155 research outputs found
Improving the Eco-system of Passwords
Password-based authentication is perhaps the most widely used method for user authentication. Passwords are both easy to understand and use, and easy to implement. With these advantages, password-based authentication is likely to stay as an important part of security in the foreseeable future. One major weakness of password-based authentication is that many users tend to choose weak passwords that are easy to guess. In this dissertation, we address the challenge and improve the eco-system of passwords in multiple aspects. Firstly, we provide methodologies that help password research. To be more specific, we propose Probability Threshold Graphs, which is superior to Guess Number Graphs when comparing password models and password datasets. We also introduce rich literature of statistical language modeling into password modeling and show that if used correctly, whole-string Markov models outperform Probabilistic Context Free Grammar models. Secondly, we improve password policies and practice used by websites by studying how to best check weak passwords. We model different password strength checking methods as Password Ranking Algorithms (PRAs), and introduce two methods for comparing different PRAs: the ÎČ-Residual Strength Graph and the Normalized ÎČ-Residual Strength Graph. Finally, we examine the security and usability of commonly suggested password generation strategies. We find that for mnemonic sentence-based strategies, differences in the exact instructions have a tremendous impact on the security level of the resulting passwords. For word-based strategies, security of the resulting passwords mainly depends on the number of words required, and requiring at least 3 words is likely to result in passwords stronger than the general passwords chosen by typical users
Using Context-Based Password Strength Meter to Nudge Users' Password Generating Behavior: A Randomized Experiment
Encouraging users to create stronger passwords is one of the key issues in password-based authentication. It is particularly important as prior works have highlighted that most passwords are weak. Yet, passwords are still the most commonly used authentication method. This paper seeks to mitigate the issue of weak passwords by proposing a context-based password strength meter. We conduct a randomized experiment on Amazon MTurk and observe the change in usersâ behavior. The results show that our proposed method is significantly effective. Users exposed to our password strength meter are more likely to change their passwords after seeing the warning message, and those new passwords are stronger. Furthermore, users are willing to invest their time to learn about creating a stronger password, even in a traditional password strength meter setting. Our findings suggest that simply incorporating contextual information to password strength meters could be an effective method in promoting more secure behaviors among end users
Using Context-Based Password Strength Meter to Nudge Users\u27 Password Generating Behavior: A Randomized Experiment
Encouraging users to create stronger passwords is one of the key issues in password-based authentication. It is particularly important as prior works have highlighted that most passwords are weak. Yet, passwords are still the most commonly used authentication method. This paper seeks to mitigate the issue of weak passwords by proposing a context-based password strength meter. We conduct a randomized experiment on Amazon MTurk and observe the change in usersâ behavior. The results show that our proposed method is significantly effective. Users exposed to our password strength meter are more likely to change their passwords after seeing the warning message, and those new passwords are stronger. Furthermore, users are willing to invest their time to learn about creating a stronger password, even in a traditional password strength meter setting. Our findings suggest that simply incorporating contextual information to password strength meters could be an effective method in promoting more secure behaviors among end users
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation
Due to the abundant neurophysiological information in the
electroencephalogram (EEG) signal, EEG signals integrated with deep learning
methods have gained substantial traction across numerous real-world tasks.
However, the development of supervised learning methods based on EEG signals
has been hindered by the high cost and significant label discrepancies to
manually label large-scale EEG datasets. Self-supervised frameworks are adopted
in vision and language fields to solve this issue, but the lack of EEG-specific
theoretical foundations hampers their applicability across various tasks. To
solve these challenges, this paper proposes a knowledge-driven cross-view
contrastive learning framework (KDC2), which integrates neurological theory to
extract effective representations from EEG with limited labels. The KDC2 method
creates scalp and neural views of EEG signals, simulating the internal and
external representation of brain activity. Sequentially, inter-view and
cross-view contrastive learning pipelines in combination with various
augmentation methods are applied to capture neural features from different
views. By modeling prior neural knowledge based on homologous neural
information consistency theory, the proposed method extracts invariant and
complementary neural knowledge to generate combined representations.
Experimental results on different downstream tasks demonstrate that our method
outperforms state-of-the-art methods, highlighting the superior generalization
of neural knowledge-supported EEG representations across various brain tasks.Comment: 14pages,7 figure
Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts
Zero-shot Visual Question Answering (VQA) is a prominent vision-language task
that examines both the visual and textual understanding capability of systems
in the absence of training data. Recently, by converting the images into
captions, information across multi-modalities is bridged and Large Language
Models (LLMs) can apply their strong zero-shot generalization capability to
unseen questions. To design ideal prompts for solving VQA via LLMs, several
studies have explored different strategies to select or generate
question-answer pairs as the exemplar prompts, which guide LLMs to answer the
current questions effectively. However, they totally ignore the role of
question prompts. The original questions in VQA tasks usually encounter
ellipses and ambiguity which require intermediate reasoning. To this end, we
present Reasoning Question Prompts for VQA tasks, which can further activate
the potential of LLMs in zero-shot scenarios. Specifically, for each question,
we first generate self-contained questions as reasoning question prompts via an
unsupervised question edition module considering sentence fluency, semantic
integrity and syntactic invariance. Each reasoning question prompt clearly
indicates the intent of the original question. This results in a set of
candidate answers. Then, the candidate answers associated with their confidence
scores acting as answer heuristics are fed into LLMs and produce the final
answer. We evaluate reasoning question prompts on three VQA challenges,
experimental results demonstrate that they can significantly improve the
results of LLMs on zero-shot setting and outperform existing state-of-the-art
zero-shot methods on three out of four data sets. Our source code is publicly
released at \url{https://github.com/ECNU-DASE-NLP/RQP}
3D Textured Shape Recovery with Learned Geometric Priors
3D textured shape recovery from partial scans is crucial for many real-world
applications. Existing approaches have demonstrated the efficacy of implicit
function representation, but they suffer from partial inputs with severe
occlusions and varying object types, which greatly hinders their application
value in the real world. This technical report presents our approach to address
these limitations by incorporating learned geometric priors. To this end, we
generate a SMPL model from learned pose prediction and fuse it into the partial
input to add prior knowledge of human bodies. We also propose a novel
completeness-aware bounding box adaptation for handling different levels of
scales and partialness of partial scans.Comment: 5 pages, 3 figures, 2 table
Effect of the heating rate on the thermal explosion behavior and oxidation resistance of 3D-structure porous NiAl intermetallic
Porous NiAl intermetallic compounds demonstrate great potential in various applications by their high porosity and excellent oxidation resistance. However, to obtain a controllable NiAl intermetallic structure by tuning different process parameters remains unclear. In this work, porous NiAl intermetallic compounds were fabricated by economic and energy-saving thermal explosion (TE) reaction. The relationship between microstructure and process parameters was revealed using three-dimensional X-ray microscopy (3D-XRM) with high resolution and non-destructive characteristics. The geometrical features and quantitative statistics of the porous NiAl obtained at different heating rates (2, 10, 20 \ub0C minâ1) were compared. The result of the closed porosity calculation showed that a lower heating rate (2 \ub0C minâ1) promoted the Kirkendall reaction between Ni and Al, resulting in a high closed porosity (5.25%). However, at a higher heating rate (20 \ub0C minâ1), a homogeneous NiAl phase was observed using the threshold segmentation method, indicating uniform and complete TE reaction can be achieved at a high heating rate. The result of the 3D fluid simulation showed that the sample heated at 10 \ub0C minâ1 had the highest permeability (2434.6 md). In this study, we systematically investigated the relationship between the heating rates and properties of the porous NiAl intermetallic, including the phase composition, porosity, exothermic mechanism, oxidation resistance, and compression resistance. Our work provides constructive directions for designing and tailoring the performance of porous NiAl intermetallic compounds
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