NoSQL Databases in Kubernetes

With the increasing popularity of deploying applications in containers, Kubernetes (K8s) has become one of the most accepted container orchestration systems. Kubernetes helps maintain containers smoothly and simplifies DevOps with powerful automations. It was originally developed as a tool to manage stateless microservices that run seamlessly in containers. The ephemeral nature of pods, the smallest deployable unit, in Kubernetes was well-aligned with stateless applications since destroying and recreating pods didn’t impact applications. There was a need to provision solutions around stateful workloads like databases so as to take advantage of K8s. This project explores this need, the challenges associated and the available solutions for running databases in Kubernetes. Most of the current research is focused towards SQL-like databases in K8s even though the DNA of NoSQL distributed databases is more aligned with K8s. With no research being done with NoSQL databases, this project outlines the process behind setting up two famous NoSQL databases in K8s: MongoDB and Cassandra. The project also shows a representative viewpoint of the performance comparison between them using the YCSB benchmark. The project lays a foundation around the setup of these databases using K8s Operators and their benchmarking. The goal of the project is to describe the advantages of having databases in K8s, provide developers a clear path for setup and provide insights on basic benchmark performance

Robust Direction-of-Arrival Estimation using Array Feedback Beamforming in Low SNR Scenarios

A new spatial IIR beamformer based direction-of-arrival (DoA) estimation method is proposed in this paper. We propose a retransmission based spatial feedback method for an array of transmit and receive antennas that improves the performance parameters of a beamformer, viz. half-power beamwidth (HPBW), side-lobe suppression, and directivity. Through quantitative comparison, we show that our approach outperforms the previous feedback beamforming approach with a single transmit antenna, and the conventional beamformer. We then incorporate a retransmission based minimum variance distortionless response (MVDR) beamformer with the feedback beamforming setup. We propose two approaches, show that one approach is superior in terms of lower estimation error, and use that as the DoA estimation method. We then compare this approach with Multiple Signal Classification (MUSIC), Estimation of Parameters using Rotation Invariant Technique (ESPRIT), robust MVDR, nested-array MVDR, and reduced-dimension MVDR methods. The results show that at SNR levels of -60 dB to -10 dB, the angle estiation error of the proposed method is 20 degree less compared to that of prior methods

Development of Thabdi milk sweets of Gujarat State, India utilizing Ghee residue as an ingredient

Thabdi an ethnic khoa based milk sweet of Gujarat State, which is famous for its characteristic colour texture and flavour, was prepared using ghee residue as an ingredient in order to provide a way to effectively utilize the by-product. Ghee residue was added in milk at different rates viz. T1 (control), T2 (2%), T3 (4%), T4 (6%), T5 (8%) and T6 (10 %). Addition of ghee residue in milk for making Thabdi was found to significantly (P?0.05) increase the fat, protein and ash content, Free fatty acids (FFA), Thiobarbituric acid (TBA) value and acidity significantly (P?0.05) decreased the hardness of the products. Addition of ghee residue resulted in elimination of the 40 min holding period generally used for making Thabdi. Sample T4 (containing 6 % ghee residue) yielded the most acceptable product in terms of sensory attributes of product. It had a glossy brown colour, soft body, uniform grainy texture and pleasing rich nutty caramel flavour. During storage of sample T4 at cabinet temperature (20±1oC) and room temperature (37 C), the acidity, FFA, HMF (Hydroxymethyl Furfural), TBA and hardness increased significantly (P? 0.05) and sensory scores, moisture, water activity and pH decreased significantly(P? 0.05). It can be concluded that the most acceptable quality Thabdi could be prepared by addition of ghee residue at the rate of 6 % w/w of milk with improved shelf-life of 28 days at 20±1oC and 14 days at 37±1oC as compared to 21 and 12 days for control respectively. Thus, Thabdi sweet prepared with the use of ghee residue as an ingredient will provide a way to effectively utilize the by-product

When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making

As machine learning (ML) models are increasingly being employed to assist human decision makers, it becomes critical to provide these decision makers with relevant inputs which can help them decide if and how to incorporate model predictions into their decision making. For instance, communicating the uncertainty associated with model predictions could potentially be helpful in this regard. However, there is little to no research that systematically explores if and how conveying predictive uncertainty impacts decision making. In this work, we carry out user studies to systematically assess how people respond to different types of predictive uncertainty i.e., posterior predictive distributions with different shapes and variances, in the context of ML assisted decision making. To the best of our knowledge, this work marks one of the first attempts at studying this question. Our results demonstrate that people are more likely to agree with a model prediction when they observe the corresponding uncertainty associated with the prediction. This finding holds regardless of the properties (shape or variance) of predictive uncertainty (posterior predictive distribution), suggesting that uncertainty is an effective tool for persuading humans to agree with model predictions. Furthermore, we also find that other factors such as domain expertise and familiarity with ML also play a role in determining how someone interprets and incorporates predictive uncertainty into their decision making

Single-Pot Rapid Synthesis of Colloidal Core/Core-Shell Quantum Dots: A Novel Polymer-Nanocrystal Hybrid Material

Colloidal core and core shell Quantum Dots (QD's) are unique and important optoelectronic materials because properties of these QD's can be tailored by configuring core and optimizing shell thickness. In this research work, lead selenide (PbSe) core and PbSe-CdSe (Core-shell) QD's are synthesized using oleic acid as a capping ligand by colloidal route. This simpler, cost-effective and rapid single pot synthesis route for colloidal core-shell quantum dots unlike conventional double-pot approach like cation-exchange and SILAR process has been reported for the very first time. Phase formation of prepared quantum dots is confirmed by XRD analysis, capping ligand presence by IR spectroscopy and morphological information by Scanning electron microscopy respectively. These synthesized inorganic quantum dots are dispersed in Poly (3-hexyl thiophene) polymer for formation of their respective nanocomposites. From PL quenching studies, it was inferred that PbSe-CdSe core-shell quantum dots showed enhanced rate of PL quenching and hence higher value of Stern-Volmer constant (K-SV) than PbSe Core QD's. This confirms that CdSe shell formation on PbSe core significantly passivates the core-surface, increases the stability and enhances the charge transfer mechanism for its potential application in Hybrid Solar cells

Combining Textual Features for the Detection of Hateful and Offensive Language

The detection of offensive, hateful and profane language has become a critical challenge since many users in social networks are exposed to cyberbullying activities on a daily basis. In this paper, we present an analysis of combining different textual features for the detection of hateful or offensive posts on Twitter. We provide a detailed experimental evaluation to understand the impact of each building block in a neural network architecture. The proposed architecture is evaluated on the English Subtask 1A: Identifying Hate, offensive and profane content from the post datasets of HASOC-2021 dataset under the team name TIB-VA. We compared different variants of the contextual word embeddings combined with the character level embeddings and the encoding of collected hate terms

A cross-sectional study of factors affecting seasonality in bipolar disorder

Background. Researchers have evinced interest in the effect of seasonal variations on mood and behavioural patterns in affective disorders.  Objective. To study seasonality in bipolar disorder (BD) patients and also the factors affecting this seasonality.  Method. Forty-nine patients with BD in euthymic phase were recruited and analysed using the Seasonal Pattern Assessment Questionnaire and Morningness-Eveningness Questionnaire.  Results. Most of the patients were morning types but chronotype had no influence on seasonality. Age of patient and number of episodes were the most important factors affecting seasonality in BD.  Conclusion. Seasonality and its influencing factors must be considered while managing bipolar disorder