Abstract

Modern autonomous vehicles (AVs) often rely on vision, LIDAR, and even radar-based simultaneous localization and mapping (SLAM) frameworks for precise localization and navigation. However, modern SLAM frameworks often lead to unacceptably high levels of drift (i.e., localization error) when AVs observe few visually distinct features or encounter occlusions due to dynamic obstacles. This paper argues that minimizing drift must be a key desiderata in AV motion planning, which requires an AV to take active control decisions to move towards feature-rich regions while also minimizing conventional control cost. To do so, we first introduce a novel data-driven perception module that observes LIDAR point clouds and estimates which features/regions an AV must navigate towards for drift minimization. Then, we introduce an interpretable model predictive controller (MPC) that moves an AV toward such feature-rich regions while avoiding visual occlusions and gracefully trading off drift and control cost. Our experiments on challenging, dynamic scenarios in the state-ofthe-art CARLA simulator indicate our method redu

Abstract

The communication in Intelligent Transportation Systems (ITS) suffers from various security and privacy related issues as several attacks, like replay, man-in-the-middle (MiTM), impersonation, data leakage and unauthorised data update attacks can be mounted by an adversary. Therefore, we need a robust security mechanism to sort out such issues, which can be further enhanced through the use of mechanism of blockchain technology. In this paper, we propose a blockchain-based secure data exchange and storage framework for the ITS (in short, we call it as BDESF-ITS). We conduct the security analysis of the BDESF-ITS to prove its robustness against various possible attacks. The practical implementation of the BDESF-ITS is also carried out to observe its behaviour with the real world settings.

Abstract

With the widespread use of Internet of Things (IoT) in various applications and several security vulnerabilities reported in them, the security requirements have become an integral part of an IoT system. Authentication and access control are the two principal security requirements for ensuring authorized and restricted accesses to limited and essential resources in IoT. The built-in authentication mechanism in IoT devices is not reliable, because several security vulnerabilities are revealed in the firmware implementation of authentication protocols in IoT. On the other hand, the current authentication approaches for IoT that are not firmware are vulnerable to some security attacks prevalent in IoT. Moreover, the recent access control approaches for IoT have limitations in context-awareness, scalability, interoperability, and security. To mitigate these limitations, there is a need for a robust authentication and access control …

Abstract

Ransomware is a type of malicious program or soft- ware that encrypts the contents on a hard disc and prevents the users from accessing them unless they pay an amount (called a ransom). Most of the organizations, such as financial institutes and healthcare sectors (i.e., smart healthcare) are targeted by ransomware attacks. Ransomware assaults are among the most frightening types of cyber-attacks, and they are not confined to a specific sector or the countries. Blockchain is a tamper-proof tech- nology, which is more secure, robust and decentralized in nature. Features of blockchain can add more security for detection and mitigation of ransomware more effectively. In this paper, we pro- pose a new blockchain-enabled security framework to detect and defend the ransomware attacks for smart healthcare (in short, BSFR-SH). The conducted security analysis proves the security of the proposed BSFR-SH against the ransomware attacks. The performance of BSFR-SH is significantly better than the other similar existing mechanisms as it achieves better accuracy and F1-score than other compared mechanisms. Furthermore, the practical demonstration of BSFR-SH is provided to estimate the impact on important performance parameters. Index Terms—Ransomware, smart healthcare, intrusion detec- tion, machine learning, blockchain

Abstract

In this article, we undertake a detailed study of the limiting behavior of a three-state discrete-time quantum walk on a one-dimensional lattice with generalized Grover coins. Two limit theorems are proved and consequently we show that the quantum walk exhibits localization at its initial position for a wide range of coin parameters. Finally, we discuss the effect of the coin parameters on the peak velocities of probability distributions of the underlying quantum walks.

Abstract

The problem of entanglement detection is a long standing problem in quantum information theory. One of the primary procedures of detecting entanglement is to find the suitable positive but noncompletely positive maps. Here we try to give a generic prescription to construct a positive map that can be useful for such scenarios. We study a class of positive maps arising from Lindblad structures. We show that two famous positive maps viz. transposition and Choi map can be obtained as a special case of a class of positive maps having Lindblad structure. Generalizing the transposition map to a one parameter family we have used it to detect genuine multipartite entanglement. Finally being motivated by the negativity of entanglement, we have defined a similar measure for genuine multipartite entanglement.

Abstract

In this article we derive the exact dynamics of a two-qubit (spin 1/2) system interacting centrally with separate spin baths composed of qubits in a thermal state. Furthermore, each spin of the bath is coupled to every other spin of the same bath. The corresponding dynamical map is constructed. It is used to analyze the non-Markovian nature of the two qubit central spin dynamics. We further observe the evolution of quantum correlations like entanglement and discord under the influence of the environmental interaction. Moreover, we demonstrate the comparison between this exact two-qubit dynamics and the locally acting central spin model in a spin bath. This work is a stepping stone towards the realization of non-Markovian heat engines and other quantum thermal devices.

Abstract

In this article we derive the exact dynamics of a two qubit (spin 1/2) system interacting centrally with separate fermionic baths composed of qubits in thermal state. Further, each spin of a bath is coupled to every other spin of the same bath. The corresponding dynamical map is constructed. It is used to analyse the non-Markovian nature of the two qubit central spin dynamics. We further observe the evolution of quantum correlations like entanglement and discord under the influence of the environmental interaction. Moreover, we demonstrate the comparison between this exact two qubit dynamics and the locally acting fermionic central spin model. This work is a stepping stone towards the realization of non-Markovian heat engines and other quantum thermal devices.

Abstract

The design and implementation of parallel algorithms for dynamic graph problems is attracting significant research attention in the recent years, driven by numerous applications to social network analysis, neuroscience, and protein interaction networks. One such problem is the computation of PageRank values of vertices in a directed graph. This paper presents two new parallel algorithms for recomputing the PageRank values of vertices in a dynamic graph. Our techniques require the recomputation of the PageRank of only the vertices affected by the insertion/deletion of a batch of edges. We conduct detailed experimental studies of our algorithm on a set of 11 realworld graphs. Our results on Intel Xeon Silver 4116 CPU and NVIDIA Tesla V100 PCIe 16GB GPU indicate that our algorithms outperform static and dynamic update algorithms by 6.1×and 8.6×on the CPU, and by 9.8×and 9.3×on the GPU respectively. We also compare the performance of the algorithms in batched mode to cumulative single-edge updates.

Abstract

Finding the biconnected components of a graph has a large number of applications in many other graph problems including planarity testing, computing the centrality metrics, finding the (weighted) vertex cover, coloring, and the like. Recent years saw the design of efficient algorithms for this problem across sequential and parallel computational models. However, current algorithms do not work in the setting where the underlying graph changes over time in a dynamic manner via the insertion or deletion of edges. Dynamic algorithms in the sequential setting that obtain the biconnected components of a graph upon insertion or deletion of a single edge are known from over two decades ago. Parallel algorithms for this problem are not heavily studied. In this paper, we design shared-memory parallel algorithms that obtain the biconnected components of a graph subsequent to the insertion or deletion of a batch of edges