247 research outputs found
A framework for proving the correctness of cryptographic protocol properties by linear temporal logic
In this paper, a framework for cryptographic protocol analysis using linear temporal logic is proposed. The framework can be used to specify and analyse security protocols. It aims to investigate and analyse the security protocols properties that are secure or have any flaws. The framework extends the linear temporal logic by including the knowledge of participants in each status that may change over the time. It includes two main parts, the Language of Temporal Logic (LTL) and the domain knowledge. The ability of the framework is demonstrated by analysing the Needham-Schroeder public key protocol and the Andrew Secure RPC protocol as examples
Trust-based Approaches Towards Enhancing IoT Security: A Systematic Literature Review
The continuous rise in the adoption of emerging technologies such as Internet
of Things (IoT) by businesses has brought unprecedented opportunities for
innovation and growth. However, due to the distinct characteristics of these
emerging IoT technologies like real-time data processing, Self-configuration,
interoperability, and scalability, they have also introduced some unique
cybersecurity challenges, such as malware attacks, advanced persistent threats
(APTs), DoS /DDoS (Denial of Service & Distributed Denial of Service attacks)
and insider threats. As a result of these challenges, there is an increased
need for improved cybersecurity approaches and efficient management solutions
to ensure the privacy and security of communication within IoT networks. One
proposed security approach is the utilization of trust-based systems and is the
focus of this study. This research paper presents a systematic literature
review on the Trust-based cybersecurity security approaches for IoT. A total of
23 articles were identified that satisfy the review criteria. We highlighted
the common trust-based mitigation techniques in existence for dealing with
these threats and grouped them into three major categories, namely:
Observation-Based, Knowledge-Based & Cluster-Based systems. Finally, several
open issues were highlighted, and future research directions presented.Comment: 20 Pages, Conferenc
Scene Matters: Model-based Deep Video Compression
Video compression has always been a popular research area, where many
traditional and deep video compression methods have been proposed. These
methods typically rely on signal prediction theory to enhance compression
performance by designing high efficient intra and inter prediction strategies
and compressing video frames one by one. In this paper, we propose a novel
model-based video compression (MVC) framework that regards scenes as the
fundamental units for video sequences. Our proposed MVC directly models the
intensity variation of the entire video sequence in one scene, seeking
non-redundant representations instead of reducing redundancy through
spatio-temporal predictions. To achieve this, we employ implicit neural
representation as our basic modeling architecture. To improve the efficiency of
video modeling, we first propose context-related spatial positional embedding
and frequency domain supervision in spatial context enhancement. For temporal
correlation capturing, we design the scene flow constrain mechanism and
temporal contrastive loss. Extensive experimental results demonstrate that our
method achieves up to a 20\% bitrate reduction compared to the latest video
coding standard H.266 and is more efficient in decoding than existing video
coding strategies
Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion
Following the performance breakthrough of denoising networks, improvements
have come chiefly through novel architecture designs and increased depth. While
novel denoising networks were designed for real images coming from different
distributions, or for specific applications, comparatively small improvement
was achieved on Gaussian denoising. The denoising solutions suffer from
epistemic uncertainty that can limit further advancements. This uncertainty is
traditionally mitigated through different ensemble approaches. However, such
ensembles are prohibitively costly with deep networks, which are already large
in size.
Our work focuses on pushing the performance limits of state-of-the-art
methods on Gaussian denoising. We propose a model-agnostic approach for
reducing epistemic uncertainty while using only a single pretrained network. We
achieve this by tapping into the epistemic uncertainty through augmented and
frequency-manipulated images to obtain denoised images with varying error. We
propose an ensemble method with two decoupled attention paths, over the pixel
domain and over that of our different manipulations, to learn the final fusion.
Our results significantly improve over the state-of-the-art baselines and
across varying noise levels.Comment: Code and models are publicly available on https://github.com/IVRL/DE
Proving Secure Properties of Cryptographic Protocols with Knowledge Based Approach
Cryptographic protocols have been widely used to protect communications over insecure network environments. Existing cryptographic protocols usually contain flaws. To analyze these protocols and find potential flaws in them, the secure properties of them need be studied in depth. This paper attempts to provide a new framework to analyze and prove the secure properties in these protocols. A number of predicates and action functions are used to model the network communication environment. Domain rules are given to describe the transitions of principals\u27 knowledge and belief states. An example of public key authentication protocols has been studied and analysed
Ameliorative Effect and Underlying Mechanisms of Total Triterpenoids from Psidium guajava Linn (Myrtaceae) Leaf on High-Fat Streptozotocin-induced Diabetic Peripheral Neuropathy in Rats
Purpose: To investigate whether the total triterpenoids extracted from Psidium Guajava leaves (TTPGL) attenuate the development of diabetic peripheral neuropathy in rats by regulating the NF-κB pathway of the inflammatory process and its signaling mediators.Methods: All the Sprague Dawley rats used were maintained in a clean environment on a 12 h light/12h dark cycle. High-fat feeding and intraperitoneal injection of 40 mg/kg streptozotocin (STZ) were used to induce diabetes in the rats. The rats were randomly divided into 5 groups: diabetic mellitus (DM) group; TTPGL - 30 group, TTPGL - 60 group and TTPGL - 120 group treated by intragastric administration (i.g) with 30, 100 and 120 mg/kg/day TTPGL, respectively. The well-established drug, rosiglitazone (RSG, 3 mg/k/d, i.g.), was used as positive control. Normal rats served as control group. Nerve conduction velocity and sensitive tests were measured on weeks 1, 4 and 8. After 8 weeks administration, expression of pro-inflammatory molecules (TNF - α, IL - 6 and iNOS) and tissue proteins (Akt, IKKα, and NF – κB - p65) were evaluated to assess biochemical changes.Results: Compared to DM group, TTPGL (especially 120 mg / kg dose) treatment improved (p < 0.05) physical functions and provided neuronal protection in high - fat/streptozotocin - induced peripheral neuropathy rats. We found that the expressions of several pro - inflammatory factors such as tumor necrosis factor - α (TNF - α), IL - 6 and inducible nitric oxide synthase (iNOS) were highly suppressed (p < 0.05 or p < 0.01) by TTPGL in sciatic nerve. Mechanism analysis indicated that the ameliorative effect of TTPGL, in part, is through suppression of the expression of pro - inflammatory cytokines by NF - κB pathway mediation.Conclusion: TTPGL offers a potential therapeutic approach for the treatment of diabetic peripheral neuropathy.Keywords: Triterpenoids, Psidium Guajava, Diabetic peripheral neuropathy, Pro inflammatory cytokines, NF-κB pathwa
Comparative Transcriptome Analysis Between Resistant and Susceptible Rice Cultivars Responding to Striped Stem Borer (SSB), Chilo suppressalis (Walker) Infestation
The striped stem borer, Chilo suppressalis (Walker), is a notorious pest of rice that causes large losses in China. Breeding and screening of resistance rice cultivars are effective strategies for C. suppressalis management. In this study, insect-resistant traits of 47 rice cultivars were investigated by C. suppressalis artificial infestation (AI) both in field and greenhouse experiments, using the susceptible (S) cultivar 1665 as a control. Results suggest that two rice cultivars, namely 1688 and 1654, are resistant (R) and moderately resistant (MR) to C. suppressalis, respectively. Then, a comparative transcriptome (RNA-Seq) was de novo assembled and differentially expressed genes (DEGs) with altered expression levels were investigated among cultivars 1688, 1654, and 1665, with or without C. suppressalis infestation for 24 h. A total of 2569 and 1861 genes were up-regulated, and 3852 and 1861 genes were down-regulated in cultivars 1688 and 1654, respectively after artificial infestation with C. suppressalis compared to the non-infested control (CK). For the susceptible cultivar 1665, a total of 882 genes were up-regulated and 3863 genes were down-regulated after artificial infestation with C. suppressalis compared to the CK. Twenty four DEGs belong to proteinase inhibitor, lectin and chitinase gene families; plant hormone signal transduction and plant-pathogen interaction pathways were selected as candidate genes to test their possible role in C. suppressalis resistance. RT-qPCR results revealed that 13 genes were significantly up-regulated and 8 were significantly down-regulated in the resistant cultivar 1688 with C. suppressalis artificial infestation (1688AI) compared to the CK. Three genes, LTPL164, LTPL151, and LOC Os11g32100, showed more than a 10-fold higher expression in 1688AI than in 1688CK, suggesting their potential role in insect resistance. Overall, our results provide an important foundation for further understanding the insect resistance mechanisms of selected resistant varieties that will help us to breed C. suppressalis resistant rice varieties
Ferromagnetic and insulating behavior in both half magnetic levitation and non-levitation LK-99 like samples
Finding materials exhibiting superconductivity at room temperature has long
been one of the ultimate goals in physics and material science. Recently,
room-temperature superconducting properties have been claimed in a copper
substituted lead phosphate apatite (PbCu(PO)O, or called
LK-99) [1-3]. Using a similar approach, we have prepared LK-99 like samples and
confirmed the half-levitation behaviors in some small specimens under the
influence of a magnet at room temperature. To examine the magnetic properties
of our samples, we have performed systematic magnetization measurements on the
as-grown LK-99-like samples, including the half-levitated and non-levitated
samples. The magnetization measurements show the coexistence of
soft-ferromagnetic and diamagnetic signals in both half-levitated and
non-levitated samples. The electrical transport measurements on the as-grown
LK-99-like samples including both half-levitated and non-levitated samples show
an insulating behavior characterized by the increasing resistivity with the
decreasing temperature
Distinct interactions between fronto-parietal and default mode networks in impaired consciousness
Existing evidence suggests that the default-mode network (DMN) and fronto-pariatal network (FPN) play an important role in altered states of consciousness. However, the brain mechanisms underlying impaired consciousness and the specific network interactions involved are not well understood. We studied the topological properties of brain functional networks using resting-state functional MRI data acquired from 18 patients (11 vegetative state/unresponsive wakefulness syndrome, VS/UWS, and 7 minimally conscious state, MCS) and compared these properties with those of healthy controls. We identified that the topological properties in DMN and FPN are anti-correlated which comes, in part, from the contribution of interactions between dorsolateral prefrontal cortex of the FPN and precuneus of the DMN. Notably, altered nodal connectivity strength was distance-dependent, with most disruptions appearing in long-distance connections within the FPN but in short-distance connections within the DMN. A multivariate pattern-classification analysis revealed that combination of topological patterns between the FPN and DMN could predict conscious state more effectively than connectivity within either network. Taken together, our results imply distinct interactions between the FPN and DMN, which may mediate conscious state
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
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