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)

Abstract

Internet of Things (IoT) is a fairly impending technologically. In recent years IoT technology spreads into all the areas which are, Agriculture sector, Industrial sector, Defense sector, Pharmacy sector, Science and Technology sector, and Irrigation sector etc. Due to its wide verity of applications, the human life becomes very much simpler than the previous. This IoT consists of many smart devices connected to each other with the help of the internet for communicate to each other for better results. With the help of IoT and other smart devices, online patient monitoring system and diagnosis are the innovative solutions for remote patients. Due to COVID-19 pandemic situations, online doctor, patient interactive system is very much essential. To further investigation of the disease or the health issues, initial test results measurements are safe. Consulting a doctor, diagnosing the disease, and Treatment a patient are very much possible through the proposed IOT Based Health Monitoring and Management System is very much useful for rural people. The proposed system is safe and the measured values are very much nearer to the physical patient test result.

Abstract

Travel blogs are a rich source of information about tourist spots. Millions of travellers use them to decide where to go and what to do. However, they are often devoid of factual information, which leads to a lack of trust and credibility surrounding blogs. On the other hand, Wikipedia is rich with accurate factual descriptions but is often bland and unfit for blogs. We propose a system to identify blog-worthy sentences on Wikipedia to augment travel blogs with factual information to make them more reliable and trustworthy. We curate a dataset of over 1.83 M Wikipedia sentences from 234,305 travel-related articles and assign a blog-worthiness score to each sentence by comparing them against 20 M sentences from ∼600 K

Abstract

The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals are frequently employed in stress detection research and are both inexpensive and noninvasive modality. This paper proposes a stress classification system by utilizing an EEG signal. EEG signals from thirty-five volunteers were analysed which were acquired using four EEG sensors using a commercially available 4-electrode Muse EEG headband. Four movie clips were chosen as stress elicitation material. Two clips were selected to induce stress as it contains emotionally inductive scenes. The other two clips were chosen that do not induce stress as it has many comedy scenes. The recorded signals were then used to build the stress classification model. We compared the Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) for classifying stress and nonstress group. The maximum classification accuracy of 93.17% was achieved using two-layer LSTM architecture.

Abstract

Electricity markets are getting more dynamic and complex today. On the one hand, there are outside-in factors like new entrants and increasing competition, regulatory mandates, and volatility of the electricity prices. In contrast, on the other hand, there are inside-out factors like the adoption of renewable energies, storages, and EV, emphasis on the generation and retail portfolio optimization together with creating affinity services around the empowered consumers. The paper will start with the introduction of this dynamic environment across the globe with a specific focus on Australia, the UK, and US electricity markets. Introduction of 5 min settlement in Australian Electricity Market, the rise of ancillary service products in the UK, or new roles like Community Choice Aggregators (CCA) in the US are some of the