Iskanje po katalogu digitalne knjižnice

Opis: While vehicle ad-hoc networks (VANETs) have many advantages, they also present privacy and security concerns. Certificate management issue has been seen in traditional public key infrastructure based privacy preserving authentication schemes while key escrow problem exists in identity based privacy preserving authentication techniques. Also existing cryptographic techniques rely heavily on assumptions about tamper-proof equipment to ensure their security. A proposal has been made for a certificateless aggregate signature system for VANETs addressing these issue and was proved provably unforgeable against collusion attack. However we found that the proposed technique was insecure and cannot withstand collusion assault. Therefore, this paper presents an improved and secure certificateless aggregate signature technique for VANETs. We also illustrate the security & performance evaluation of our presented technique and based upon the hardness assumption of the elliptic curve discrete logarithm problem we have shown that the technique is safe against existential forgery on adaptive chosen message attack in the random oracle model. Also the presented technique has better efficiency compared to some recent existing authentication techniques.
Ključne besede: aggregate signature, certificateless public key cryptography, elliptic curve cryptosystem, vehicular ad hoc networks
Objavljeno v DKUM: 13.03.2025; Ogledov: 0; Prenosov: 1
Celotno besedilo (888,32 KB)

Opis: This paper introduces a novel framework for augmenting explainability in the interpretation of point cloud data by fusing expert knowledge with counterfactual reasoning. Given the complexity and voluminous nature of point cloud datasets, derived predominantly from LiDAR and 3D scanning technologies, achieving interpretability remains a significant challenge, particularly in smart cities, smart agriculture, and smart forestry. This research posits that integrating expert knowledge with counterfactual explanations – speculative scenarios illustrating how altering input data points could lead to different outcomes – can significantly reduce the opacity of deep learning models processing point cloud data. The proposed optimization-driven framework utilizes expert-informed ad-hoc perturbation techniques to generate meaningful counterfactual scenarios when employing state-of-the-art deep learning architectures. The optimization process minimizes a multi-criteria objective comprising counterfactual metrics such as similarity, validity, and sparsity, which are specifically tailored for point cloud datasets. These metrics provide a quantitative lens for evaluating the interpretability of the counterfactuals. Furthermore, the proposed framework allows for the definition of explicit interpretable counterfactual perturbations at its core, thereby involving the audience of the model in the counterfactual generation pipeline and ultimately, improving their overall trust in the process. Results demonstrate a notable improvement in both the interpretability of the model’s decisions and the actionable insights delivered to end-users. Additionally, the study explores the role of counterfactual reasoning, coupled with expert input, in enhancing trustworthiness and enabling human-in-the-loop decision-making processes. By bridging the gap between complex data interpretations and user comprehension, this research advances the field of explainable AI, contributing to the development of transparent, accountable, and human-centered artificial intelligence systems.
Ključne besede: explainable AI, point cloud data, counterfactual reasoning, information fusion, interpretability, human-centered AI
Objavljeno v DKUM: 06.03.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (186,97 KB)

Ključne besede: data mining, sports training, automatic generation of sports trainig, evolutionary algorithms
Objavljeno v DKUM: 28.02.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (2,54 MB)

Opis: A new approach using a horizontally layered analytical soil model for inhomogeneous soil is presented. The presented approach also considers deeper soil layers, which is not the case when simply dividing the area of interest into smaller subareas. The finite element method model was used to prepare test data because, in such a case, the soil parameters are known. Six lines simulating Wenner’s method were used, and their results were combined appropriately to determine the soil parameters of nine subareas. To determine the soil parameters in the scope of each subarea, different optimization methods were used and compared to each other. The results were analyzed, and Artificial Bee Colony was selected as the most appropriate method among those tested. Additionally, the convergence of the methods was analyzed, and Memory Assistance is presented, with the aim of shortening the calculation time. In this study, three-, four-, five-, and six-layered soil models were tested, and it is concluded that the three-layered model is most appropriate. A finite element method model based on the soil determination results was constructed to verify the results. The results of the Wenner’s method simulation in the cases of the test data and final model were compared to confirm the accuracy of the presented method
Ključne besede: grounding system, soil model, finite element method, differential evolution, artificial bee colony, teaching–learning-based optimization
Objavljeno v DKUM: 21.02.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (6,81 MB)

Ključne besede: property graph, geographical maps, smart sports training, data mining, data fusion
Objavljeno v DKUM: 12.02.2025; Ogledov: 0; Prenosov: 1
Celotno besedilo (6,85 MB)
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