While much has changed in Internet security over the past decades, textual passwords remain as the dominant method to secure user web accounts and they are proliferating in nearly every new web services. Nearly every web services, no matter new or aged, now enforce some form of password creation policy. In this work, we conduct an extensive empirical study of 50 password creation policies that are currently imposed on high-profile web services, including 20 policies mainly from US and 30 ones from mainland China. We observe that no two sites enforce the same password creation policy, there is little rationale under their choices of policies when changing policies, and Chinese sites generally enforce more lenient policies than their English counterparts.

The Emperor's New Password Creation Policies.

A probabilistic password model assigns a probability value to each string. Such models are useful for research into understanding what makes users choose more (or less) secure passwords, and for constructing password strength meters and password cracking utilities. Guess number graphs generated from password models are a widely used method in password research. In this paper, we show that probability-threshold graphs have important advantages over guess-number graphs. They are much faster to compute, and at the same time provide information beyond what is feasible in guess-number graphs. We also observe that research in password modeling can benefit from the extensive literature in statistical language modeling. We conduct a systematic evaluation of a large number of probabilistic password models, including Markov models using different normalization and smoothing methods, and found that, among other things, Markov models, when done correctly, perform significantly better than the Probabilistic Context-Free Grammar model proposed in Weir et al., which has been used as the state-of-the-art password model in recent research.

A study of probabilistic password models

Optical Sensors Fusion is intended to enrich the data obtained from Robotic Vision systems, which play a crucial role in applications such as machine guidance and monitoring. This paper presents a data augmentation method that uniquely combines cameras with the rotational wide-based Laser Scanner Technical Vision System (LSTVS), an innovative system not previously explored in conjunction with other 3D data acquisition systems. This combined work aims to address common issues in 3D reconstruction by utilizing a deterministic position estimation from the laser scanner complementary to probabilistic estimations from stereo vision. This approach aims to reduce informational entropy in regions where data is lacking or difficult to interpret, primarily due to the inherent limitations of Robotic Vision systems. An LSTVS with stereo cameras prototype was calibrated using intrinsic and extrinsic parameters of the cameras and laser scanner components, enabling laser positioning over selected interest point and areas from the stereo 3D data. By fusing 3D data from both systems, data quality is improved on challenging surfaces often problematic for stereo vision, like low texture or non-Lambertian surfaces. Experiments aim to test the stereo system limits in order to fuse the obtained data. Multiple experiments with variable parameters (angle of view, striking distance, most indicative kinds of the obstacle's refractive surface among them) are described in order to prove new abilities of the proposed combined RV. This improvement is made possible by using complementary data from LSTVS and original smart algorithm of image patching, as shown in the experimental results. 

Multi-view 3D data fusion and patching to reduce Shannon entropy in Robotic Vision

Welded features in the tubular joints are one of the most severe sources of fatigue failure in supporting structure for offshore wind turbines (OWTs). Therefore, fatigue resistance becomes a vital design, inspection and maintenance criterion for tripod supporting structures. This paper proposes a time-domain fatigue damage assessment approach for tripod structures by establishing a detailed finite element model for the critical zones and simplified beam model for the remaining regions. The stress influence matrix (SIM) is formed once by utilizing multiple finite element analyses. Based on the equivalent static loads and linear superposition principle, the time-variant stress can be efficiently calculated. By comparing the fatigue damage caused by multiple loads to that caused by a single load in a particular direction, we conclude that the advantage of the proposed method over the conventional stress concentration factors method. Afterwards, we investigate the effects of environmental conditions on the cumulative fatigue damage of critical zones. The fatigue resistances of various structures by altering angles between the main barrel and up brace, and a novel structure inspired by topology optimization, are promptly verified. All results indicate that the present approach can determine the fatigue damage for the tripod structure with precision and efficiency. 

A time-domain fatigue damage assessment approach for the tripod structure of offshore wind turbines

TG-SPSR: A Systematic Targeted Password Attacking Model

A New Frequency-Domain Method for Fatigue Prediction of Offshore Structures Under Wideband Random Loadings

Lightweight real-time stereo matching algorithm for AI chips

Password generation model based on generative adversarial network usually has the problem of high duplicate rate, which further leads to low cover rate. In this regard, we propose PGGAN model. It sets up an additional controller network which is similar to the discriminator in the aspect of structure and function. The discriminator and the controller respectively learn the measure between the distribution of generated password with the real password distribution and the uniform distribution, and then use two measures to teach generator meanwhile. By changing the activation function and loss function of the controller, different measure functions can be selected. The experimental results show that compared with GAN, our PGGAN performs better both in cover rate and duplicate rate. Moreover, Wasserstein distance usually has a better effect to the other measure in model. Specifically, PGGAN with Wasserstein distance can increase the cover rate by 3.57% and reduce the duplicate rate by 30.85% on rockyou dataset.

PGGAN: Improve Password Cover Rate Using the Controller

Speech enhancement tasks for audio with a low SNR are challenging. Existing speech enhancement methods are mainly designed for high SNR audio, and they usually use RNNs to model audio sequence features, which causes the model to be unable to learn long-distance dependencies, thus limiting its performance in low-SNR speech enhancement tasks. We design a complex transformer module with sparse attention to overcome this problem. Different from the traditional transformer model, this model is extended to effectively model complex domain sequences, using the sparse attention mask balance model’s attention to long-distance and nearby relations, introducing the pre-layer positional embedding module to enhance the model’s perception of position information, adding the channel attention module to enable the model to dynamically adjust the weight distribution between channels according to the input audio. The experimental results show that, in the low-SNR speech enhancement tests, our models have noticeable performance improvements in speech quality and intelligibility, respectively.

/pdf/cst-complex-sparse-transformer-for-low-snr-speech-23exxnbc.pdf

CST: Complex Sparse Transformer for Low-SNR Speech Enhancement

With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password attacks and password strength evaluation. In real attack scenarios, high-probability passwords are easy to enumerate; extremely low-probability passwords usually lack semantic structure and, so, are tough to crack by applying statistical laws in machine learning models, but these passwords with lower probability have a large search space and certain semantic information. Improving the low-probability password hit rate in this interval is of great significance for improving the efficiency of offline attacks. However, obtaining a low-probability password is difficult under the current password-generation model. To solve this problem, we propose a low-probability generator–probabilistic context-free grammar (LPG–PCFG) based on PCFG. LPG–PCFG directionally increases the probability of low-probability passwords in the models’ distribution, which is designed to obtain a degeneration distribution that is friendly for generating low-probability passwords. By using the control variable method to fine-tune the degeneration of LPG–PCFG, we obtained the optimal combination of degeneration parameters. Compared with the non-degeneration PCFG model, LPG–PCFG generates a larger number of hits. When generating 107 and 108 times, the number of hits to low-probability passwords increases by 50.4% and 42.0%, respectively.

/pdf/lpg-pcfg-an-improved-probabilistic-context-free-grammar-to-mh4zma1n.pdf

LPG–PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xiaozhou Guo

Author Tools

Chat about Author