Abstract

In this work, NiMg@OH/reduced graphene oxide (rGO) nanocomposites are synthesised through microwave-assisted synthetic methodology using various reducing agents. The structural features and surface morphology of synthesised materials are fully examined with different spectroscopy and microscopy techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-visible spectroscopy (UV-Vis), Raman and Fourier Transform Infrared (FT-IR) spectroscopy. Further, this material was subjected to cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques for the systematic study of the properties and impact of reducing agents at various percentages of rGO on the specific capacitance of synthesised materials. The presence of rGO gradually improves the charge transfer process at the electrode/electrolyte interface. Among these nanocomposite materials, maximum specific capacitance of 650.31 F/g was observed for NiMg@OH-1.5 wt% rGO synthesised using NH4OH reducing agent in 6 M KOH electrolyte. Hence, the result signifies these composite materials are a promising

Abstract

Automatic identification of emotions from a given music track would be of significant importance in the area of Music Information Retrieval. Several studies have found lyrics an important feature in the task of emotion classification [1]. Using lyrics, till date, a transformer-model which is a Natural-Language-Processing (NLP) based Deep-Learning model has achieved state-of-the-art results in identifying music emotion as represented in the Valence-Arousal (VA) space [2]. While transformer models rely on parsing the syntactic structure of sequential input data, we assume that semantic relations could be efficiently learned from these representations using novel capsule networks [3]. The “capsules” are akin to neuronal populations breaking down information into parts that mimics human processing of information by chunking for efficient encoding and evaluation of the stimulus. We extend the same logic further to say that capsule networks should be able to look at the semantic representation of each word in the lyrics of a song and should be able to learn the relationship between these words and the emotion that they represent as a group

Abstract

Recent studies utilizing online music streaming data have found genre preferences to be associated with risk for depression (Surana et al., 2020). However, the mentioned study does not address gender differences among participants. Gender differences have been reported in depression in terms of the prevalence, symptomatology, and emotion regulation strategies used (Garnefski et al., 2004). Also, there exists a gender-specific tendency to choose certain kinds of music where females tend to prefer ‘softer’ styles like mainstream pop, while males are inclined towards ‘heavier’ styles like hard rock (North and Hargreaves, 2008). Martin et al.(1993) investigated the relationship between adolescents’ music preferences and risk factors for suicide, which included depression. Females preferring rock/metal music were found to have higher scores for depression as compared to males preferring rock/metal. Miranda and Claes (2009) found metal music-listening to be associated with higher depression levels in adolescent females, but only if they affiliate with peers who are more depressed. However, the above studies relied on self-reported music preferences. The current study explores these associations in more naturalistic settings using online music consumption.

Abstract

Personality is known to influence emotional experiences including those induced by music listening. Previous research has largely relied on self-report and models with limited terms, which may not adequately capture naturalistic behavior and experience. Data from the platform Last.fm allows for the analysis of listening behaviors using both musical features and semantic tags provided by listeners, for a more naturalistic exploration of personality, music and emotion.

Abstract

Studies have emerged characterizing naturally occurring music listening behaviour of individuals at-risk for depression via the online music streaming platform Last.fm (Surana et al. 2020a; Surana et al. 2020b), albeit limited to social tags and acoustic features. No studies have looked at artist preferences and predictive capacity using this information associated with risk for depression.

Abstract

Large pretrained models have seen enormous success in extractive summarization tasks. In this work, we investigate the influence of pretraining on a BERT-based extractive summarization system for scientific documents. We derive significant performance improvements using an intermediate pretraining step that leverages existing summarization datasets and report state-of-the-art results on a recently released scientific summarization dataset, SciTLDR. We systematically analyze the intermediate pretraining step by varying the size and domain of the pretraining corpus, changing the length of the input sequence in the target task and varying target tasks. We also investigate how intermediate pretraining interacts with contextualized word embeddings trained on different domains.

Abstract

Principal component analysis(PCA) is one of the most popular linear dimensionality reduction techniques in machine learning. In this paper, we try to present a method for performing PCA on encrypted data using a homomorphic encryption scheme. In a client-server model where the server performs computations on the encrypted data, it (server) does not require to perform any matrix operations like multiplication, inversion, etc. on the encrypted data. This reduces the number of computations significantly since matrix operations on encrypted data are very computationally expensive. For our purpose, we used the CKKS homomorphic encryption scheme since it is most suitable for machine learning tasks allowing approximate computations on real numbers. We also present the experimental results of our proposed Homomorphic PCA(HPCA) algorithm on a few datasets. We measure the R2 score on the reconstructed data and use it as an evaluation metric for our HPCA algorithm. Keywords: Homomorphic Encryption · Principal Component Analysis(PCA) · CKKS scheme · Goldschmidt’s Algorithm

Abstract

Recently, there has been research on what factors influence a user’s password setting practices, which include various types of emotions such as anger, risk-taking tendencies, etc. However, research has shown that factors such as memorability and perceived memorability have a greater influence on password choice. Some recent research has shown a negative correlation between the perceived memorability and the perceived security of passwords, particularly passphrases (that are technically more secure). However, it is unclear whether this effect can be extended to groups with good experiences with digital spaces (IT professionals, entrepreneurs, etc.). Furthermore, it has not been determined whether random, uncommonly-worded, or complex structure passphrases would also maintain the correlation, as opposed to relatively less secure, common/simple passphrases. This study examines this problem using a diverse demographic and different categories of passphrases. Keywords: Metacognition; Passwords; Perception; Memorability; Security

Abstract

ECG analysis comprises the following steps: preprocessing, segmentation, feature extraction, and classification of heart-beat instances to detect cardiac arrhythmias. This work focuses on the first three steps in cardiac arrhythmia analysis. Since no publicly available feature sets are available for the ECG arrhythmia detection problem, researchers have to explicitly extract the feature sets from the raw signals available in PhysioBank repository. In this work, the complete feature extraction pipeline has been developed using Matlab environment. Both the Matlab code and resulting feature sets for all records have been deposited and made publicly available in the Github repository. This is one of the major

Abstract

This research was carried out along the Bhadra River, which is one of the tributaries of the TungaBhadra and originates near Gangamula in Karnataka’s the Western Ghats. The study stretch is around 27 km divided into three reaches with elements of 1 km as 3, 4, and 20 for each reach, respectively. The objectives of the study are to assess the effects of wastewater discharges on the water quality of the Bhadra River stretch, to simulate the Dissolved oxygen (DO) by varying the Biochemical oxygen demand (BOD) loads coming from different pollutant sources within the study stretch using the QUAL2K river water quality model. The study period considered is about 24 months (April 2014-March 2016) for the simulation of temperature, pH, DO, and Nitrate parameters with the help of observed data at three monitoring stations. The investigation period was separated into two parts: the model was calibrated using monthly average data from April 2014 to March 2015 (12 months), and the model was validated using monthly average data from April 2015 to March 2016 (12 months). The R-square (R2 ) value for temperature and pH ranges from 0.87-0.56 and 0.64-0.62, respectively. The Root Mean Square Error (RMSE) for Nitrate and DO ranges from 0.06-0.02 and 0.47-0.34, respectively. The results revealed that the Bhadra river stretch was highly polluted due to effluents coming from the industries present in the study stretch. It was also discovered that river flow rate, point source discharge, fast Carbonaceous Biochemical oxygen demand (CBOD) oxidation rate, and nitrification rate as the highly sensitive quality parameters defining the Bhadra river water quality. There must be a reduction of 25% of BOD effluent to reach the minimum standards set by the Central Pollution Control Board (CPCB). It is noted that a 75% reduction of BOD effluent from point sources will lead to an increase of 15% average DO throughout the study stretch. Keyword Qual2k model, Water quality, Bhadra River, Dissolved oxygen, BOD.