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6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Sewariya, Manish Katarya, Rahul Delhi Technical University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science New Delhi India

ISBN: (纸本)9781665435598

The pandemic COVID-19 (SARS-CoV-2) known as the coronavirus is spreading at a very alarming rate in the world which is quite a threatening situation. In terms of disease presentation, the global epidemic COVID-19 can be traced to the ancestral influenza viruses. COVID-19 outbreak is a very perturbing situation for the high rate of mortality as reported in recent studies. The mortality and morbidity rates have always been a concerning issue in the determination of the scale of epidemics. These mortality rate predictions subsume an interesting factor within them i.e. variance of mortality rates across regions. The advancement in machine learning-based tools is a vital component corresponding to addressing the 'heterogeneity in fatality rates'. This review envelopes the recent developments in mortality rate predictions for COVID-19. The review helps in gaining an insight into Spatio-temporal mortality dynamics which will be fruitful with reference to future control-strategy implementation. By observing the Spatio-temporal pattern of mortality dynamics, the inter-dependence of various factors can be explored, and hence, mortality rates can be reduced in the upcoming time. © 2021 IEEE.

关键词： COVID-19

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Seeing Through the Lies: A Vision Transformer-Based Solution 3rd

Seeing Through the Lies: A Vision Transformer-Based Solution

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3rd International Conference on Artificial-Business analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Rasool, Aale Katarya, Rahul Big Data Analytics and Web Intelligence Laboratory Department of Computer Science Delhi Technological University New Delhi India

ISBN: (纸本)9789819725076

Deepfake technology has become increasingly sophisticated and poses a growing threat to society, as it can be used to create convincing fake videos for malicious purposes. Therefore, detecting deepfakes has become crucial to ensure the authenticity of visual content. This paper proposes a novel deepfake detection approach that utilizes InceptionResNetV2 for feature extraction and Vision Transformer (ViT) with Nyström Attention mechanism for classification. Our model achieves high accuracy, AUC, precision, and recall on the CelebDFv2 dataset and outperforms state-of-the-art techniques like EfficientNet, XceptionNet, and ResNet. We also perform evaluations on the CelebDFv1 and DFDC datasets. Furthermore, we conduct a comprehensive literature survey to highlight the existing research in deepfake detection. We provide a roadmap for future work in this field, highlighting potential research directions that can further improve the effectiveness and applicability of deepfake detection models. The proposed model shows great potential for real-world applications, and the suggested future work can help to address some of the remaining challenges in this area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Parallelizing OPTICS for multicore systems 14

Parallelizing OPTICS for multicore systems

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7th ACM India Computing Conference, COMPUTE 2014

作者： Goyal, Poonam Kumari, Sonal Kumar, Dhruv Balasubramaniam, Sundar Goyal, Navneet Advanced Data Analytics and Parallel Technologies Laboratory Department of Computer Science and Information Systems BITS-Pilani Pilani India

ISBN: (纸本)9781605588148

Parallelizing algorithms to leverage multiple cores in a processor or multiple nodes in a cluster setup is the only way forward to handle ever-increasing volumes of data. OPTICS is a well-known density based clustering algorithm to identify arbitrary shaped clusters. Since, hierarchical cluster ordering of OPTICS is sensitive to the order in which data is processed, typically a priority queue is used to maintain the order. This sequential access order makes it difficult to parallelize OPTICS. Moreover, the execution time of OPTICS increases with increase in density of data. We propose a parallel version of OPTICS for shared memory multi-core systems using a master-slave pattern for parallelization. The master runs concurrently with the slaves and distributes data to the slaves. Each slave performs neighborhood queries for a subset of data. Our approach ensures that cluster ordering matches with that of the classical OPTICS. Our solution runs in a mostly data parallel mode yielding scalable performance. We also argue that our approach is well suited for dense datasets in particular. Copyright 2014 ACM.

关键词： Hierarchical systems

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Review Authorship Attribution in a Similarity Space

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Journal of computer science & Technology 2015年第1期30卷 200-213页

作者：钱铁云刘兵李青司建锋 State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China Department of Computer Science University of Illinois at Chicago Chicago 60607 U.S.A. Multimedia Software Engineering Research Centre and Department of Computer Science City University of Hong Kong Hong Kong China Data Analytics Department Institute for Infocomm Research Singapore 138632 Singapore

Authorship attribution, also known as authorship classification, is the problem of identifying the authors （reviewers） of a set of documents （reviews）. The common approach is to build a classifier using supervised learning. This approach has several issues which hurts its applicability. First, supervised learning needs a large set of documents from each author to serve as the training data. This can be difficult in practice. For example, in the online review domain, most reviewers （authors） only write a few reviews, which are not enough to serve as the training data. Second, the learned classifier cannot be applied to authors whose documents have not been used in training. In this article, we propose a novel solution to deal with the two problems. The core idea is that instead of learning in the original document space, we transform it to a similarity space. In the similarity space, the learning is able to naturally tackle the issues. Our experiment results based on online reviews and reviewers show that the proposed method outperforms the state-of-the-art supervised and unsupervised baseline methods significantly.

关键词： authorship attribution supervised learning similarity space

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Cosmological constraints from noisy convergence maps through deep learning

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Physical Review D 2018年第12期98卷 123518-123518页

作者： Janis Fluri Tomasz Kacprzak Alexandre Refregier Adam Amara Aurelien Lucchi Thomas Hofmann Data Analytics Lab Department of Computer Science ETH Zurich Switzerland

Deep learning is a powerful analysis technique that has recently been proposed as a method to constrain cosmological parameters from weak lensing mass maps. Because of its ability to learn relevant features from the data, it is able to extract more information from the mass maps than the commonly used power spectrum, and thus achieve better precision for cosmological parameter measurement. We explore the advantage of convolutional neural networks over the power spectrum for varying levels of shape noise and different smoothing scales applied to the maps. We compare the cosmological constraints from the two methods in the ΩM−σ8 plane for sets of 400 deg2 convergence maps. We find that, for a shape noise level corresponding to 8.53 galaxies/arcmin2 and the smoothing scale of σs=2.34 arcmin, the network is able to generate 45% tighter constraints. For a smaller smoothing scale of σs=1.17 the improvement can reach ∼50%, while for a larger smoothing scale of σs=5.85, the improvement decreases to 19%. The advantage generally decreases when the noise level and smoothing scales increase. We present a new training strategy to train the neural network with noisy data, as well as considerations for practical applications of the deep learning approach.

关键词： Cosmological parameters Gravitational lenses

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Analysing the Twitter Sentiments in COVID-19 Using Machine Learning Algorithms

Analysing the Twitter Sentiments in COVID-19 Using Machine L...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Katarya, Rahul Nath, G Amar Singhal, Divit Shukla, Aryaman Delhi Technological University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science Engineering New Delhi India

ISBN: (纸本)9781665495295

The coronavirus emanated in Wuhan city of China, in the last month of 2019 and was even announced as a global threat. Social media could be an utterly noteworthy supply of facts during a time of crisis. User-generated texts yield perception into users' minds withinside the direction of such times, giving us insights into their critiques in addition to moods. This venture examines Twitter messages (tweets) regarding people's sentiment on the unconventional coronavirus. The essential aim of sentiment evaluation is the origin of human emotion from messages or tweets. This venture is geared toward using numerous gadgets studying type algorithms to expect the people's reception of the worldwide pandemic by reading their tweets on Twitter. In the course of this paper, we are testing our dataset on five different classifiers, namely Random Forest, Logistic regression, Multinomial naive Bayes, K-nearest neighbor, and Support vector machines classifiers. Together with precision rankings and balanced accuracy rankings, metrics are offered to gauge the fulfilment of the numerous algorithms implemented. The K-Nearest Neighbor classifier has given the highest precision score while the Logistic Regression classifier gives the highest recall, F1, accuracy and balanced accuracy scores. © 2022 IEEE.

关键词： Coronavirus

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Machine Learning Techniques for Autism Spectrum Disorder: Current trends and future directions 4

Machine Learning Techniques for Autism Spectrum Disorder: Cu...

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4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Khan, Kainat Katarya, Rahul Delhi Technological University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665494144

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD. © 2023 IEEE.

关键词： Supervised learning

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Cosmological parameter estimation and inference using deep summaries

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Physical Review D 2021年第12期104卷 123526-123526页

作者： Janis Fluri Tomasz Kacprzak Alexandre Refregier Aurelien Lucchi Thomas Hofmann Data Analytics Lab Department of Computer Science ETH Zurich 8006 Switzerland

The ability to obtain reliable point estimates of model parameters is of crucial importance in many fields of physics. This is often a difficult task given that the observed data can have a very high number of dimensions. In order to address this problem, we propose a novel approach to construct parameter estimators with a quantifiable bias using an order expansion of highly compressed deep summary statistics of the observed data. These summary statistics are learned automatically using an information maximising loss. Given an observation, we further show how one can use the constructed estimators to obtain approximate Bayes computation (ABC) posterior estimates and their corresponding uncertainties that can be used for parameter inference using Gaussian process regression even if the likelihood is not tractable. We validate our method with an application to the problem of cosmological parameter inference of weak lensing mass maps. We show in that case that the constructed estimators are unbiased and have an almost optimal variance, while the posterior distribution obtained with the Gaussian process regression is close to the true posterior and performs better or equally well than comparable methods.

关键词： Cosmological parameters Dark energy Dark matter

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Evaluating and Explaining Natural Language Generation with GenX 2

Evaluating and Explaining Natural Language Generation with G...

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2nd Workshop on data science with Human-in-the-Loop: Language Advances, DaSH-LA 2021

作者： Duskin, Kayla Sharma, Shivam Yun, Ji Young Saldanha, Emily Arendt, Dustin Data Science and Analytics Group Pacific Northwest National Laboratory United States Department of Computer Science New Jersey Institute of Technology United States Visual Analytics Group Pacific Northwest National Laboratory United States

ISBN: (纸本)9781954085398

Current methods for evaluation of natural language generation models focus on measuring text quality but fail to probe the model creativity, i.e., its ability to generate novel but coherent text sequences not seen in the training corpus. We present the GenX tool which is designed to enable interactive exploration and explanation of natural language generation outputs with a focus on the detection of memorization. We demonstrate the tool on two domain-conditioned generation use cases — phishing emails and ACL abstracts. © 2021 Association for Computational Linguistics

关键词： Natural language processing systems

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A concurrent k-NN search algorithm for R-tree 15

A concurrent k-NN search algorithm for R-tree

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8th ACM COMPUTE INDIA Conference, Compute 2015

作者： Challa, Jagat Sesh Goyal, Poonam Nikhil, S. Balasubramaniam, Sundar Goyal, Navneet Advanced Data Analytics and Parallel Technologies Laboratory Department of Computer Science and Information Systems Birla Institute of Technology and Science - Pilani Pilani Campus Pilani India

ISBN: (纸本)9781450336505

K-nearest neighbor (k-NN) search is one of the commonly used query in database systems. It has its application in various domains like data mining, decision support systems, information retrieval, multimedia and spatial databases, etc. When k-NN search is performed over large data sets, spatial data indexing structures such as R-trees are commonly used to improve query efficiency. The best-first k-NN (BF-kNN) algorithm is the fastest known k-NN over R-trees. We present CBF-k NN, a concurrent BF-kNN for R-trees, which is the first concurrent version of k-NN we know of for R-trees. CBF-k NN uses one of the most efficient concurrent priority queues known as mound. CBF-k NN overcomes the concurrency limitations of priority queues by using a tree-parallel mode of execution. CBF-kNN has an estimated speedup of 0(p/k) for p threads. Experimental results on various real datasets show that the speedup in practice is close to this estimate. © 2015 ACM.

关键词： data mining

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