(SoE) School of 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

Increasing threat of password leakage from compromised password hashes demands a resource consuming password-hashing algorithm to prevent the precomputation of the password hashes. A class of password-hashing schemes (PHS) provides such a defense by making the design Memory hard. This ensures that any reduction in the memory consumed by the algorithm leads to an exponential increase in its runtime. The security offered by a memory-hard PHS design is measured in terms of its time–memory trade-off (TMTO) defense. Another important measure for a good PHS is its efficiency in utilizing all the available memory as quickly as possible, and fast running time when more than the required memory is available. In this work, we present a simple technique to analyze TMTO for a password-hashing scheme which can be represented as a directed acyclic graph (DAG). The nodes of the DAG correspond to the storage required by the algorithm and the edges correspond to the flow of the execution. Our proposed technique provides expected runtimes at varied levels of available storage utilizing the DAG representation of the algorithm. We show the effectiveness of our proposed technique by applying it on three designs from the “Password Hashing Competition" (PHC)—Argon2-Version 1.2.1 (the PHC winner), Catena-Version 3.2 and Rig-Version 2. Our analysis shows that Argon2i is not providing expected memory hardness which is also highlighted in a recent work by Corrigan-Gibbs et al. We analyze these PHS for performance under various settings of time and memory complexities. Our experimental results show (i) simple DAGs for PHS are efficient but not memory hard, (ii) complex DAGs for PHS are memory hard but less efficient, and (iii) combination of two simple graphs in the representation of a DAG for PHS achieves both memory hardness and efficiency. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

World University root node

Computer Science and Engineering

International Journal of Information Security

Cryptanalytic time–memory trade-off for password hashing schemes

The problem of language translation has prevailed in society for so long. However, up to some extent the problem is being reduced by the online available machine translation systems like Google, Bing, Babelfish, etc. But with the emergence of these Machine Translation Systems, there arises the problem of their validation. Can we trust on such translation systems blindly? Is there no scope of improvement? Are these Machine Translation systems not prone to errors? The answer to all these questions is No. So, for this purpose, we need a mechanism that can test or assess these Machine Translation systems. In this paper, we have proposed an algorithm that will evaluate such Machine Translation systems. Our algorithm is being compared with a very well-known BLEU algorithm that works very well for non-Indian languages. The accuracy of the designed algorithm is evaluated using the standard datasets like Tides and EMILLE. © BEIESP.

International Journal of Innovative Technology and Exploring Engineering

A better gauging model for the evaluation of automatic machine translation of english – hindi language

Traditional information spread and activation models on social networks, fail to take user interests towards specific content (topics) into account. To this, we propose a predictive topical spreading activation model (TopSPA). Following cues from the well-known spreading activation (SPA) model, we design the TopSPA algorithm to include the affinity of users to given topics. TopSPA utilizes the social connection structures of users, along with their topic affinities, to model the information flow. We use topic-based skew in energy seeding and energy propagation resistance in the network to form our overall information diffusion model. We empirically validate our model on multiple social event datasets on Twitter, predicting information diffusion over the social graph with a high accuracy. © 2019, Springer Nature Switzerland AG.

Studies in Computational Intelligence

Modeling topical information diffusion over microblog networks

We perform a first-of-its-kind characterization of topical homophily - familiarity co-occurring with topic-participation similarity of user pairs - by correlating topic participation similarity and degree of familiarity of users on Twitter. We quantify similarity between a user pair by measuring their distribution of participation in topics, wherein topics are defined as clusters of hashtags formed using semantically related user-generated content. We examine the topic participation similarity of users against different degrees of familiarity: edges, shared neighbors, and structural communities. We provide varying relaxation in identifying topics, and characterize the correlation of topical similarity with the degree of familiarity over the range of relaxation. We empirically substantiate the characteristics of topical homophily, over the varying relaxation of identified topics. We empirically show that homophily grows linearly with increase of familiarity, reaches a peak, and subsequently falls, indicating that, familiarity correlates with similarity up to a point, beyond which, similarity occurs for other reasons. © Springer Nature Switzerland AG 2019.

Assessing topical homophily on twitter

A large number of advantages are offered by multi level inverters and hence a number of topologies are proposed over the years. There has been an evolution of new topologies with reduced number of switching devices. Not a single topology is enough for all the applications, so these topologies are developed with orientation based upon the application. Some topologies suites one application but do not suite well to the other application. The paper presents a comparative modelling analysis of a number of multilevel inverter topologies with reduced device count with the purpose of proposing the better options out of reduced device count topologies for economic optimization of grid interface inverter. © 2019 IEEE.

Electrical Engineering

2019 6th International Conference on Signal Processing and Integrated Networks, SPIN 2019

A Comparative Modeling Analysis of Optimized Multilevel Inverter Topologies with Reduced Device Count for SPV and Wind Integration

This paper proposes an idea of employing sparse reconstruction-based technique for thermal imaging defect detection. The implementation of the reconstruction technique is tested on a carbon fiber reinforced polymer test piece with artificially drilled defects and the test results are compared with the established cross correlation method. The two processes are compared in terms of defect detectability, their SNR variation with defect depth and their computation complexity. When compared with cross correlation algorithm, the technique is expected to solve memory space problems by compressing all information from large cross-correlated pulse video into a single reconstructed image as an output. Furthermore, in existing cross correlation methods, the pulse peak time shifts with defect depth. Hence, defect quantification algorithms, such as SNR calculation, require multiple frame analysis. Such algorithms are comparatively simplified in sparse reconstruction technique. This paper explores sparse reconstruction algorithm for resolving close-spaced defects. This paper further describes cross-validation method for optimization of a user parameter in sparse reconstruction method. © 1963-2012 IEEE.

IEEE Transactions on Instrumentation and Measurement

Sparse Reconstruction-Based Thermal Imaging for Defect Detection

In this paper a methodology is proposed for multiple distributed generation (DG) unit placement as active power source and as reactive power source in succession. The proposed work uses analytical method based upon the exact loss formula for determining optimum sizes for DG as active power source and capacitor as reactive power source. The optimization is done for loss minimization in the system. The methodology is tested for IEEE 33 bus system and results are validated with previous researches. The methodology is then extended for placement of multiple units in succession. The methodology is proved to have minimized losses to a large extent and improved voltage profile of distribution network. © 2019, Institute of Advanced Scientific Research, Inc. All rights reserved.

Journal of Advanced Research in Dynamical and Control Systems

Multiple mix of distributed generator and capacitor optimal placement in distribution system for loss reduction

Constant rate of strain (CRS) consolidation test is a fast test method to characterize consolidation behavior of fine grained soils. In this test method, the test specimen is deformed at a constant rate of deformation, and pore water pressure at the base of the test specimen and axial reaction load are measured at successive interval during the test. This test method has several advantages over traditional incremental loading consolidation test. In the present research, a series of CRS consolidation tests have been performed on reconstituted samples of eight different clayey soils with different plasticity indices. The suitable strain rate at which the test is to be performed has been decided considering the criterion given in the literature. The test data obtained from these tests have been analyzed through the method of interpretation given in ASTM: D4186-06 (Standard Test Method for One-Dimensional Consolidation Properties ofSaturated Cohesive Soils Using Controlled-Strain Loading. 2008). The present study shows that the existing method of interpretation yields unreliable results for a significant duration at early stage of the test despite of performing the test at recommended strain rate. Analysis of present experimental data and some of the available recommendations to select suitable strain rate indicates that the theory for CRS consolidation which forms the basis for the existing method of interpretation is unable to describe rapid evolution of pore-water pressure at early stage of the test. Because of this, the existing method of interpretation fails to interpret the consolidation parameters accurately throughout the test. © 2019, Springer International Publishing AG, part of Springer Nature.

Civil Engineering

Sustainable Civil Infrastructures

Issues in Interpretation of Constant Rate of Strain Consolidation Test Data

Building and expansion of an efficient transportation network are essential for urban city advancement. However, tracking road development in an area is not an easy task as city planners do not always have access to credible information. A road network mapping framework is proposed which uses a random forest model for pixel-wise road segmentation. Road detection is followed by computer vision post-processing steps including Connected Component Analysis (CCA) and Hough Lines method for network extraction from high-resolution aerial images. The custom dataset used consists of images collected from an urban settlement in India. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Proceedings of SPIE - The International Society for Optical Engineering

Road network mapping from aerial images

Monitoring tree cover in an area plays an important role in a wide range of applications and advances in UAV technology has made it feasible to capture high resolution imagery which can be used for this purpose. In this study, we adopt a state of the art object detector Mask Region-based CNN (Mask R-CNN1), through transfer learning, for the task of tree segmentation and counting. One bottleneck for the proposed task is the huge amount of data required if the model is required to be scalable to various different geographical regions. Towards this end, we explore the use of a sampling technique based on Gist descriptors and Gabor filtering in order to minimize the amount of training data required for obtaining excellent model performance across images with varied geographical features. This study was conducted across four regions in India, each having a different geographical landscape. We captured a total of 2357 images across all four regions. The final training dataset comprised of 48 images (sampled using the aforementioned method), representative of the entire dataset. Our method demonstrates high quality and scalable tree detection results. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.