Abstract

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Abstract

The rise of smart cities and the increasing demand for drones has sparked considerable interest in the Internet of Drones (IoD) within the realms of academia and industry. IoD presents a multitude of advantages in smart city settings, facilitating services like traffic monitoring, environmental surveillance, and disaster management by harnessing the potential of IoT and Flying Ad-Hoc Networks (FANET) infrastructures. However, the transmission of sensitive messages between drones in IoD-based smart cities is disseminated over insecure channels, leaving them exposed to security vulnerabilities. Furthermore, drones operating in IoD architectures are prone to physical capture attacks as they operate in unattended environments with minimal human intervention. Moreover, the limited resources of drones pose challenges to the practicality of employing computationally intensive cryptographic methods. In response to these challenges, we introduce PAF-IoD, an authentication framework that prioritizes security and efficiency. PAF-IoD leverages physical unclonable functions (PUFs) and the AEGIS authenticated encryption scheme to guarantee trustworthy and secure communication between users and drones in smart cities. In terms of security validation, we perform both random and real model-based formal analyses. Furthermore, we employ the Scyther tool to ensure the resilience of PAF-IoD against different security vulnerabilities. Additionally, an informal analysis is conducted to demonstrate the resilience of PAF-IoD against various attacks. By introducing PAF-IoD, we offer a secure solution that addresses vulnerabilities and resource limitations associated with drone communication. The proposed framework guarantees the integrity and confidentiality of data while optimizing computational and communication resources, thereby enabling reliable and effective IoD operations in smart cities.

Abstract

—Smart Microfactories use additive manufacturing to create products with mixed materials and variable sizes. Digital twin technology enhances control of the additive manufacturing equipment in these factories, increasing productivity and minimizing errors. The digital twins communicate with the machines to furnish sensitive data and instructions, which must be protected from tampering. Authentication rescues the digital and physical twins from menacing attacks such as privileged insider, impersonation, Ephemeral Secret Leakage (ESL) and Man-in-The-Middle (MiTM) attacks. To this end, we propose lightweight authentication among the digital and physical twins with the undeniability of issued commands and deniable key agreement. It achieves perfect forward secrecy, unforgeability and anonymity. Rigorous formal verification with AVISPA and informal security analysis show the robustness of this protocol to the aforesaid attacks.

Abstract

In this paper, we propose a network whose nodes are labeled by the composite numbers and two nodes are connected by an undirected link if they are relatively prime to each other. As the size of the network increases, the network will be connected whenever the largest possible node index n ≥ 49. To investigate how the nodes are connected, we analytically describe that the link density saturates to 6/π2 , whereas the average degree increases linearly with slope 6/π2 with the size of the network. To investigate how the neighbors of the nodes are connected to each other, we find the shortest path length will be at most 3 for 49 ≤ n ≤ 288 and it is at most 2 for n ≥ 289. We also derive an analytic expression for the local clustering coefficients of the nodes, which quantifies how close the neighbors of a node to form a triangle. We also provide an expression for the number of r-length labeled cycles, which indicates the existence of a cycle of length at most O(log n). Finally, we show that this graph sequence is actually a sequence of weakly pseudo-random graphs. We numerically verify our observed analytical results. As a possible application, we have observed less synchronizability (the ratio of the largest and smallest positive eigenvalue of the Laplacian matrix is high) as compared to Erdős–Rényi random network and Barabási–Albert network. This unusual observation is consistent with the prolonged transient behaviors of ecological and predator–prey networks which can easily avoid the global synchronization.

Abstract

This research explores the foundational frameworks and conceptual arguments shaping environmental litigation in the Supreme Court of India. Our central aim was to systematically analyze the legal language used to construct environmental arguments within the court. We sought to identify the recurring frameworks employed and understand how these frameworks integrate fundamental concepts like the right to life and freedom with environmental concerns.

Abstract

Graph neural networks (GNNs) are increasingly being used on sensitive graph-structured data, necessitating techniques for handling unlearning requests on the trained models, particularly node unlearning. However, unlearning nodes on GNNs is challenging due to the interdependence between the nodes in a graph. We compare MEGU, a state-of-the-art graph unlearning method, and SCRUB, a general unlearning method for classification, to investigate the efficacy of graph unlearning methods over traditional unlearning methods. Surprisingly, we find that SCRUB performs comparably or better than MEGU on random node removal and on removing an adversarial node injection attack. Our results suggest that 1) graph unlearning studies should incorporate general unlearning methods like SCRUB as baselines, and 2) there is a need for more rigorous behavioral evaluations that reveal the differential advantages of proposed graph unlearning methods. Our work, therefore, motivates future research into more comprehensive evaluations for assessing the true utility of graph unlearning algorithms.

Abstract

Charts are tools for data communication used in a wide range of documents. Recently, the pattern recognition community has shown interest in developing methods for automatically processing charts found in the wild. Following previous efforts on ICPR’s CHART-Infographics competitions, here we propose a newer, larger dataset and benchmark for analyzing and recognizing charts. Inspired by the steps required to make sense of a chart image, the benchmark is divided into 7 different tasks: chart image classification, chart text detection and recognition, text role classification, axis analysis, legend analysis, data extraction, and end-toend data extraction. We also show the performance of different baselines for the first five tasks. We expect that the increased scale of the proposed dataset will enable the development of better chart recognition systems.

Abstract

We present CheXtriev, a graph-based, anatomy-aware framework for chest radiograph retrieval. Unlike prior methods focussed on global features, our method leverages graph transformers to extract informative features from specific anatomical regions. Furthermore, it captures spatial context and the interplay between anatomical location and findings. This contextualization, grounded in evidence-based anatomy, results in a richer anatomy-aware representation and leads to more accurate, effective and efficient retrieval, particularly for less prevalent findings. CheXtriv outperforms state-of-the-art global and local approaches by to in retrieval accuracy and to in ranking quality.

Abstract

Biological processes like growth, aging, and disease progression are generally studied with follow-up scans taken at different time points, i.e., image time series (TS) based analysis. Image TS represents the evolution of anatomy over time, but different anatomies may have different structural characteristics and temporal paths. Therefore, separating the time-dependent path difference and time-independent basic anatomy/shape changes is important when comparing two image TS to understand the causes of the observed differences better. A method to untangle and quantify the path and shape difference between the TS is presented in this paper. The proposed method is evaluated with simulated and adult and fetal neuro templates. Results show that the metric can separate and quantify the path and shape differences between TS.

Abstract

This work proposes a resource efficient robust control scheme for missile-target engagement scenarios subjected to external disturbances. The robustness is achieved by using an annular event-triggered artificial time-delayed control (ET-TDC) methodology with input saturation. The ET-TDC philosophy uses the TDC strategy through a dynamic predefined triggering mechanism which overcomes the requirement to update the control periodically for every sampling instant, unlike conventional TDC and other robust control schemes. Thus the proposed methodology can tackle uncertainties with minimal a-priori knowledge while significantly reducing the over-utilisation of system resources. In addition, the adopted event-triggered mechanism facilitates further conservation of energy which might be crucial for mid-to-long range engagement scenarios. The closed-loop stability is derived analytically and the simulation results illustrate the efficacy of the proposed guidance framework in comparison with other state-of-art robust control methodologies.