Aparna Nayak

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Arnab Bhattacharya

IIT Kanpur

Samit Chakrabarty

University of Leeds

Shahab Rezaeian, PhD in Epidemiology

Kermanshah University Of Medical Science

Viacheslav Kuleshov

Stockholm University

Alan F Geater

Prince of Songkla University

Mohamad Ivan Fanany

University of Indonesia

Carol Hargreaves

National University of Singapore

Alexander Essien Timothy

University of Calabar, Calabar, Nigeria.

Ronaldo Izzo

UTFPR - UNIVERSIDADE TECNOLOGICA FEDERAL DO PARANÁ

Ryan Demmer

Columbia University

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Papers by Aparna Nayak

Data Quality Assessment of Comma Separated Values Using Linked Data Approach

Lecture notes in business information processing, 2022

Parallel Implementation of Rotation Visual Cryptography on GPU using CUDA

Proceedings of the International Conference on Advances in Image Processing, 2017

Visual cryptography scheme (VCS), is a cryptography technique where visual information is encrypt... more Visual cryptography scheme (VCS), is a cryptography technique where visual information is encrypted in such a way that decryption can be fulfilled with human visual system by direct stacking of the encrypted shares. Rotation visual cryptography is advanced technique of VCS where direct stacking of shares will not uphold the original image. In the existing literature, encryption techniques of VCS take longer time that can be substantially reduced by using CUDA. The proposed method uses CUDA for computationally intensive independent tasks performed on pixels during the rotation VCS. Parallel implementation of VCS will be faster and can be adopted in many real time applications that requires image security. Results show that the proposed method is 40-50 times faster than the existing encryption techniques.

Traffic Surveillance Video Summarization for Detecting Traffic Rules Violators Using R-CNN

Advances in Intelligent Systems and Computing, 2018

Many a times violating traffic rules leads to accidents. Many countries have adopted systems invo... more Many a times violating traffic rules leads to accidents. Many countries have adopted systems involving surveillance cameras at accident zones. Monitoring each frame to detect the violators is unrealistic. Automation of this process is highly desirable for reliable and robust monitoring of traffic rules violations. With deep learning techniques on GPU, the violation detection can be automated and performed in real time on surveillance video. This paper proposes a novel technique to summarize the traffic surveillance videos that uses Faster Regions with Convolutions Neural Networks(R-CNN) to automatically detect violators. As the proof of concept, an attempt is made to implement the proposed method to detect the two-wheeler riders without helmet. Long duration videos can be summarized into very short video that includes details about only rules violators.

Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review

ArXiv, 2022

Advocates for Neuro-Symbolic AI (NeSy) assert that combining deep learning with symbolic reasonin... more Advocates for Neuro-Symbolic AI (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is inextricably linked to language, it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We conduct a structured review of studies implementing NeSy for NLP, challenges and future directions, and aim to answer the question of whether NeSy is indeed meeting its promises: reasoning, out-of-distribution generalization, interpretability, learning and reasoning from small data, and transferability to new domains. We examine the impact of knowledge representation, such as rules and semantic networks, language structure and relational structure, and whether implicit or explicit reasoning contributes to h...

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Linked Data Quality Assessment: A Survey

Data is of high quality if it is fit for its intended use in operations, decision-making, and pla... more Data is of high quality if it is fit for its intended use in operations, decision-making, and planning. There is a colossal amount of linked data available on the web. However, it is difficult to understand how well the linked data fits into the modeling tasks due to the defects present in the data. Faults emerged in the linked data, spreading far and wide, affecting all the services designed for it. Addressing linked data quality deficiencies requires identifying quality problems, quality assessment, and the refinement of data to improve its quality. This study aims to identify existing end-to-end frameworks for quality assessment and improvement of data quality. One important finding is that most of the work deals with only one aspect rather than a combined approach. Another finding is that most of the framework aims at solving problems related to DBpedia. Therefore, a standard scalable system is required that integrates the identification of quality issues, the evaluation, and the improvement of the linked data quality. This survey contributes to understanding the state of the art of data quality evaluation and data quality improvement. A solution based on ontology is also proposed to build an end-to-end system that analyzes quality violations' root causes.

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Extending R2RML to a source-independent mapping language for typed literals

Linked data is often generated from raw data with help of mapping languages. However, such techni... more Linked data is often generated from raw data with help of mapping languages. However, such techniques do not allow complex data transformations which either can be implemented as custom solutions or separated from the mapping process. In this paper, we propose an approach of separating complex data transformations from the mapping process that can be still reusable across the systems. Complex data transformations include the entailment of (i) language tag and (ii) datatype that are present at the data source. The proposed method also includes inferring missing datatype information. We extended R2RML-F to handle data transformations. Our approach is validated on the test cases specified by the RDF mapping language (RML). The proposed method considers data in the form of JSON, thus making the system interoperable and reusable.

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Can an ontology really support the development of more accurate data quality assessment models? : A survey

Data is considered to be of high quality if it satisfies the requirements of intended use. There ... more Data is considered to be of high quality if it satisfies the requirements of intended use. There is a colossal amount of linked data available on the web. However, it is difficult to know how well the linked data fit the modeling tasks due to the defects present in the data. Faults present in the linked data spread far and wide, affecting all the services designed for it. Addressing linked data quality deficiencies requires functions such as the identification of quality problems, quality assessment and the refinement of data to improve its quality. This study aims to identify existing end-to-end frameworks for quality assessment and improvement of data quality. One important finding is that most of the work deals with only one aspect rather than a combined approach. Another finding is that most of the framework aims at solving problems related to DBpedia. Therefore, a standard scalable system is required that integrates the identification of quality issues, the evaluation and the i...

Prediction Models for Indian Stock Market

Procedia Computer Science, 2016

Stock market price data is generated in huge volume and it changes every second. Stock market is ... more Stock market price data is generated in huge volume and it changes every second. Stock market is a complex and challenging system where people will either gain money or lose their entire life savings. In this work, an attempt is made for prediction of stock market trend. Two models are built one for daily prediction and the other one is for monthly prediction. Supervised machine learning algorithms are used to build the models. As part of the daily prediction model, historical prices are combined with sentiments. Up to 70% of accuracy is observed using supervised machine learning algorithms on daily prediction model. Monthly prediction model tries to evaluate whether there is any similarity between any two months trend. Evaluation proves that trend of one month is least correlated with the trend of another month.

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Stock market prediction: A big data approach

TENCON 2015 - 2015 IEEE Region 10 Conference, 2015

Growth and development of the economy is dependent on the banking system. Bad loans which are Non... more Growth and development of the economy is dependent on the banking system. Bad loans which are Non-Performing Assets (NPA) are the measure for assessing the financial health of the bank. It is very important to control NPA as it affects the profitability, and deteriorates the quality of assets of the bank. It is observed that there is a significant rise in the number of willful defaulters. Hence systematic identification, awareness and assessment of parameters is essential for early prediction of willful default behavior. The main objective of the paper is to identify exhaustive list of parameters essential for predicting whether the loan will become NPA and thereby willful default. This process includes understanding of existing system to check NPAs and identifying the critical parameters. Also propose a framework for NPA/Willful default identification. The framework classifies the data comprising of structured and unstructured parameters as NPA/Willful default or not. In order to select the best classification model in the framework an experimentation is conducted on loan dataset on big data platform. Since the loan data is structured, unstructured component is incorporated by generating synthetic data. The results indicate that neural network model gives best accuracy and hence considered in the framework.

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Data hiding in halftone images using mathematical morphology and conjugate ordered dithering

2014 International Conference on High Performance Computing and Applications (ICHPCA), 2014

Visual Cryptography (VC) is a cryptographic technique that allows visual information such as text... more Visual Cryptography (VC) is a cryptographic technique that allows visual information such as textual images, handwritten notes etc to be encrypted in such a way that the decryption can be performed using the human visual system i.e. eyes. Here we propose an algorithm for data hiding in halftone images. This algorithm uses a morphological operation and ordered dithering, and is a modified version of DHCOD[1].In this paper we focus on improving the security and robustness of shares in VC and generating more meaningful shares with respect to basic cryptographic scheme.

(Linked) Data Quality Assessment: An Ontological Approach

The effective functioning of data-intensive applications usually requires that the dataset should... more The effective functioning of data-intensive applications usually requires that the dataset should be of high quality. The quality depends on the task they will be used for. However, it is possible to identify task-independent data quality dimensions which are solely related to data themselves and can be extracted with the help of rule mining/pattern mining. In order to assess and improve data quality, we propose an ontological approach to report data quality violated triples. Our goal is to provide data stakeholders with a set of methods and techniques to guide them in assessing and improving data quality.

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Data Quality Assessment of Comma Separated Values Using Linked Data Approach

Lecture notes in business information processing, 2022

Parallel Implementation of Rotation Visual Cryptography on GPU using CUDA

Proceedings of the International Conference on Advances in Image Processing, 2017

Traffic Surveillance Video Summarization for Detecting Traffic Rules Violators Using R-CNN

Advances in Intelligent Systems and Computing, 2018

Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review

ArXiv, 2022

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Linked Data Quality Assessment: A Survey

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Extending R2RML to a source-independent mapping language for typed literals

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Can an ontology really support the development of more accurate data quality assessment models? : A survey

Prediction Models for Indian Stock Market

Procedia Computer Science, 2016

Download

Stock market prediction: A big data approach

TENCON 2015 - 2015 IEEE Region 10 Conference, 2015

Download

Data hiding in halftone images using mathematical morphology and conjugate ordered dithering

2014 International Conference on High Performance Computing and Applications (ICHPCA), 2014

(Linked) Data Quality Assessment: An Ontological Approach

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