Abstract

We establish a convex resource theory of non-Markovianity inducing information backflow under the constraint of small time intervals within the temporal evolution. We identify the free operations and a generalized bona-fide measure of non-Markovian information backflow. The framework satisfies the basic properties of a consistent resource theory. The proposed resource quantifier is lower bounded by the optimization free Rivas–Huelga–Plenio (RHP) measure of non-Markovianity. We next define the robustness of non-Markovianity and show that it can directly linked with the RHP measure of non-Markovianity through a lower bound. This enables a physical interpretation of the RHP measure. We further relate robustness of non-Markovianity with the quantum capacity of dephasing channels.

Abstract

Software-defined networking (SDN) is an agile, modern networking approach that facilitates innovations in the networking paradigm. The abstracted and centralized network operating system facilitates the network management and reduces operational expenditure (OPEX). The open nature and simplicity of the data-forwarding plane dramatically reduces capital expenditure (CAPEX) by leveraging commodity servers and switches. SDN also lends itself very well to address major cloud computing issues and complement cloud services, especially in terms of network virtualization and networking as a service (NaaS). As a new technology, SDN does involve certain security challenges, which include distributed denial of service (DDoS) threats, build and run time injected malware, insider (tenant) attacks, and security holes resulting from controller misconfigurations. These are severe threats that can cripple an entire network. It is crucial to address the SDN vulnerabilities to ensure its successful deployment in private data center networks, on cloud platforms and beyond. Some security solutions leverage the built-in features of SDN, such as its controller software component, while other solutions provide external SDN applications running above the controller. This study reviews the security solutions for the vulnerabilities of state-of-the-art SDN controllers and the available countermeasures. Furthermore, an in-depth analysis of the SDN features that support security is presented, and some unresolved research issues on SDN controllers are identified.

Abstract

This article introduces a new consortium blockchain-enabled access control scheme in edge computing based generic Internet of Things environment (called CBACS-EIoT), where the mutual authentication among the IoT smart devices and the gateway node(s), and also among the gateway node(s) and respective edge server(s) occur. In addition, key management phase is executed among the edge server(s) and associated cloud server(s). Using the established secret keys, the entities in the network communicate securely. The data gathered securely by the gateway nodes are then used to form various types of blocks (private, public, or consortium) at the edge server(s) based on application types in the generic IoT environment. The created blocks are mined by the edge servers in order to add them in the blockchain center. A detailed security analysis including the formal security has revealed that the proposed CBACS-EIoT is robust against various potential attacks needed in the IoT environment. To further strengthen the security, the simulation-based formal security verification on CBACS-EIoT has been carried out to exhibit that CBACS-EIoT is secure against passive and active attacks. Finally, a meticulous comparative performance analysis shows that CBACS-EIoT offers superior security and supports more functionality features, and also provides less communication and computational overheads compared with existing relevant schemes.

Abstract

The recent research is focused on the development of an advanced interceptor missile that has hit-to-kill accuracy against ballistic targets performing evasive maneuvers. In this paper, a guidance law that achieves hit-to-kill accuracy against ballistic target executing worst-case maneuvers is developed using second-order sliding mode control and optimal control. The guidance law thus developed is continuous and has minimum time convergence for worst-case target maneuvers. The performance of the continuous guidance law with minimum time convergence is evaluated through numerical simulations against ballistic targets executing step maneuvers with changing polarity and weaving maneuvers.

Abstract

This work was supported by the Science and Engineering Research Board, Department of Science and Technology, Ministry of Science and Technology, India, under Grant SR/CSI/PDF-21/2013. John Eric Steephen: Conception and design, analysis and interpretation of data, drafting the article. Sneha Kummetha: Design, collection and analysis of data. Siva Charan Obbineni: Design, collection and analysis of data. Raju Surampudi Bapi: Design, drafting the article.

Abstract

A solution to the waypoint navigation problem for the fixed wing micro air vehicles (MAV) having a severe coupling between longitudinal and lateral dynamics, in the framework of integrated guidance and control (IGC) is addressed in this paper. IGC yields a single step solution to the waypoint navigation problem, unlike conventional multiple loop design. The pure proportional navigation (PPN) guidance law is integrated with the coupled MAV dynamics. A multivariable static output feedback (SOF) controller is designed for the linear state space model formulated in IGC framework. A waypoint navigation algorithm is proposed that handles the minimum turn radius constraint of the MAV and also evaluates the feasibility of reaching a waypoint. Non-linear simulations with and without wind disturbances are performed on a high fidelity 150 mm wingspan MAV model to demonstrate the proposed waypoint navigation algorithm.

Abstract

This paper analyzes the effects of propeller flow on the linear coupled longitudinal and lateral dynamics of a 150 mm wingspan fixed wing micro air vehicle (MAV). The effects propeller flow on the lift, drag, pitching moment and side force is obtained through wind tunnel tests. The aerodynamic forces and moments are modeled as a function of angle of attack, sideslip angle, control surface deflection and propeller rotation per minute. The nonlinear six degrees of freedom model is linearized about straight and constant altitude flight conditions for different trim airspeed to obtain linear coupled longitudinal and lateral state space model. The eigenvalues and eigenvectors of linear coupled longitudinal and lateral state space model are compared with and without propeller flow effects. The variation in the natural frequencies and damping ratios of short period mode, phugoid mode and Dutch roll mode are analyzed for various trim airspeed. An increase in the natural frequency is observed for phugoid mode and Dutch roll mode with propeller effects. The stability of the spiral mode is enhanced by the propeller flow and also the response of the roll subsidence mode is faster with propeller effects. Detailed analysis of eigenvalues and eigenvectors shows the importance of incorporating propeller flow in analyzing the dynamics of the MAV

Abstract

We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second. Specifically, we enhance the disentanglement capabilities of a state-of-the-art sequence-to-sequence based system with a Variational AutoEncoder (VAE) and a Householder Flow. The proposed system provides a 22% KLdivergence reduction while jointly improving perceptual metrics over state-of-the-art. At synthesis time we use one example of expressive style as a reference input to the encoder for generating any text in the desired style. Perceptual MUSHRA evaluations show that we can create a voice with a 9% relative naturalness improvement over standard Neural Text-toSpeech, while also improving the perceived emotional intensity (59 compared to the 55 of neutral speech). Index Terms— Text-to-speech, data efficiency, and semisupervised learning

Abstract

We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation. Here, the task is to fine-tune a pre-trained TTS model to mimic a new speaker using a small corpus of target utterances. We demonstrate that there does not exist a one-size-fits-all adaptation strategy, with convincing synthesis requiring a corpus-specific configuration of the hyper-parameters that control fine-tuning. By using Bayesian optimization to efficiently optimize these hyper-parameter values for a target speaker, we are able to perform adaptation with an average 30% improvement in speaker similarity over standard techniques. Results indicate, across multiple corpora, that BOFFIN TTS can learn to synthesize new speakers using less than ten minutes of audio, achieving the same naturalness as produced for the speakers used to train the base model.

Abstract

The introduction of transformers in 2017 and successively BERT in 2018 brought about a revolution in the field of natural language processing. Such models are pretrained on vast amounts of data, and are easily extensible to be used for a wide variety of tasks through transfer learning. Continual work on transformer based architectures has led to a variety of new models with state of the art results. RoBERTa (Liu et al., 2019) is one such model, which brings about a series of changes to the BERT (Devlin et al., 2018) architecture and is capable of producing better quality embeddings at an expense of functionality. In this paper, we attempt to solve the well known text classification task of fine-grained domain classification using BERT and RoBERTa and perform a comparative analysis of the same. We also attempt to evaluate the impact of data preprocessing specially in the context of fine-grained domain classification. The results obtained outperformed all the other models at the ICON TechDOfication 2020 (subtask-2a) Fine-grained domain classification task and ranked first. This proves the effectiveness of our approach.