Password Cracking and Countermeasures in Computer Security: A Survey

With the rapid development of internet technologies, social networks, and other related areas, user authentication becomes more and more important to protect the data of the users. Password authentication is one of the widely used methods to achieve authentication for legal users and defense against intruders. There have been many password cracking methods developed during the past years, and people have been designing the countermeasures against password cracking all the time. However, we find that the survey work on the password cracking research has not been done very much. This paper is mainly to give a brief review of the password cracking methods, import technologies of password cracking, and the countermeasures against password cracking that are usually designed at two stages including the password design stage (e.g. user education, dynamic password, use of tokens, computer generations) and after the design (e.g. reactive password checking, proactive password checking, password encryption, access control). The main objective of this work is offering the abecedarian IT security professionals and the common audiences with some knowledge about the computer security and password cracking, and promoting the development of this area.Comment: add copyright to the tables to the original authors, add acknowledgement to helpe

Poole-Frenkel Effect and Phonon-Assisted Tunneling in GaAs Nanowires

We present electronic transport measurements of GaAs nanowires grown by catalyst-free metal-organic chemical vapor deposition. Despite the nanowires being doped with a relatively high concentration of substitutional impurities, we find them inordinately resistive. By measuring sufficiently high aspect-ratio nanowires individually in situ, we decouple the role of the contacts and show that this semi-insulating electrical behavior is the result of trap-mediated carrier transport. We observe Poole-Frenkel transport that crosses over to phonon-assisted tunneling at higher fields, with a tunneling time found to depend predominantly on fundamental physical constants as predicted by theory. By using in situ electron beam irradiation of individual nanowires we probe the nanowire electronic transport when free carriers are made available, thus revealing the nature of the contacts

Examining the Relationship Between Confusion and Learning: A Descriptive Meta-Analysis

Previous research into confusion and learning neglects to investigate how this relationship varies when faced with impact factors such as multiple types of affect and learning measurements, learning environment, or grade level. Moreover, past research also reports di-verse effect size values for this relationship, making the correlation ambiguous. As such, the current research seeks to reconcile these nuances between confusion and learning through a meta-analytic approach. In this analysis, it was found that there was no relationship between confusion and learning gains, or in the subgroup analysis of grade level. Since only one impact factor, grade level, was analyzed, it is considered that the analysis of other or multiple impact factors could help to further understand the association between confusion and learning. It is also reasoned that variability in the definitions of confusion and learning in the papers included in the meta-analysis as well as publication bias contribute to this result. As such, future research may choose to investigate these avenues and continued research generally into the association between confusion and learning would also be helpful to better understand this relationship

FluShuffle and FluResort: new algorithms to identify reassorted strains of the influenza virus by mass spectrometry

Background: Influenza is one of the oldest and deadliest infectious diseases known to man. Reassorted strains of the virus pose the greatest risk to both human and animal health and have been associated with all pandemics of the past century, with the possible exception of the 1918 pandemic, resulting in tens of millions of deaths. We have developed and tested new computer algorithms, FluShuffle and FluResort, which enable reassorted viruses to be identified by the most rapid and direct means possible. These algorithms enable reassorted influenza, and other, viruses to be rapidly identified to allow prevention strategies and treatments to be more efficiently implemented.Results: The FluShuffle and FluResort algorithms were tested with both experimental and simulated mass spectra of whole virus digests. FluShuffle considers different combinations of viral protein identities that match the mass spectral data using a Gibbs sampling algorithm employing a mixed protein Markov chain Monte Carlo (MCMC) method. FluResort utilizes those identities to calculate the weighted distance of each across two or more different phylogenetic trees constructed through viral protein sequence alignments. Each weighted mean distance value is normalized by conversion to a Z-score to establish a reassorted strain.Conclusions: The new FluShuffle and FluResort algorithms can correctly identify the origins of influenza viral proteins and the number of reassortment events required to produce the strains from the high resolution mass spectral data of whole virus proteolytic digestions. This has been demonstrated in the case of constructed vaccine strains as well as common human seasonal strains of the virus. The algorithms significantly improve the capability of the proteotyping approach to identify reassorted viruses that pose the greatest pandemic risk. © 2012 Lun et al.; licensee BioMed Central Ltd.Link_to_subscribed_fulltex

Parametric Optomechanical Oscillations in Two-Dimensional Slot-Type High-Q Photonic Crystal Cavities

We experimentally demonstrate an optomechanical cavity based on an air-slot photonic crystal cavity with optical quality factor Qo=4.2\times10^4 and a small modal volume of 0.05 cubic wavelengths. The optical mode is coupled with the in-plane mechanical modes with frequencies up to hundreds of MHz. The fundamental mechanical mode shows a frequency of 65 MHz and a mechanical quality factor of 376. The optical spring effect, optical damping and amplification are observed with a large experimental optomechanical coupling rate gom/2pi of 154 GHz/nm, corresponding to a vacuum optomechanical coupling rate g*/2pi of 707 kHz. With sub-mW or less input power levels, the cavity exhibits strong parametric oscillations. The phase noise of the photonic crystal optomechanical oscillator is also measured

Cancer-Associated noncoding mutations affect RNA G-quadruplex-mediated regulation of gene expression

© 2017 The Author(s). Cancer is a multifactorial disease driven by a combination of genetic and environmental factors. Many cancer driver mutations have been characterised in protein-coding regions of the genome. However, mutations in noncoding regions associated with cancer have been less investigated. G-quadruplex (G4) nucleic acids are four-stranded secondary structures formed in guanine-rich sequences and prevalent in the regulatory regions. In this study, we used published whole cancer genome sequence data to find mutations in cancer patients that overlap potential RNA G4-forming sequences in 5ⲠUTRs. Using RNAfold, we assessed the effect of these mutations on the thermodynamic stability of predicted RNA G4s in the context of full-length 5ⲠUTRs. Of the 217 identified mutations, we found that 33 are predicted to destabilise and 21 predicted to stabilise potential RNA G4s. We experimentally validated the effect of destabilising mutations in the 5ⲠUTRs of BCL2 and CXCL14 and one stabilising mutation in the 5ⲠUTR of TAOK2. These mutations resulted in an increase or a decrease in translation of these mRNAs, respectively. These findings suggest that mutations that modulate the G4 stability in the noncoding regions could act as cancer driver mutations, which present an opportunity for early cancer diagnosis using individual sequencing information.Link_to_subscribed_fulltex

identifying patients with relapsing remitting multiple sclerosis using algorithms applied to us integrated delivery network healthcare data

Abstract Background Relapsing-remitting multiple sclerosis (RRMS) has a major impact on affected patients; therefore, improved understanding of RRMS is important, particularly in the context of real-world evidence. Objectives To develop and validate algorithms for identifying patients with RRMS in both unstructured clinical notes found in electronic health records (EHRs) and structured/coded health care claims data. Methods US Integrated Delivery Network data (2010–2014) were queried for study inclusion criteria (possible multiple sclerosis [MS] base cohort): one or more MS diagnosis code, patients aged 18 years or older, 1 year or more baseline history, and no other demyelinating diseases. Sets of algorithms were developed to search narrative text of unstructured clinical notes (EHR clinical notes–based algorithms) and structured/coded data (claims-based algorithms) to identify adult patients with RRMS, excluding patients with evidence of progressive MS. Medical records were reviewed manually for algorithm validation. Positive predictive value was calculated for both EHR clinical notes–based and claims-based algorithms. Results From a sample of 5308 patients with possible MS, 837 patients with RRMS were identified using only the EHR clinical notes–based algorithms and 2271 patients were identified using only the claims-based algorithms; 779 patients were identified using both algorithms. The positive predictive value was 99.1% (95% confidence interval [CI], 94.2%–100%) for the EHR clinical notes–based algorithms and 94.6% (95% CI, 89.1%–97.8%) to 94.9% (95% CI, 89.8%–97.9%) for the claims-based algorithms. Conclusions The algorithms evaluated in this study identified a real-world cohort of patients with RRMS without evidence of progressive MS that can be studied in clinical research with confidence

Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia

Coherence analysis characterizes frequency-dependent covariance between signals, and is useful for multivariate oscillatory data often encountered in neuroscience. The global coherence provides a summary of coherent behavior in high-dimensional multivariate data by quantifying the concentration of variance in the first mode of an eigenvalue decomposition of the cross-spectral matrix. Practical application of this useful method is sensitive to noise, and can confound coherent activity in disparate neural populations or spatial locations that have a similar frequency structure. In this paper we describe two methodological enhancements to the global coherence procedure that increase robustness of the technique to noise, and that allow characterization of how power within specific coherent modes change through time.National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant K25-NS057580)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385)National Institutes of Health (U.S.) (Grant R01-MH071847