hand. Competition participants designed classification models trained on the normally-typed samples in an attempt to classify an unlabeled dataset that consists of normally-typed and one-handed samples. Participants competed against each other to obtain the highest classification accuracies and submitted classification results through an online system similar to Kaggle. The classification results and top performing strategies are described.The authors would like to acknowledge the support from the National Science Foundation under Grant No. 1241585. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the US government

KBOC: Keystroke Biometrics OnGoing Competition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents the first Keystroke Biometrics Ongoing evaluation platform and a Competition (KBOC) organized to promote reproducible research and establish a baseline in person authentication using keystroke biometrics. The ongoing evaluation tool has been developed using the BEAT platform and includes keystroke sequences (fixedtext) from 300 users acquired in 4 different sessions. In addition, the results of a parallel offline competition based on the same data and evaluation protocol are presented. The results reported have achieved EERs as low as 5.32%, which represent a challenging baseline for keystroke recognition technologies to be evaluated on the new publicly available KBOC benchmarkA.M. and M. G.-B. are supported by a JdC contract (JCI-2012- 12357) and a FPU Fellowship from Spanish MINECO and MCD, respectively. J.M. and J.C. are supported by CAPES and CNPq (grant 304853/2015-1). This work was partially funded by the projects: CogniMetrics (TEC2015-70627-R) from MINECO FEDER and BEAT (FP7-SEC-284989) from E

Measurement of the t t-bar production cross section in the dilepton channel in pp collisions at sqrt(s) = 7 TeV

The t t-bar production cross section (sigma[t t-bar]) is measured in proton-proton collisions at sqrt(s) = 7 TeV in data collected by the CMS experiment, corresponding to an integrated luminosity of 2.3 inverse femtobarns. The measurement is performed in events with two leptons (electrons or muons) in the final state, at least two jets identified as jets originating from b quarks, and the presence of an imbalance in transverse momentum. The measured value of sigma[t t-bar] for a top-quark mass of 172.5 GeV is 161.9 +/- 2.5 (stat.) +5.1/-5.0 (syst.) +/- 3.6(lumi.) pb, consistent with the prediction of the standard model.Comment: Replaced with published version. Included journal reference and DO

Combined search for the quarks of a sequential fourth generation

Results are presented from a search for a fourth generation of quarks produced singly or in pairs in a data set corresponding to an integrated luminosity of 5 inverse femtobarns recorded by the CMS experiment at the LHC in 2011. A novel strategy has been developed for a combined search for quarks of the up and down type in decay channels with at least one isolated muon or electron. Limits on the mass of the fourth-generation quarks and the relevant Cabibbo-Kobayashi-Maskawa matrix elements are derived in the context of a simple extension of the standard model with a sequential fourth generation of fermions. The existence of mass-degenerate fourth-generation quarks with masses below 685 GeV is excluded at 95% confidence level for minimal off-diagonal mixing between the third- and the fourth-generation quarks. With a mass difference of 25 GeV between the quark masses, the obtained limit on the masses of the fourth-generation quarks shifts by about +/- 20 GeV. These results significantly reduce the allowed parameter space for a fourth generation of fermions.Comment: Replaced with published version. Added journal reference and DO