Abstract

We study lepton number violating and lepton number conserving semileptonic decays of heavy baryons Λ0 b to three charged leptons and a neutrino. The decays occur through two intermediate quasidegenerate GeV-scale sterile neutrinos of either Majorana or Dirac type that can be on-shell. Interference between the intermediate heavy neutrinos leads to CP violation in the final states. Effect of neutrino oscillations between the heavy states are also considered in observables of interests, i.e., branching ratio, and the CP-asymmetry. Given the present constraints on the heavy-to-light mixing elements jVeNj and jVμNj, CP-averaged branching ratio of Λ0 b → ðΛc; pÞμμeν with intermediate Majorana neutrinos is almost two orders of magnitude larger than for the same with Dirac neutrinos, whereas, CP-averaged branching ratio of Λ0 b → ðΛc; pÞeeμν is of the same order of magnitude for both Majorana and Dirac neutrino induced decays. CP-violation is found to be appreciable when the neutrino mass difference is comparable with the average decay widths.

Abstract

The acoustic responses of a dynamical system reflect the characteristics of the external excitation and vibrating system. The objective in an identification problem is to characterise the system and excitation from the measured responses. This becomes challenging if the characteristics of the dynamical system are time-varying. The analysis of the acoustic signal generated as a result of the tyre-pavement interaction (TPI) is one such situation, where in order to study the time-varying nature there is a need to obtain the characteristics of the instantaneous response. This study explores the single frequency filtering analysis of signals to examine the time-varying characteristics of tyre-pavement interaction noise (TPIN). Field measurements of TPI acoustic signals were obtained for a two-wheeler driven at different speeds on asphalt and cement concrete pavements. The time-frequency characteristics of the tyre tread impact and of the air cavity resonances are analysed by investigating the effect of vehicular speed and pavement type on TPIN. The specific features of TPIN such as the harmonics due to periodic tread impact and the frequency response due to resonances of the air cavities are examined in detail. The analysis presented in this paper will be useful in developing methods for health monitoring of tyre and pavement systems.

Abstract

Stress, either physical or mental, is experienced by almost every person at some point in his lifetime. Stress is one of the leading causes of various diseases and burdens society globally. Stress badly affects an individual's well-being. Thus, stress-related study is an emerging field, and in the past decade, a lot of attention has been given to the detection and classification of stress. The estimation of stress in the individual helps in stress management before it invades the human mind and body. In this paper, we proposed a system for the detection and classification of stress. We compared the various machine learning algorithms for stress classification using EEG signal recordings. Interaxon Muse device having four dry electrodes

Abstract

In recent years, Deep Reinforcement Learning (DRL) has been extensively used to solve problems in various domains like traffic control, healthcare, and simulation-based training. Proximal Policy Optimization (PPO) and Soft-Actor Critic (SAC) methods are DRL’s latest state of art on-policy and off-policy algorithms. Though previous studies have shown that SAC generally performs better than PPO, hyperparameter tuning can significantly impact the performance of these algorithms. Also, a systematic evaluation of the efficacy of these algorithms after hyperparameter tuning in dynamic and complex environments is missing and much needed in literature. This research aims to evaluate the effect of the number of layers

Abstract

Language diversity in India is an extension of the vast cultural diversity that exists in Indian society. The globalization and presence of multinational companies made an inevitable need of English language, usage including in academics, though there are regional universities that use local languages as the medium of instruction. The emerging digital content market cannot elude the language diversity prevalent in the Indian population. The paper explores the possibility of finding common patterns among Hindi-speaking university-educated netizens

Abstract

In this smart and rapidly growing computing world, most of the organizations and companies needs to share necessary information (data) to third parties for their analysis which plays major role in decision making. Any data generally consists of sensitive (personal) information about people and organizations and when the sensitive information to be revealed to third parties, there is always chance of privacy loss. In this regard any organization or an individual requires the application of privacy preserving techniques to ensure data privacy, that means the data to be exchanged between two or more sites, without data loss or privacy violation. When data to

Abstract

This paper presents the resources and benchmarks developed for keyword search (KWS) in spoken audio from six low-resource Indian languages (from two families), namely Gujarati, Hindi, Marathi, Odia, Tamil, and Telugu. The current work on constructing keywords and building benchmark KWS systems is inspired by the popular IARPA Babel program and the subsequent works on low-resource KWS. The keywords are constructed by taking into account their properties i.e., occurrence, length, and average confusability; and their effects on the evaluation metric - the term-weighted value (TWV). We make use of freely available speech datasets, and reprocess them to create resources for KWS, thereby adding value to the existing speech resources. Four ASR-based KWS systems are built, and their performance is analyzed across the three keyword properties on all the six languages. The prepared keywords and other related resources to replicate our experiments are made available for the public. We believe that the analysis and guidelines provided in this paper will not only help the research community, but also practitioners and engineers to easily create KWS resources for newer languages, datasets, and scenarios. INDEX TERMS Keyword search, low-resource languages, term-weighted value (TWV).

Abstract

In this digitally connected world, data networks play a crucial role in the communication field. As there is a massive growth in data exchange, transactions and other sensitive data need to be secured. Networks must be safeguarded from malware attacks for flawless transmission of data between devices. Some of the harmful consequences of malware attacks are gaining administrative control and data breaches. Malware detection has become a significant task to get rid of those dreadful consequences and make networks secure. In this paper, we implement machine learning algorithms against the malware detection datasets NetML and CICIDS2017, and the traffic classification dataset non-vpn2016 dataset. The results are very promising and have been validated with the results obtained in the NetML Network Traffic Analytics Challenge 2020, organized by ACANETS. The overall score on the CICIDS2017 and non-vpn2016 datasets outperformed the baseline results published in the challenge, against all the five tracks (top, mid, and fine annotations)

Abstract

Static Single Assignment (SSA) is the workhorse of modern optimizing compilers for imperative programming languages. However, functional languages have been slow to adopt SSA and prefer to use intermediate representations based on minimal lambda calculi due to SSA's inability to express higher order constructs. We exploit a new SSA construct -- regions -- in order to express functional optimizations via classical SSA based reasoning. Region optimization currently relies on ad-hoc analyses and transformations on imperative programs. These ad-hoc transformations are sufficient for imperative languages as regions are used in a limited fashion. In contrast, we use regions pervasively to model sub-expressions in our functional IR. This motivates us to systematize region optimizations. We extend classical SSA reasoning to regions for functional-style analyses and transformations. We implement a new SSA+regions based backend for LEAN4, a theorem prover that implements a purely functional, dependently typed programming language. Our backend is feature-complete and handles all constructs of LEAN4's functional intermediate representation {\lambda}rc within the SSA framework. We evaluate our proposed region optimizations by optimizing {\lambda}rc within an SSA+regions based framework implemented in MLIR and demonstrating performance parity with the current LEAN4 backend. We believe our work will pave the way for a unified optimization framework capable of representing, analyzing, and optimizing both functional and imperative languages.

Abstract

Copyright © 2022 Hadaate Ullah et al. 2is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations. First, 97720 ECG beats extracted from all records of a benchmark MITBIH arrhythmia dataset have been transformed into 2D RGB (red, green, and blue) images that act as the inputs to the proposed 2D CNN models. 2en, the optimization of the proposed models is performed through the proper initialization of model layers, on-the-fly augmentation, regularization techniques, Adam optimizer, and weighted random sampler. Finally, the performance of the proposed models is evaluated by a stratified 5-fold cross-validation strategy along with callback features. 2e obtained overall accuracy of recognizing normal beat and three arrhythmias (V-ventricular ectopic, S-supraventricular ectopic, and F-fusion)