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

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 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

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 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

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

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

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

Speakers exhibit dialectal traits in speech at sub-segmental, segmental, and supra-segmental levels. Any feature representation for dialect classification should appropriately represent these dialectal traits. Traditional segmental features such as mel-frequency cepstral coefficients (MFCCs) fail to represent sub-segmental and supra-segmental dialectal traits. This study proposes to use frequency domain linear prediction cepstral coefficients (FDLPCCs) for dialect classification inspired by its long temporal summarization during pole estimation. The i-vectors and x-vectors derived from both baseline (MFCCs, linear prediction cepstral coefficients (LPCCs), perceptual LPCCs (PLPCCs), RASTA filtered PLPCCs (PLPCC-R) and proposed (FDLPCC) features are used for identifying the dialects with support vector machine (SVM) and feed-forward neural network (FFNN) as classifiers. Proposed FDLPCC features have shown to perform better than baseline features such as MFCCs and PLPCC-Rs (best among LPCCs variants) by an absolute improvement of 3.4% and 3.9% (in unweighted average recall (UAR)), with i-vector + SVM system and 1.6% and 4.6% (in UAR), i-vector + FFNN system respectively. It is also found that there exists a complementary information between the proposed and baseline features. Furthermore current studies are compared with previous studies and it is found that performances of current studies are better than previous studies.

Abstract

Coral Reefs are vital ecosystems as they support a vast population of aquatic life and help in maintaining biodiversity. Currently, these ecosystems are under severe threat due to rising ocean temperatures and contaminated surroundings. Hence, Coral Reefs need to be monitored regularly to study their health and protect them in a better way. Remote detection and assessment of Coral Reefs using satellite data would significantly enhance the reach and ensure continuous Coral Reef monitoring. The spectral signature of the Coral Reefs captured by the satellite sensor could be a great asset in this process. Still, these signatures are heavily dopped with several unwanted noise signals coming from various surface and atmospheric features. It is essential to remove these noise signals from the satellite data. This paper studied the effectiveness of different available atmospheric correction methods such as QUAC, 6S, and FLAASH Atmospheric correction tools (ACT) in removing atmospheric noise and studied the impact of sun glint and water column correction on improving remote detection of Coral Reefs. As a case study, these processes were applied on Landsat ETM+ to study the abundant Coral habitat near the North Reef Island at the Andaman Islands. The results indicated that FLAASH is the best atmospheric correction tool among the selected algorithms as FLAASH corrected image facilitated better identification of Coral Reefs. Hence this image was further enhanced using the Hedley sun glint and Lyzenga water column correction methods which improved Coral Reef detectability significantly. The detection of Corals from the image was done using ISO-DATA unsupervised classification to evaluate the role of each process in enhancing the Coral detection. Visual inspection was used to assess the classifier accuracy towards Coral reef detection. The results showed that a combination of FLAASH, sun glint removal, and water column correction significantly improved Coral Reef identification from the remote sensing data.