Abstract

Deep Learning (DL) finds application in several prominent fields, including computer vision, natural language processing, and bioinformatics. The proliferation of DL-based methods has brought to notice critical issues about bias (or unfairness) in classification and weak privacy guarantees of the training data. It is crucial to prioritize addressing these issues to prevent the potentially significant negative impact on users. While there has been progress, majority of the works focus on independently resolving fairness and privacy. We propose a tutorial on “Fair and Private Deep Learning” – aimed to provide an exhaustive discussion on (i) reasons behind unfair classifications and lack of privacy, (ii) fairness notions in literature and methods to ensure them, (iii) differentially private DL, and (iv) algorithms that address fair and private DL simultaneously. Moreover, in this tutorial, we not just limit our attention to classical, centralized DL models but also to the fairness and privacy challenges in distributed (or federated) DL. The code, presentation and other details are available at https://github.com/magnetar-iiith/FairPrivateDL.

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 article presents a metacognitive decision-making (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search without communication for detecting and confirming stationary targets (fixed/sudden pop-up) and dynamic targets. The UAVs are equipped with multiple sensors (varying sensing capability) and search for targets in a largely unknown area. The MDM framework consists of a metacognitive component and a self-cognitive component. The metacognitive component helps to self-regulate the search with multiple sensors addressing the issues of “which-sensor-to-use,” “when-to-switch-sensor,” and “how-to-search.” Based on the information gathered by sensors carried by each UAV, the self-cognitive component regulates different levels of stochastic search and switching levels for effective searching, where the lower levels of search aim to localize a target (detection) and the highest level of a search exploit a target (confirmation). The performance of the MDM framework with two sensors having a low accuracy for detection and increased accuracy to confirm targets is evaluated through Monte Carlo simulations and compared with six decentralized multi-UAV search algorithms (three self-cognitive searches and three self and social-cognitive-based searches). The results indicate that the MDM framework can efficiently detect and confirm targets in an unknown environment.

Abstract

This article proposes the multiswarm cooperative information-driven search and divide and conquer mitigation control (MSCIDC) approach for faster detection and mitigation of forest fires by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of unmanned aerial vehicles (UAVs) flying together to search and quench the fire areas effectively. The multiswarm cooperative information-driven search uses a two-stage search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire locations. The search level is selected based on the thermal sensor information about the potential fire area. The dynamic nature of swarms acquired from global regulative repulsion and merging between swarms reduces the detection and mitigation time compared to the existing methods. The local attraction among the swarm members helps the nondetector members reach the fire location faster, and divide-and-conquer mitigation control ensures a nonoverlapping fire sector allocation for all members quenching the fire. The performance of the MSCIDC has been compared with different multi- UAV methods using a simulated pine forest environment. The Monte-Carlo simulation results indicate that the MSCIDC reduces the average forest area burnt by 65% and mission time by 60% compared to the best case of the multi- UAV approaches, guaranteeing a faster and more successful mission.

Abstract

Developing a reinforcement learning (RL) framework that works satisfactorily in deterministic and stochastic environments is challenging. To address this problem, a twin agent RL framework is proposed in this work, wherein we amalgamate both stochastic and deterministic agents’ actions in a multiagent framework that works with a feedback mechanism that actively monitors the output. The proposed algorithm uses twin actor networks of different agents, corresponding to deterministic and stochastic agents, and an action selection critic network is used to choose the best action from both agents. Here, the algorithm blends the outcomes of two reinforcement learning (RL) agents, a stochastic agent and a deterministic agent, namely, Proximal Policy Optimization (PPO) and Twin Delayed Deep Deterministic Policy Gradient (TD3), respectively. We assess the effectiveness of the proposed algorithm by applying it to two case studies: (i) monoclonal antibody (mAb) production and (ii) production of propylene glycol (PG). The studies are conducted in the presence of parametric uncertainties, measurement noise, and nominal conditions. It is observed that for case study 1, the root-mean-square error (RMSE) value for the proposed algorithm is reduced by 40.9% when compared with TD3 and 27.57% when compared with PPO for the simulations. Similarly, for case study 2, the RMSE for the proposed algorithm is reduced by 8.87% when compared with TD3 and 5.8% with PPO. Based on extensive simulations, it is found that the proposed twin agent algorithm has faster convergence and better set-point tracking when compared to the agents operated individually.

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

Transportation of a suspended payload using quadrotor demands a controller to simultaneously track the desired path and stabilize the planar payload swing angles. Such a control objective is made challenging by the need to orchestrate the coupling between fully actuated dynamics (quadrotor attitude) and underactuated dynamics (quadrotor position and planar payload swings). State-of-the-art controllers cannot orchestrate such coupled dynamics in the presence of unmodeled and state-dependent terms, such as aerodynamic drags and rotor downwash. This article solves this control challenge by adaptively estimating the state-dependent uncertainty. Closed-loop stability for the coupled underactuated and fully actuated dynamics is established analytically. Extensive real-time experiments confirm significant improvements over the state-of-the-art under various scenarios, such as path tracking with suspended payload and anti-swing control against externally induced payload swings.