Hasil untuk "Mathematics"

X. Ren, A. Shumakovitch

E. Fennema, J. Sherman

T. N. Carraher, David W. Carraher, A. Schliemann

N. Betz, G. Hackett

P. Martin-Löf

G. Hackett, N. Betz

A. Thompson

A. Bishop

K.P.E. Gravemeijer, H. Vonk, Roth Rainero

Paul Ernest

K. A. Jackson

Christopher T. Cross, Taniesha A. Woods, H. Schweingruber

J. Hyde, J. Mertz

Qing Li, Xin Ma

Shengbao Wang, Yixiao Wu, Kang Wen et al.

We conduct a cryptanalysis of the Vehicle-to-Infrastructure (V2I) handover authentication protocol newly developed by Son et al., which incorporates blockchain technology for authentication purposes. Although this approach is notably efficient, our analysis reveals that the protocol is vulnerable to vehicle impersonation attacks, traceability attacks, and trusted authority (TA) circumvention attacks. To address these security vulnerabilities, we propose an enhanced protocol integrating Schnorr signature-based authentication, dynamically refreshed temporary identities, and TA-anchored credential mechanisms. We validate its security through heuristic analysis and formal verification using ProVerif. Furthermore, a comprehensive comparison with various related schemes confirms that the new protocol achieves a higher level of security while simultaneously maintaining satisfactory efficiency in both the computational and communication aspects.

Uttam U. Deshpande, Supriya Shanbhag, Ramesh Koti et al.

Phone calls are strictly forbidden in certain locations due to the potential security threats. Mobile phones’ growing capabilities have also increased the risk of their misuse in places that are restricted, like manufacturing plants. Unauthorized mobile phone use in these environments can lead to significant safety hazards, operational disruptions, and security breaches. There is an urgent need to develop an intelligent system that can identify the presence of individuals as well as cellphone usage. We propose an advanced Artificial Intelligence and Computer Vision-based real-time cell phone detection system to detect mobile phone usage in restricted zones. Modern deep learning approaches, such as YOLOv8 for real-time object detection to accurately detect cell phone usage, are combined with dense layers of ResNet-50 to perform image classification tasks. We highlight the critical need for such detection systems in manufacturing settings and discuss the specific challenges encountered. To support this research, we have developed a custom dataset of 2,150 images, which features a diverse array of images with varying foreground and background elements to reflect real-world conditions. Our experimental results demonstrate that YOLOv8 achieves a Mean Average Precision (mAP50) of 49.5% at 0.5 IoU for cellphone detection tasks and an accuracy of 96.03% for prediction tasks. These findings underscore the effectiveness of our AI and CV-based system in detecting unauthorized mobile phone usage in restricted zones.

Elisa Atza, Rob Klooster, Falko Hofstra et al.

Abstract The vigor of potato plants is of crucial importance for potato seed producers, who are interested in predicting it at scale by exploiting the dependence of plant growth and development on the origin and physiological state of the seed tuber. In this article we present the results of a three-year long experiment in which we studied six potato varieties in three test fields. We identify a 73– $$90\%$$ overall correlation in the vigor of plants from the same seedlot grown in different test fields. Similarly, the biochemical tuber data produce plant vigor predictions that correlate up to 70– $$90\%$$ with the measurements. However, these relatively large data and prediction correlations are mostly due to the strong dependence of the seedlot vigor on the tuber genotype. For five out of six studied varieties, variety-specific cross-field and cross-year vigor predictions produce negligible or even negative correlations when the seed tubers and young plants experience environmental stress. At the same time, for the variety that appeared to be less sensitive to environmental stresses, we obtained cross-field and cross-year vigor predictions correlating up to $$80\%$$ with the measurements. Analysis of individual predictor variables, such as the abundance of a particular metabolite, indicates that the vigor-enhancing properties of the seed tubers are also variety-specific and that the FTIR spectroscopy data is the most reliable predictor.

Saritha Kodikara, Kim-Anh Lê Cao

Abstract Background The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal microbiome studies are becoming increasingly popular. These studies enable researchers to infer taxa associations towards the understanding of coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, are limited due to the data characteristics of microbiome data (sparse, compositional, multivariate). Several network inference methods have been proposed, but have been largely unexplored in a longitudinal setting. Results We introduce LUPINE (LongitUdinal modelling with Partial least squares regression for NEtwork inference), a novel approach that leverages on conditional independence and low-dimensional data representation. This method is specifically designed to handle scenarios with small sample sizes and small number of time points. LUPINE is the first method of its kind to infer microbial networks across time, while considering information from all past time points and is thus able to capture dynamic microbial interactions that evolve over time. We validate LUPINE and its variant, LUPINE_single (for single time point analysis) in simulated data and four case studies, where we highlight LUPINE’s ability to identify relevant taxa in each study context, across different experimental designs (mouse and human studies, with or without interventions, and short or long time courses). To detect changes in the networks across time and groups or in response to external disturbances, we used different metrics to compare the inferred networks. Conclusions LUPINE is a simple yet innovative network inference methodology that is suitable for, but not limited to, analysing longitudinal microbiome data. The R code and data are publicly available for readers interested in applying these new methods to their studies. Video Abstract

Urslla Uchechi Izuazu, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim et al.

Abstract Deep learning-based intrusion detection systems (DL-IDS) have proven effective in detecting cyber threats. However, their vulnerability to adversarial attacks and environmental noise, particularly in industrial settings, limits practical application. Current IDS models often assume ideal conditions, overlooking noise and adversarial manipulations, leading to degraded performance when deployed in real-world environments. Additionally, the black-box nature of DL model complicates decision-making, especially in industrial control systems (ICS) network, where understanding model behavior is crucial. This paper introduces the eXplainable Cyber-Threat Detection Framework (XC-TDF), a novel solution designed to overcome these challenges. XC-TDF enhances robustness against noise and adversarial attacks using regularization and adversarial training respectively, and also improves transparency through an eXplainable Artificial Intelligence (XAI) module. Simulation results demonstrate its effectiveness, showing resilience to perturbation by achieving commendable accuracy of 100% and 99.4% on the Wustl-IIoT2021 and Edge-IIoT datasets, respectively.

A. Frenzel, T. Goetz, R. Pekrun et al.