Showing papers by "ITM University, Gurgaon, Haryana published in 2023"

Democratic Model of Restorative Justice

Abstract: The adversarial model of criminal justice, with punishing the offender as its main aim, has proven counterproductive. Mounting arrears in the court, delay in the disposal of cases, and consistently rising rates of acquittals resulted in the loss of public confidence in the system. In India, hundreds of reports from different bodies and individuals are testimony to the fact that the system in the country is virtually collapsed. This chapter establishes that in a democratic country like India, while keeping the adversarial system for certain serious and complex offenses, India needs to experiment with more democratic models aimed at reconciliation and restoration of relationships through restorative justice. Further, this system with more democratic models aimed at reconciliation and restoration of relationships is a welcome step, particularly in the matter of rape offenses, juvenile justice, and family disputes; thereby providing a viable alternative to the criminal justice system.

A New Image Encryption Scheme Using RSA Cryptosystem and Arnold Algorithm

Abstract: In the present period, it is a pivotal worry that appropriate encryption unscrambling ought to be applied to send the information starting with one spot and then onto the next place across the Web to forestall unapproved access. RSA requires log key sizes and more calculations, huge memory spaces, and furthermore a long execution time; it appears to be important to roll out certain improvements or play it safe during the execution of RSA to give the most extreme security level. Picture Cryptography is a unique sort of encryption method to conceal information in a picture for encryption and unscrambling of unique messages in view of some key worth. A picture document is chosen for encoding and unscrambling utilizing the key age method to move the information starting with one objective and then onto the next. This paper proposes an uneven encryption plot for shading pictures by presenting another effective triple-layered encryption conspire in view of the RSA cryptosystem.

Performance and Emissions Characteristics of CI Engine Fuelled with Waste Engine Oil Biodiesel

Abstract: In this study, waste engine oil is utilized as an alternative fuel for the CI engine. The physico-chemical characteristics of waste engine oil blended with diesel are found to be closer to standard diesel. It is observed that engine performance was satisfactory; however, BSFC was lower than neat diesel. Furthermore, considering cost impact, it can be economical while production at a mass scale with controlled parameters. In India, there are a lot of automobile users; hence, there is a huge availability of waste engine oil. Hence, they can be easily converted into biodiesel and can be utilized as alternative fuels for internal combustion engines.

Meta-Learned Word Embeddings for Few-Shot Sentiment Classification

Abstract: For each new domain, researchers and technologists developing sentiment classification algorithms must gather and curate data. In particular for domains where the features change over time, the work required to annotate these corpora is prohibitive. The idea of training classifiers on a limited set of domains before using them on further domains is explored in this study. One issue is that classifiers frequently perform poorly when the distributions of the training and testing sets of data are different. Another issue is that it is unclear how to choose domains that are suitable for training and testing other domains based on similarities. To train classifiers that generalize and still perform well on test sets that come from various distributions, the authors approach this challenge by making use of numerous, smaller training datasets. The authors suggest using current developments in meta-learning to learn stronger cross-domain embeddings that can generalize for the few-shot learning context to achieve this. This work investigates sentiment classification using few-shot learning. There are a variety of existing approaches to this problem, but recent advances in meta-learning have proven to be quite effective in the few-shot context. For few-shot learning, the authors suggest using MAML (Model Agnostic Meta-Learning) to quickly train better cross-domain embeddings and networks. Our final model performs at a cutting-edge level.

Harmonic Performance Analysis for Different Loads with and Without PV

Abstract: In this paper, the synchronous reference frame technique (SRF technique) is implemented as a control strategy for the control of an inverter (power electronic converter) which is connected to a grid with constant supply. The objectives of this paper include modeling of the SRF technique with a DC voltage source and a Photovoltaic array with different types of loads. The types of loads considered here include linear load, non-linear load and a combination of both linear and non-linear loads. The paper compares the total. harmonic distortion for a system with constant DC voltage source and a system with PV as its source for different types of loads. The paper presents the harmonic performance of the system modeled in each case.