Relation Between Indian Constitution and Democracy

Today the people in India are in a mood which comes rarely in the life of a country. They are looking forward starry eyed, to a new direction, a new era, a life. It is time not merely for a new budget or a new licensing policy or a new price structure. It is the moment for shaping and moulding a new society, for giving a new and clear orientation to the nation. The constitution is not a structure of fossils like a coral reef and is not intended merely to enable politicians to play their unending game of power. When a republic comes to birth, it is the leaders who produce the institutions. Later, it is the institutions which produce the leaders. In India’s case the established structures failed to give desired results. If the system of Parliamentary democracy had been worked in conformity with the objectives for which it has been established and the obligations and codes of conduct it imposes on politicians, political parties and their mutual relations, it would have constituted a most heart warming feature in finding a way out of the morass and confusion in which we are finding ourselves as a nation. In the words of T.S. Eliot, ‘we had the experience, but we missed the meaning’. We the Indians, know it well that our democratic institutions have not been worked in that manner. Our electorate is largely illiterate and not in a position to take an objective or critical view of the promises and performances of different political parties.

Contemporary Challenges of Indian State System

Indian society is drastically changing after globalisation. In this era different forms of difference have come to the foreground in relation to identity politics, gender, minorities rights, indigenous peoples, and ethnic and religious movement. The lower and weaker section of society has to be the worst sufferers as they will not get jobs. Consequently the income gap between lower and upper level of society is bound to rise and this, along with consumerism, and its demonstration over modern electronic means of communication, will lead to crimes, anarchy and destruction of social harmony and equilibrium. On the other the role of government is changing as we witness a fragmentation of policy responsibility in society in which the traditional mechanism of government control are no longer workable or even appropriate. It challenges the traditional relationship between economy and state. The globalized market system stretches beyond the political authority of any single government. Faced with a network of connections that escape their power of surveillance or regulation, national governments have become increasingly unequal to providing the legal, monetary, or protective functions that are their contribution to a well divided loyalties -on the one hand eager for its firms to maximize revenues, which are subject to national taxation, on the other hand, reluctant to see employment or research capabilities that it wants as part of its national economic strength located in a competitive national entity. As the globalization is a necessary evil affecting the entire system of today’s state by the analysis of Indian state system the paper aims to draw world attention towards the challenges / problems of other developing countries who are losers in this frame work due to reasons more than one. Thus it is beneficial as well as relevant not only for any particular country of region but for across the globe.

Overview of Image Processing and Various Compression Schemes

Image processing is key research among researchers. Compression of images are required when need of transmission or storage of images. Demand of multimedia growth, contributes to insufficient bandwidth of network and memory storage device. Advance imaging requires capacity of extensive amounts of digitized information. Therefore data compression is more required for reducing data redundancy to save more hardware space and transmission bandwidth. Various techniques are given for image compression. Some of which are discussed in this paper

CYTOLOGY OF BREAST LESIONS: A RETROSPECTIVE STUDY IN A NORTHERN INDIAN STATE OF HIMALAYAS.

Background: Palpable breast lump, breast pain, and nipple discharge are common symptoms of breast disease. Breast cytology (fine-needle aspiration, nipple discharge smear, and touch preparation) accurately identifies benign, atypical, and malignant pathological changes in breast specimens. This study aims to determine the types of breast lesions diagnosed by breast cytology and assess the clinical adequacy of narrative reporting of breast cytology results. Methods: Medical records of 390 patients presenting to breast or general surgery clinics in Dr. Rajendra Prasad Medical College, Tanda between four years were evaluated retrospectively. Results: Of the 390 diagnosed breast lesions, 89.7 % (n = 350) occurred in females, while 10.3 % (n = 40) occurred in males, giving rise to a female-to-male ratio of 8.8:1. Neoplastic breast lesions (n = 296) comprised 75.9 %, while non-neoplastic breast lesions (n = 94) comprised 24.1 % of all diagnosed breast lesions. The neoplastic lesions were classified as 72.3 % (n = 214) benign and 27.7 % (n = 82) malignant, resulting in a benign-to-malignant ratio of 2.6:1. Fibroadenoma (n = 136) and gynecomastia (n = 33) were the most frequently diagnosed breast lesions for women and men, respectively. Conclusions: Breast cytology effectively diagnosed neoplastic and non-neoplastic breast lesions. Neoplastic breast lesions occurred more frequently in women whereas non-neoplastic lesions occurred more frequently in men. To address the limitations associated with narrative reporting of breast cytology results, a synoptic reporting format incorporating the United Kingdom’s National Health Service Breast Screening Programme’s diagnostic categories (C1 to C5) is recommended for adoption by this hospital

Towards a Game-theoretic Understanding of Explanation-based Membership Inference Attacks

Model explanations improve the transparency of black-box machine learning (ML) models and their decisions; however, they can also be exploited to carry out privacy threats such as membership inference attacks (MIA). Existing works have only analyzed MIA in a single "what if" interaction scenario between an adversary and the target ML model; thus, it does not discern the factors impacting the capabilities of an adversary in launching MIA in repeated interaction settings. Additionally, these works rely on assumptions about the adversary's knowledge of the target model's structure and, thus, do not guarantee the optimality of the predefined threshold required to distinguish the members from non-members. In this paper, we delve into the domain of explanation-based threshold attacks, where the adversary endeavors to carry out MIA attacks by leveraging the variance of explanations through iterative interactions with the system comprising of the target ML model and its corresponding explanation method. We model such interactions by employing a continuous-time stochastic signaling game framework. In our framework, an adversary plays a stopping game, interacting with the system (having imperfect information about the type of an adversary, i.e., honest or malicious) to obtain explanation variance information and computing an optimal threshold to determine the membership of a datapoint accurately. First, we propose a sound mathematical formulation to prove that such an optimal threshold exists, which can be used to launch MIA. Then, we characterize the conditions under which a unique Markov perfect equilibrium (or steady state) exists in this dynamic system. By means of a comprehensive set of simulations of the proposed game model, we assess different factors that can impact the capability of an adversary to launch MIA in such repeated interaction settings.Comment: arXiv admin note: text overlap with arXiv:2202.0265

To ChatGPT, or not to ChatGPT: That is the question!

ChatGPT has become a global sensation. As ChatGPT and other Large Language Models (LLMs) emerge, concerns of misusing them in various ways increase, such as disseminating fake news, plagiarism, manipulating public opinion, cheating, and fraud. Hence, distinguishing AI-generated from human-generated becomes increasingly essential. Researchers have proposed various detection methodologies, ranging from basic binary classifiers to more complex deep-learning models. Some detection techniques rely on statistical characteristics or syntactic patterns, while others incorporate semantic or contextual information to improve accuracy. The primary objective of this study is to provide a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection. Additionally, we evaluated other AI-generated text detection tools that do not specifically claim to detect ChatGPT-generated content to assess their performance in detecting ChatGPT-generated content. For our evaluation, we have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains and user-generated responses from popular social networking platforms. The dataset serves as a reference to assess the performance of various techniques in detecting ChatGPT-generated content. Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content

BayBFed: Bayesian Backdoor Defense for Federated Learning

Federated learning (FL) allows participants to jointly train a machine learning model without sharing their private data with others. However, FL is vulnerable to poisoning attacks such as backdoor attacks. Consequently, a variety of defenses have recently been proposed, which have primarily utilized intermediary states of the global model (i.e., logits) or distance of the local models (i.e., L2-norm) from the global model to detect malicious backdoors. However, as these approaches directly operate on client updates, their effectiveness depends on factors such as clients' data distribution or the adversary's attack strategies. In this paper, we introduce a novel and more generic backdoor defense framework, called BayBFed, which proposes to utilize probability distributions over client updates to detect malicious updates in FL: it computes a probabilistic measure over the clients' updates to keep track of any adjustments made in the updates, and uses a novel detection algorithm that can leverage this probabilistic measure to efficiently detect and filter out malicious updates. Thus, it overcomes the shortcomings of previous approaches that arise due to the direct usage of client updates; as our probabilistic measure will include all aspects of the local client training strategies. BayBFed utilizes two Bayesian Non-Parametric extensions: (i) a Hierarchical Beta-Bernoulli process to draw a probabilistic measure given the clients' updates, and (ii) an adaptation of the Chinese Restaurant Process (CRP), referred by us as CRP-Jensen, which leverages this probabilistic measure to detect and filter out malicious updates. We extensively evaluate our defense approach on five benchmark datasets: CIFAR10, Reddit, IoT intrusion detection, MNIST, and FMNIST, and show that it can effectively detect and eliminate malicious updates in FL without deteriorating the benign performance of the global model