Department of Computer Science & Engineering

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IIT Jodhpur

A high percentage of information that propagates through a social network is sourced from different exogenous sources. E.g., individuals may form their opinions about products based on their own experience or reading a product review, and then share that with their social network. This sharing then diffuses through the network, evolving as a combination of both network and external effects. Besides, different individuals (nodes in a social network) have different degrees of exposition to their external sources, as well. Modeling this influence of external sources is important in order to understand the diffusion process and predict future content sharing patterns. Recognizing this fusion of intrinsic (network) effect and exogenous (external) effect, this paper develops a novel fuzzy relative willingness (FRW) model. Leveraging a fuzzy set approach provides a way to handle the uncertainties arising within the human concept of willingness. We demonstrate that FRW is able to accurately identify both top-k most content producers and diffusion effect based on external influence. We also demonstrate that the fuzzy set theory provides a compelling framework to model uncertainties pertaining to the influence as well as the susceptibility of individuals for both network and exogenous effects. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

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Fuzzy relative willingness: Modeling influence of exogenous factors in driving information propagation through a social network

Preprocessing techniques form an important task in document image analysis. In structured documents like forms, cheques, etc., there is a predefined space called frame field/cell for the user to fill the entry. When the user is writing, the nonuniformity of inter-character spacing becomes an issue. Many times, the starting characters of the word are written with sparse spacing between the characters and then gradually with a more compact spacing so as to accommodate the word within the frame field. To deal with this variation in intra-word spacing, horizontal-scale correction is applied to the extracted form fields. The effectiveness of the system is proved by applying it as a preprocessing step in a recognition system proposed in (Almazán et al. in Pattern Anal Mach Intell 36(12):21552–2566, 2014 [2]). The recognition framework results in reduced error rates with this normalization. © 2020, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Cell Extraction and Horizontal-Scale Correction in Structured Documents

Since the advent of cloud computing, a huge number of data owners are outsourcing their sensitive data to be stored on to the cloud. To maintain privacy requirements, this sensitive data should be encrypted when it is stored on the cloud server which renders simple keyword based document retrieval schemes obsolete. In this paper, we propose a semantic multi-keyword ranked search scheme for document retrieval on cloud data that is encrypted. The proposed scheme returns not only the documents containing terms that match with our query terms but also some more documents that contain terms which are semantically similar to the query keywords. Our experimental result shows that our technique is more precise than TF-IDF/VSM(Vector Space Model) models that focus only keyword matching while simultaneously achieving faster retrieval times than other tree based TF-IDF/VSM approach as our algorithm runs on reduced dimensions. © 2020 IEEE.

2020 International Wireless Communications and Mobile Computing, IWCMC 2020

An Efficient LSI Based Multi-keyword Ranked Search Algorithm on Encrypted Data in Cloud Environment

In Alzheimer's disease (AD), insoluble Aβ42 peptide fragments self-aggregate and form oligomers and fibrils in the brain, causing neurotoxicity. Further, the presence of redox-active metal ions such as Cu2+ enhances the aggregation process through chelation with these Aβ42 aggregates as well as generation of Aβ42-mediated reactive oxygen species (ROS). Herein, we have adopted a bioinspired strategy to design and develop a multifunctional glycopeptide hybrid molecule (Glupep), which can serve as a potential AD therapeutic. This molecule consists of a natural metal-chelating tetrapeptide motif of human serum albumin (HSA), a β-sheet breaker peptide, and a sugar moiety for better bioavailability. We performed different biophysical and docking experiments, which revealed that Glupep not only associates with Aβ42 but also prevents its self-aggregation to form toxic oligomers and fibrils. Moreover, Glupep was also shown to sequester out Cu2+ from the Aβ-Cu2+ complex, reducing the ROS formation and toxicity. Besides, this study also revealed that Glupep could protect PC12-derived neurons from Aβ-Cu2+-mediated toxicity by reducing intracellular ROS generation and stabilizing the mitochondrial membrane potential. All these exciting features show Glupep to be a potent inhibitor of Aβ42-mediated multifaceted toxicity and a prospective therapeutic lead for AD. Copyright © 2020 American Chemical Society.

Department of Bioscience & Bioengineering

ACS Omega

Human Serum Albumin-Inspired Glycopeptide-Based Multifunctional Inhibitor of Amyloid-β Toxicity

The vehicular ad-hoc network (VANET) is used for communication between vehicles in the same vicinity and road-side-units (RSUs). Since they communicate over a public channel, a secure data transmission protocol is necessary to exchange confidential information. Moreover, the number of vehicles is increasing day-by-day and thus, the receiver gets myriad messages from nearby RSUs and vehicles. As a result, the verification time plays a major role in vehicular communication scheme. Further, providing anonymous communication between vehicles is also a challenging task. To overcome these concerns, in this paper, we propose an identity-based signature protocol using the batch verification concept for VANETs. This paper aims to decrease the computational time (during the communication), and the proposed scheme resists various security attacks, i.e., man-in-the-middle, impersonation, replay, Sybil, and password guessing. Moreover, the proposed protocol achieves better performance results (for computational time, communication overhead, and storage cost) on Raspberry Pi 3B+ compared to other existing data transmission schemes. © Springer Nature Switzerland AG 2020.

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

An Efficient Message Transmission and Verification Scheme for VANETs

The integration of learning architecture with SDN-based VANETs (SDVN) is beneficial for utilizing computing power by decoupling network management services from data transfer services. However, fast safety messages dissemination in a highly dynamic vehicular environment is a challenging and complex dilemma due to bi-directional traffic and the directional movement of vehicles. It is also challenging to get an effective solution against bottleneck situations and a reliable and fault-tolerant SDN network using clustering. So considering the features of adaptive learning, in this paper, we propose adaptive self-learning clustering algorithm with reinforcement routing in SDVN known as RL-SDVN. An Expectation-Maximization model is used to predict a vehicle's movement and further Q-learning model is used to route data packets, so that vehicles in the same cluster coordinate with each other to find optimum routes. We evaluate our experimental results by comparing our approach with the clustering and self-learning based schemes proposed in the past. The outcomes exhibit that the proposed scheme improved cluster stability and life-time of a cluster member vehicle with better performance in terms of low average transmission delay, and high throughput compared to the existing routing protocols used in this research. © 2020 IEEE.

Adaptive Reinforcement Routing in Software Defined Vehicular Networks

Approximate computing has in recent times found significant applications towards lowering power, area, and time requirements for arithmetic operations. Several works done in recent years have furthered approximate computing along these directions. In this work, we propose a new approximate adder that employs a carry prediction method. This allows parallel propagation of the carry allowing faster calculations. In addition to the basic adder design, we also propose a rectification logic which would enable higher accuracy for larger computations. Experimental results show that our adder produces results 91.2% faster than the conventional ripple-carry adder. In terms of accuracy, the addition of rectification logic to the basic design produces results that are more accurate than state-of-the-art adders like SARA [13] and BCSA [5] by 74%. © 2020 Association for Computing Machinery.

Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

An approximate carry estimating simultaneous adder with rectification

Let π be a family of graphs. In the classical π-Vertex Deletion problem, given a graph G and a positive integer k, the objective is to check whether there exists a subset S of at most k vertices such that G − S is in π. In this paper, we introduce the conflict free version of this classical problem, namely Conflict Free π-Vertex Deletion (CF-π-VD), and study this problem from the viewpoint of classical and parameterized complexity. In the CF-π-VD problem, given two graphs G and H on the same vertex set and a positive integer k, the objective is to determine whether there exists a set S⊆ V(G) , of size at most k, such that G − S is in π and H[S] is edgeless. Initiating a systematic study of these problems is one of the main conceptual contribution of this work. We obtain several results on the conflict free versions of several classical problems. Our first result shows that if π is characterized by a finite family of forbidden induced subgraphs then CF-π-VD is Fixed Parameter Tractable (FPT). Furthermore, we obtain improved algorithms for conflict free versions of several well studied problems. Next, we show that if π is characterized by a “well-behaved” infinite family of forbidden induced subgraphs, then CF-π-VD is W[1]-hard. Motivated by this hardness result, we consider the parameterized complexity of CF-π-VD when H is restricted to well studied families of graphs. In particular, we show that the conflict free version of several well-known problems such as Feedback Vertex Set, Odd Cycle Transversal, Chordal Vertex Deletion and Interval Vertex Deletion are FPT when H belongs to the families of d-degenerate graphs and nowhere dense graphs. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Theory of Computing Systems

Conflict Free Version of Covering Problems on Graphs: Classical and Parameterized

Reconstructing an indoor scene and generating a layout/floor plan in 3D or 2D is a widely known problem. Quite a few algorithms have been proposed in the literature recently. However, most of the existing methods either use RGB-D images, thus requiring a depth camera, or depend on panoramic photos with the assumption that there is little to no occlusion in the rooms. In this work, we proposed generation of layout using an RGB image captured using a simple mobile phone camera. We take advantage of Simultaneous Localization and Mapping (SLAM) to assess the 3D transformations required for layout generation. SLAM technology is built-in in recent mobile libraries such as ARCore by Google. Hence, the proposed method is fast and efficient, while giving the user freedom to generate layout by simply taking a few conventional photos, rather than relying on specialized depth hardware or occlusion-free panoramic photos. © 2020 IEEE.

Department of Mathematics

Proceedings - 2020 IEEE 6th International Conference on Multimedia Big Data, BigMM 2020

LayART: Generating indoor layout using ARCore Transformations

Gene ontology (GO) is a comprehensive resource for the properties of gene products and their relationships. A similarity measure can be defined between two gene products by utilizing GO, and the corresponding similarity score can be treated as a likelihood to interact between them physically. However, GO is being updated regularly by the addition of new terms and removal/merging of obsolete terms. Therefore, the similarity score of interaction may differ from one instance of GO to another. In this paper, we systematically study the impact of the continuous evolution of GO on the performance of similarity measures for the task of scoring confidence of protein–protein interactions (PPIs). We find that the performance of a similarity measure gets affected due to the continuous evolution of GO. We further observe that the degree of robustness of a similarity measure is highly influenced by the particular setting we consider. © 2020, Springer Nature Singapore Pte Ltd.