Computer Science and Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

This paper proposes a novel technique for single image dehazing using adaptive nearest neighbor regularization to obtain a haze-free transmission map and then approximating the additional airlight component present in the hazy image. The proposed method relies on the intensity distribution in the small image patches of the image, exhibited from the Y channel of the YCbCr representation of the image, in order to preserve the texture information of the image. We substitute the commonly used soft matting technique in assessing the refined transmission map for haze removal, by adaptive nearest neighbor classifier. We assume that the actual color of the haze-free pixels in the image is approximated by a set of discrete colors. We discover the haze-free pixels using the Nearest-Neighbor (NN) regularization. Finally, unlike the state-of-the-art methods, we approximate the additional airlight present in the image patch and eliminate that to clear the haze, instead of estimating the transmission of the medium. The proposed nearest neighbor regularization technique automatically changes the patch size, which helps in dealing with the high depth region (e.g., sky region) of the image. We experimented on standard synthetic and real hazy image datasets and observed that, the proposed method outperforms the state-of-the-art, especially for images with sky regions. © 2020 Elsevier B.V.

World University root node

(SoE) School of Engineering

Neurocomputing

Single image dehazing by approximating and eliminating the additional airlight component

In spite of the recent advancements in the field of deep learning based techniques for facial expression recognition, the efficiency of the state-of-the-art recognition methods in the wild scenarios, remains a challenge. The main reason behind the less efforts made for handling wild scenarios is two-folds: very less and varying levels of cues available to identify the distinguishable patterns of features (spatial and temporal) and non-availability of a big dataset to train a deep learning model. Recently, a huge dataset called AffectNet is introduced in the literature providing enough base to apply a deep learning model to train. This paper proposes an efficient combination of hand crafted and deep learning features for facial expression recognition in the wild. We use facial landmark points as hand-crafted features and XceptionNet for the deep learned features. We experiment with XceptionNet and Densenet propose the use of XceptionNet as it performs better compared to DenseNet, when applied on wild scenarios. The proposed fusion of the hand-crafted and XceptionNet features outperforms the state-of-the-art methods for facial expression recognition in the wild. © 2020 Elsevier B.V.

Cognitive Systems Research

Facial expression recognition in the wild, by fusion of deep learnt and hand-crafted features

Millions of people get injured, disabled or die in automotive accidents each year. Knowledge about the type of road accident is invaluable to the emergency medical services providers for optimal planning and execution of the rescue operation. An IoT based system has been developed in this work to report the occurrence, location as well as the type of road accident. The system uses in-built sensors of passenger smartphone to detect and classify the accident as head-on collision, rollover or fall-off. The accuracy of the proposed system, which uses Naïve Bayes classifier for classification, has been evaluated using precision, recall, F1 score and ROC curve. © 2020 IEEE.

2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings

Modeling IoT Enabled Automotive System for Accident Detection and Classification

Additive Manufacturing (AM) is one of several technological breakthroughs in the world, and Nanotechnology is the science of manipulating matter on an atomic, molecular, and supramolecular scale. Integration of both these domains can create wide variety of materials for new and innovative applications. In certain applications related to the fabrication of intricate structure and complex shapes, nanomaterials can help ease the manufacturing process and enhancement in the properties. This also leads to the development of some new and potential applications in biological sciences, space sciences, agriculture, and medicine. With the approach of using nanomaterials in additive manufacturing, we can make the new technology accessible to the inaccessible and more sustainable by easing the processing of the materials. In the past few years, there have been a lot of advancements in the integration of nanomaterials in AM for various applications. This review briefly comprehends these recent research and future of nanomaterial in AM. © 2020 Elsevier Ltd

Current Opinion in Chemical Engineering

Recent developments of nanomaterial applications in additive manufacturing: a brief review

Blind Image Quality Assessment (BIQA) has been an enticing research problem in image processing, during the last few decades. In spite of the introduction of several BIQA algorithms, quantifying image quality without the help of a reference image still remains an unsolved problem. We propose a method for BIQA, combining Natural Scene Statistics (NSS) feature and Probabilistic Quality representation by a CNN. A certain number of features are considered for each image. We also propose to increase the NSS feature set alongside with the same CNN architecture and compare its results accordingly. Support Vector Machine (SVM) regression is applied on these features to get a quality score for that particular image. The results obtained by applying the proposed quality score on benchmark datasets, show the effectiveness of the proposed quality metric compared to the state-of-the-art metrics. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Multimedia Tools and Applications

Blind image quality assessment using a combination of statistical features and CNN

Background: Automatic Machine Translation (AMT) Evaluation Metrics have become popular in the Machine Translation Community in recent times. This is because of the popularity of Machine Translation engines and Machine Translation as a field itself. Translator is a very important tool to break barriers between communities especially in countries like India, where people speak 22 different languages and their many variations. With the onset of Machine Translation engines, there is a need for a system that evaluates how well these are performing. This is where machine translation evaluation enters. Objective: This paper discusses the importance of Automatic Machine Translation Evaluation and compares various Machine Translation Evaluation metrics by performing Statistical Analysis on various metrics and human evaluations to find out which metric has the highest correlation with human scores. Methods: The correlation between the Automatic and Human Evaluation Scores and the correlation between the five Automatic evaluation scores are examined at the sentence level. Moreover, a hypothesis is set up and p-values are calculated to find out how significant these correlations are. Results: The results of the statistical analysis of the scores of various metrics and human scores are shown in the form of graphs to see the trend of the correlation between the scores of Automatic Machine Translation Evaluation metrics and human scores. Conclusion: Out of the five metrics considered for the study, METEOR shows the highest correlation with human scores as compared to the other metrics. © 2020 Bentham Science Publishers.

Recent Advances in Computer Science and Communications

Statistical analysis of machine translation evaluation systems for English-Hindi language pair

Communication using mediums like video and audio is essential for a lot of professions. In this paper, interaction with realtime audio transmission is looked upon using the tools in the domains of IoT and machine learning. Two transport layer protocols-TCP and UDP are examined for audio transmission quality. Further, different RNN models are examined for their efficiency in predicting music and being used as a substitute in case of loss of packets during transmission. © 2020 The Twenty-Fifth AAAI/SIGAI Doctoral Consortium (AAAI-20). All Rights Reserved.

AAAI 2020 - 34th AAAI Conference on Artificial Intelligence

MUSIC COLLAB: An IoT and ML Based Solution for Remote Music Collaboration

We introduce the notion of TMPS: Ticket-Mediated Password Strengthening, a technique for allowing users to derive keys from passwords while imposing a strict limit on the number of guesses of their password any attacker can make, and strongly protecting the users’ privacy. We describe the security requirements of TMPS, and then a set of efficient and practical protocols to implement a TMPS scheme, requiring only hash functions, CCA2-secure encryption, and blind signatures. We provide several variant protocols, including an offline symmetric-only protocol that uses a local trusted computing environment, and online variants that use group signatures or stronger trust assumptions instead of blind signatures. We formalize the security of our scheme by defining an ideal functionality in the Universal Composability (UC) framework, and by providing game-based definitions of security. We prove that our protocol realizes the ideal functionality in the random oracle model (ROM) under adaptive corruptions with erasures, and prove that security with respect to the ideal/real definition implies security with respect to the game-based definitions. © Springer Nature Switzerland AG 2020.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

TMPS: Ticket-mediated password strengthening

The widespread deployment of multi-biometrics to authenticate users prompts the need for biometric systems with high recognition performance. Further, the biometric data, once leaked or stolen, remains compromised forever. Hence biometric security is of utmost importance. Existing biometric template protection schemes either degrade the recognition performance or they have issues with security and speed. We propose a cancelable multi-biometric authentication approach where a novel bit-wise encryption scheme transforms a biometric template to a protected template using a secret key generated from another biometric template. It fully preserves the number of bit-errors in the original and the protected template to ensure recognition performance equivalent to the performance of the unprotected systems. We introduce Algorithm I and Algorithm II for bit-wise encryption; both are defined over cryptographic-primitives- block cipher based encryption and keyed-hash function. We profile these algorithms on various hardware architectures to calculate the efficiency in terms of the time taken during enrolment and authentication phase. For Algorithm II, we observe that a 3.3 GHz desktop architecture takes about 18 milliseconds on an average of over 200 runs to authenticate a user. Additionally, we provide mathematical proof to show that the proposed scheme guarantees secrecy and irreversibility. The results of comparisons with the existing biometric template protection schemes on the various face and iris databases show that the proposed work provides significantly good recognition performance and efficiency, while it achieves high security. Finally, the bit-wise encryption scheme can be built over the commercial-off-the-shelf systems to achieve security with equivalent high performance. © 2005-2012 IEEE.

IEEE Transactions on Information Forensics and Security

Cancelable Multi-Biometric Approach Using Fuzzy Extractor and Novel Bit-Wise Encryption

In recent years, orientation and navigation technologies and support for people with visually impaired disabilities have increased. This paper proposes a model for the detection of objects for visually impaired individuals. We have developed a module for blind people to identify and match items scanned to a list of objects, such as currency notes, person and bottle, and the angle and distance of the object being detected from the camera. The model is based on image recognition from a database of objects. The results of the image recognition shall be communicated to the user by system audio feedback. © 2020 IEEE.