Computer Science and Engineering

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.

Shiv Nadar University

The attributed multiplex network is a set of attributed networks in which each network represents a different type of interaction between the same set of nodes. Individual networks are termed as layers or dimensions and network nodes are characterized by attribute vectors. Neighborhood, in general, refers to any dense connected subgraph. We refer neighborhood1 as subgraph induced on graph node and its neighbors. It is usually observed that majority of the nodes in multilayer networks are active only on small number of layers except some outliers [18]. However, node activity is not strictly correlated to the edges incident in a node. A node might be active at few layers with relatively large number of incident edges and at the same time, multi-active node might not have many links even on single layer. Moreover, each layer has distinct importance in the multiplex networks2 and the structure and size of neighborhood formed by these multiplex nodes are different on each layer. Nodes with different attributes come together on different layers in the attributed multiplex networks. This node and layer heterogeneity should be considered while identifying anomalous neighborhoods in the attributed multiplex networks. Thus, a measure is required to quantify the quality of neighborhoods formed by active nodes on different layers. Existing approaches do not consider heterogeneity among network layers and do quantify the structure of networks either separately for each layer or its aggregated network and ignore the attributes of nodes. In this work, we define a novel quality measure Multi-Normality which utilizes the structure and attributes together of each layer and detect attribute coherence in neighborhoods between layers. We also propose an algorithm exhausting multi-normality to identify anomalous neighborhoods in multiplex networks and is named as Anomaly Detection of Entity Neighborhoods in Multiplex Networks (ADENMN). We evaluate the effectiveness of the proposed algorithm in anomaly detection by comparing its performance with three existing baseline approaches including ADOMS, AMM and AGG+AD on five real-world attributed multiplex networks including Amazon, YouTube, Noordin top terrorist network, DBLP_C, and Aarhus. The results of experiments demonstrate that multi-normality outperforms baseline algorithms. © 2020 Association for Computing Machinery.

(SoE) School of Engineering

ACM International Conference Proceeding Series

Ranking and discovering anomalous neighborhoods in attributed multiplex networks

Humans generally spend most of their time indoors, therefore, having good Indoor Air Quality (IAQ) and its real time information is critical for maintaining human health and productivity. According to United States Environmental Protection Agency, indoor air even in centrally air-conditioned buildings is several times more polluted than outdoor air, primarily due to change in occupancy pattern, old or ill maintained ventilation systems, and cracks in buildings. In this work, we have proposed an Internet of Things (IoT) based Discrete Time Markov Chain (DTMC) model for analysis and forecasting of IAQ. The IoT architecture used for collecting IAQ data consists of sensing nodes deployed in different rooms of the University building. This sensed data is transferred and stored in IoT cloud and used to generate the IAQ state transition matrix and compute return periods for each state. The predicted and actual return periods have been compared and the accuracy of the proposed model is found to be satisfactory with a low average absolute prediction error of 4.75\%. © 2020 IEEE.

World University root node

2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings

An IoT-based Discrete Time Markov Chain Model for Analysis and Prediction of Indoor Air Quality Index

Ubiquitous computing blended with context awareness gives user the facility of "anywhere anytime" computing. Location based services represents a class of context aware computing. Involvement of location as the primary input in location based services triggered concerns for user's privacy. Most of the privacy work in domain of location based services relies on obfuscation strategy along with K anonymity. The proposed work acknowledges the idea of calculating value of K for K anonymity using context factors in fuzzy format. However, with increasing number of these fuzzy context factors resulting in more fuzzy rules, the system will tend to get slower. In order to address this issue, requirement is to reduce the size of rule base without hampering the performance much. Goal of the proposed work is to attain scalability and high performance for the above said system. Towards this, reduction of number of rules in the rule base, of fuzzy inference system has been done using Fuzzy C Means and Genetic Algorithm. Results of reduced rule base have been compared with the results of exhaustive rule base. It has been identified that number of rules can be reduced up to considerable extent with comparable performances and acceptable level of error. © 2020 Science and Information Organization.

Fulltext

International Journal of Advanced Computer Science and Applications

Soft computing for scalability in context aware location based services

—Machine Translation (MT) is a programmed conversion in which computer software is utilized to convert manuscripts from one Natural Language (like English) to a different Language (such as Hindi). To process any such conversion, through human or through automatic means, the conversion must be established such that it reinstate the complete sense of a manuscript from its base (source) linguistic into the target language. In this paper, the study of prevailing evaluation systems along with assessing their performance is achieved through the similarity metrics. Moreover, the authors have also presented an improved technique of translation employing features of Natural Language Processing and consequently, to acquire an enhanced and more accurate assessing Machine Translation system, a corpus is selected and the outcomes are compared with the prevailing methods. Besides this, two well-known systems such as Google and Bing decoders are selected to inquire and to assess the study of metrics called similarity metrics through Assessment of Text Essential Characteristics score. This is found to provide more accuracy than prevailing methods. Furthermore, evaluations are tested under various metrics systems like Jaccard similarity metrics, cosine similarity metrics, and sine metrics to deliver enhanced accuracy than prevailing methods.&nbsp;

International Journal of Modern Education and Computer Science

Performance Enhancement of Machine Translation Evaluation Systems for English – Hindi Language Pair

Bit error rate (BER) and channel capacity are two important metrics to assess the performance of free-space optical communication (FSOC) systems. Due to the fading of the optical signal owing to the atmospheric effects, these two quantities behave as random variables. Most of the studies in this direction have focused on the calculation of only the average of these quantities. However, since the complete information about a random variable is encoded in its distribution, it is more informative to examine the latter itself. In this work, the authors derive exact probability density function (PDF) expressions for the BER and the channel capacity for an arbitrary irradiance model. In particular, they investigate these exact results for log-normal, gamma-gamma, and K distributions. For the BER analysis, they focus on the binary phase shift keying and quadrature phase shift keying modulation schemes. The authors' analytical reults are found to be in conformity with Monte Carlo simulations. The exact PDFs of the BER and the channel capacity reveal that there are several instances when the average is unable to capture the actual behaviour of these quantities, and therefore one must be careful in drawing conclusions based on the first moment only. © The Institution of Engineering and Technology 2019

Physics

(SoNS) School of Natural Sciences

IET Communications

Exact distributions for bit error rate and channel capacity in free-space optical communication

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.

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

Cervical cancer affects 570,000 women globally and is among the most common causes of cancer-related deaths. Cervical cancer is caused due to the Human Papilloma Virus (HPV) which leads to abnormal growth of cells in the cervix region. Regular testing for HPV in women has helped reduce the death rate in developed countries. However, developing nations are still struggling to provide low-cost solutions due to the lack of affordable medical facilities. The skewed ratio of the oncologists to patients has also aggravated the problem. Motivated by the Deep Learning solutions in Bio-medical imaging, we propose a novel CervixNet methodology which performs image enhancement on cervigrams followed by Segmenting the Region of Interest (RoI) and then classifying the RoI to determine the appropriate treatment. For the classification task, a novel Hierarchical Convolutional Mixture of Experts (HCME) algorithm is proposed. HCME is capable of tackling the problem of overfitting, given that small datasets are an inherent problem in the field of biomedical imaging. Our proposed methodology has outperformed all the existing methodologies on publicly available Intel and Mobile-ODT Kaggle dataset giving an Accuracy of 96.77\% and kappa score of 0.951. Hence, the results obtained validate our approach to provide first level screening at a low cost. © 2019 IEEE.