Showing papers by "Birla Institute of Technology and Science published in 2019"

China and India lead in greening of the world through land-use management

Abstract: Satellite data show increasing leaf area of vegetation due to direct (human land-use management) and indirect factors (climate change, CO2 fertilization, nitrogen deposition, recovery from natural disturbances, etc.). Among these, climate change and CO2 fertilization effect seem to be the dominant drivers. However, recent satellite data (2000-2017) reveal a greening pattern that is strikingly prominent in China and India, and overlapping with croplands world-wide. China alone accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area. The greening in China is from forests (42%) and croplands (32%), but in India is mostly from croplands (82%) with minor contribution from forests (4.4%). China is engineering ambitious programs to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change. Food production in China and India has increased by over 35% since 2000 mostly due to increasing harvested area through multiple cropping facilitated by fertilizer use and surface/ground-water irrigation. Our results indicate that the direct factor is a key driver of the "Greening Earth", accounting for over a third, and likely more, of the observed net increase in green leaf area. They highlight the need for realistic representation of human land-use practices in Earth system models.

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Abstract: The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

Bile acid metabolites control TH17 and Treg cell differentiation

Abstract: Bile acids are abundant in the mammalian gut, where they undergo bacteria-mediated transformation to generate a large pool of bioactive molecules. Although bile acids are known to affect host metabolism, cancer progression and innate immunity, it is unknown whether they affect adaptive immune cells such as T helper cells that express IL-17a (TH17 cells) or regulatory T cells (Treg cells). Here we screen a library of bile acid metabolites and identify two distinct derivatives of lithocholic acid (LCA), 3-oxoLCA and isoalloLCA, as T cell regulators in mice. 3-OxoLCA inhibited the differentiation of TH17 cells by directly binding to the key transcription factor retinoid-related orphan receptor-γt (RORγt) and isoalloLCA increased the differentiation of Treg cells through the production of mitochondrial reactive oxygen species (mitoROS), which led to increased expression of FOXP3. The isoalloLCA-mediated enhancement of Treg cell differentiation required an intronic Foxp3 enhancer, the conserved noncoding sequence (CNS) 3; this represents a mode of action distinct from that of previously identified metabolites that increase Treg cell differentiation, which require CNS1. The administration of 3-oxoLCA and isoalloLCA to mice reduced TH17 cell differentiation and increased Treg cell differentiation, respectively, in the intestinal lamina propria. Our data suggest mechanisms through which bile acid metabolites control host immune responses, by directly modulating the balance of TH17 and Treg cells. Screening of a library of bile acid metabolites revealed two derivatives of lithocholic acid that act as regulators of T helper cells that express IL-17a and regulatory T cells, thus influencing host immune responses.

The UCSC Xena platform for public and private cancer genomics data visualization and interpretation

Abstract: UCSC Xena is a visual exploration resource for both public and private omics data, supported through the web-based Xena Browser and multiple turn-key Xena Hubs. This unique archecture allows researchers to view their own data securely, using private Xena Hubs, simultaneously visualizing large public cancer genomics datasets, including TCGA and the GDC. Data integration occurs only within the Xena Browser, keeping private data private. Xena supports virtually any functional genomics data, including SNVs, INDELs, large structural variants, CNV, expression, DNA methylation, ATAC-seq signals, and phenotypic annotations. Browser features include the Visual Spreadsheet, survival analyses, powerful filtering and subgrouping, statistical analyses, genomic signatures, and bookmarks. Xena differentiates itself from other genomics tools, including its predecessor, the UCSC Cancer Genomics Browser, by its ability to easily and securely view public and private data, its high performance, its broad data type support, and many unique features.

The potential of siRNA based drug delivery in respiratory disorders: Recent advances and progress

Abstract: Lung diseases are the leading cause of mortality worldwide. The currently available therapies are not sufficient, leading to the urgent need for new therapies with sustained anti-inflammatory effects. Small/short or silencing interfering RNA (siRNA) has potential therapeutic implications through post-transcriptional downregulation of the target gene expression. siRNA is essential in gene regulation, so is more favorable over other gene therapies due to its small size, high specificity, potency, and no or low immune response. In chronic respiratory diseases, local and targeted delivery of siRNA is achieved via inhalation. The effectual delivery can be attained by the generation of aerosols via inhalers and nebulizers, which overcomes anatomical barriers, alveolar macrophage clearance and mucociliary clearance. In this review, we discuss the different siRNA nanocarrier systems for chronic respiratory diseases, for safe and effective delivery. siRNA mediated pro-inflammatory gene or miRNA targeting approach can be a useful approach in combating chronic respiratory inflammatory conditions and thus providing sustained drug delivery, reduced therapeutic dose, and improved patient compliance. This review will be of high relevance to the formulation, biological and translational scientists working in the area of respiratory diseases.

Multifunctional nanocrystals for cancer therapy: a potential nanocarrier

Abstract: Nanotechnology-based drug delivery systems offer an unprecedented opportunity for tumor targeting. Nanocrystals are carrier-free crystalline nanosized solid drug particles. Due to high drug loading (as high as 100%), and being free of organic solvents or surfactants or polymers or solubilizing chemicals, nanocrystals have attracted great attention in the field of drug delivery for treatment of various cancers. Additionally, the hybrid or multifunctional nanocrystal has been extensively investigated for applications in experimental as well as clinical settings to improve delivery efficiency of therapeutic and diagnostic agents. This review gives an overview of recent advances and current status of nanocrystals, especially with respect to the method of preparations, physicochemical characterizations, in vitro/in vivo performance, scale-up techniques and applications in the field of drug delivery for different tumor targeting. Recently much attention has been given to multifunctional nanocrystals showing the capability to codeliver multiple components that target the drug delivery by surface functionalization, performing therapy as well as diagnosis. Preparative techniques like high-pressure homogenization, precipitation, and media milling have been known to show large-scale production of nanocrystals. High therapeutic applications of nanoparticles enable its administration through various routes like oral, parenteral, pulmonary, dermal, and ocular. Along with preparation and characterization, this review will dwell on the progress involved with multifunctional nanocrystals for cancer therapy and theranostics. Most available results in multifunctional nanocrystal targeting rely upon in vitro and animal models, which do not match the actual environment of the tumor in the body, which is one of the major obstacles. Other challenges faced when it comes to nanocrystals are scale-up and reproducibility. In addition, potential problems and possible future research directions for the advancement of newer techniques of multifunctional nanocrystals that make them highly suitable for tumor targeted are highlighted in this review.

Blockchain Applications for Industry 4.0 and Industrial IoT: A Review

Abstract: The potential of blockchain has been extensively discussed in the literature and media mainly in finance and payment industry. One relatively recent trend is at the enterprise-level, where blockchain serves as the infrastructure for internet security and immutability. Emerging application domains include Industry 4.0 and Industrial Internet of Things (IIoT). Therefore, in this paper, we comprehensively review existing blockchain applications in Industry 4.0 and IIoT settings. Specifically, we present the current research trends in each of the related industrial sectors, as well as successful commercial implementations of blockchain in these relevant sectors. We also discuss industry-specific challenges for the implementation of blockchain in each sector. Further, we present currently open issues in the adoption of the blockchain technology in Industry 4.0 and discuss newer application areas. We hope that our findings pave the way for empowering and facilitating research in this domain, and assist decision-makers in their blockchain adoption and investment in Industry 4.0 and IIoT space.

Combating Adversarial Misspellings with Robust Word Recognition

Abstract: To combat adversarial spelling mistakes, we propose placing a word recognition model in front of the downstream classifier. Our word recognition models build upon the RNN semi-character architecture, introducing several new backoff strategies for handling rare and unseen words. Trained to recognize words corrupted by random adds, drops, swaps, and keyboard mistakes, our method achieves 32% relative (and 3.3% absolute) error reduction over the vanilla semi-character model. Notably, our pipeline confers robustness on the downstream classifier, outperforming both adversarial training and off-the-shelf spell checkers. Against a BERT model fine-tuned for sentiment analysis, a single adversarially-chosen character attack lowers accuracy from 90.3% to 45.8%. Our defense restores accuracy to 75%. Surprisingly, better word recognition does not always entail greater robustness. Our analysis reveals that robustness also depends upon a quantity that we denote the sensitivity.

A comprehensive review on hybrid electric vehicles: architectures and components

Abstract: The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuel-efficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors’ role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability.

Blockchain in Smart Grids: A Review on Different Use Cases

Abstract: With the integration of Wireless Sensor Networks and the Internet of Things, the smart grid is being projected as a solution for the challenges regarding electricity supply in the future. However, security and privacy issues in the consumption and trading of electricity data pose serious challenges in the adoption of the smart grid. To address these challenges, blockchain technology is being researched for applicability in the smart grid. In this paper, important application areas of blockchain in the smart grid are discussed. One use case of each area is discussed in detail, suggesting a suitable blockchain architecture, a sample block structure and the potential blockchain technicalities employed in it. The blockchain can be used for peer-to-peer energy trading, where a credit-based payment scheme can enhance the energy trading process. Efficient data aggregation schemes based on the blockchain technology can be used to overcome the challenges related to privacy and security in the grid. Energy distribution systems can also use blockchain to remotely control energy flow to a particular area by monitoring the usage statistics of that area. Further, blockchain-based frameworks can also help in the diagnosis and maintenance of smart grid equipment. We also discuss several commercial implementations of blockchain in the smart grid. Finally, various challenges to be addressed for integrating these two technologies are discussed.

Dark sector interaction: a remedy of the tensions between CMB and LSS data

Abstract: The well-known tensions on the cosmological parameters $$H_0$$ and $$\sigma _8$$ within the $$\Lambda $$ CDM cosmology shown by the Planck-CMB and LSS data are possibly due to the systematics in the data or our ignorance of some new physics beyond the $$\Lambda $$ CDM model. In this letter, we focus on the second possibility, and investigate a minimal extension of the $$\Lambda $$ CDM model by allowing a coupling between its dark sector components (dark energy and dark matter). We analyze this scenario with Planck-CMB, KiDS and HST data, and find that the $$H_0$$ and $$\sigma _8$$ tensions disappear. In the joint analyses with Planck, HST and KiDS data, we find non-zero coupling in the dark sector up to 99% CL. Thus, we find a strong statistical support from the observational data for an interaction in the dark sector of the Universe while solving the $$H_0$$ and $$\sigma _8$$ tensions simultaneously.

The energetic brain – A review from students to students

Abstract: The past 20 years have resulted in unprecedented progress in understanding brain energy metabolism and its role in health and disease. In this review, which was initiated at the 14th International Society for Neurochemistry Advanced School, we address the basic concepts of brain energy metabolism and approach the question of why the brain has high energy expenditure. Our review illustrates that the vertebrate brain has a high need for energy because of the high number of neurons and the need to maintain a delicate interplay between energy metabolism, neurotransmission, and plasticity. Disturbances to the energetic balance, to mitochondria quality control or to glia-neuron metabolic interaction may lead to brain circuit malfunction or even severe disorders of the CNS. We cover neuronal energy consumption in neural transmission and basic ('housekeeping') cellular processes. Additionally, we describe the most common (glucose) and alternative sources of energy namely glutamate, lactate, ketone bodies, and medium chain fatty acids. We discuss the multifaceted role of non-neuronal cells in the transport of energy substrates from circulation (pericytes and astrocytes) and in the supply (astrocytes and microglia) and usage of different energy fuels. Finally, we address pathological consequences of disrupted energy homeostasis in the CNS.

Comparative Analyses of Circular Gate TFET and Heterojunction TFET for Dielectric-Modulated Label-Free Biosensing

Abstract: This paper compares circular gate (CG) tunnel field effect transistor (TFET) and uniform gate Heterojunction (HJ) TFET as label-free biosensors based on dielectric modulation. Neutral and charged biomolecules with different values of dielectric constant are considered. Sensitivities of partially filled nanogaps arising out of steric hindrance in both the biosensors for concave, convex, increasing and decreasing step profiles of biomolecules are compared. The effect of probe position on sensitivities of the two biosensors is reported for various cases. A status map is presented, plotting the sensitivities of some of the most significant works in applications of FET as label-free biosensors along with sensitivities of the proposed devices. CG TFET exhibits higher sensitivity than HJ TFET due to its non-uniform gate architecture. The sensitivities of the TFETs are highly dependent on the position of biomolecules (steric hindrance and probe position) with respect to the tunnel junction. A maximum sensitivity of $1.31\times 10^{8}$ ( $3.382\times 10^{6}$ ) is achieved for fully filled nanogap in CG TFET (HJ TFET) for dielectric constant 12.

Real-time Yoga recognition using deep learning

Abstract: An approach to accurately recognize various Yoga asanas using deep learning algorithms has been presented in this work. A dataset of six Yoga asanas (i.e. Bhujangasana, Padmasana, Shavasana, Tadasana, Trikonasana, and Vrikshasana) has been created using 15 individuals (ten males and five females) with a normal RGB webcam and is made publicly available. A hybrid deep learning model is proposed using convolutional neural network (CNN) and long short-term memory (LSTM) for Yoga recognition on real-time videos, where CNN layer is used to extract features from keypoints of each frame obtained from OpenPose and is followed by LSTM to give temporal predictions. To the best of our knowledge, this is the first study using an end-to-end deep learning pipeline to detect Yoga from videos. The system achieves a test accuracy of 99.04% on single frames and 99.38% accuracy after polling of predictions on 45 frames of the videos. Using a model with temporal data leverages the information from previous frames to give an accurate and robust result. We have also tested the system in real time for a different set of 12 persons (five males and seven females) and achieved 98.92% accuracy. Experimental results provide a qualitative assessment of the method as well as a comparison to the state-of-the-art.

Learning to Deceive with Attention-Based Explanations

Abstract: Attention mechanisms are ubiquitous components in neural architectures applied to natural language processing. In addition to yielding gains in predictive accuracy, attention weights are often claimed to confer interpretability, purportedly useful both for providing insights to practitioners and for explaining why a model makes its decisions to stakeholders. We call the latter use of attention mechanisms into question by demonstrating a simple method for training models to produce deceptive attention masks. Our method diminishes the total weight assigned to designated impermissible tokens, even when the models can be shown to nevertheless rely on these features to drive predictions. Across multiple models and tasks, our approach manipulates attention weights while paying surprisingly little cost in accuracy. Through a human study, we show that our manipulated attention-based explanations deceive people into thinking that predictions from a model biased against gender minorities do not rely on the gender. Consequently, our results cast doubt on attention's reliability as a tool for auditing algorithms in the context of fairness and accountability.

Fault diagnosis of rolling element bearing based on artificial neural network

Abstract: This paper proposes the expert system for accurate fault detection of bearing. The study is based upon advanced signal processing method as wavelet transform and artificial intelligence technique as artificial neural network (ANN) and K-nearest neighbor (KNN), for fault classification of bearing. An adaptive algorithm based on wavelet transform is used to extract the fault classifying features of the bearing from time domain signal. These features have been used as inputs to proposed ANN models and the same features have also been used for KNN. Dedicated experimental setup was used to perform the test upon the bearing. Single data set for four fault conditions of bearing is collected to train ANN and KNN. The processed and normalized data was trained by using backpropagation multilayer perceptron neural network. The results obtained from ANN are compared with KNN, ANN results proved to be highly effective for classification of multiple faults.

A Provably Secure and Lightweight Anonymous User Authenticated Session Key Exchange Scheme for Internet of Things Deployment

Abstract: With the ever increasing adoption rate of Internet-enabled devices [also known as Internet of Things (IoT) devices] in applications such as smart home, smart city, smart grid, and healthcare applications, we need to ensure the security and privacy of data and communications among these IoT devices and the underlying infrastructure. For example, an adversary can easily tamper with the information transmitted over a public channel, in the sense of modification, deletion, and fabrication of data-in-transit and data-in-storage. Time-critical IoT applications such as healthcare may demand the capability to support external parties (users) to securely access IoT data and services in real-time. This necessitates the design of a secure user authentication mechanism, which should also allow the user to achieve security and functionality features such as anonymity and un-traceability. In this paper, we propose a new lightweight anonymous user authenticated session key agreement scheme in the IoT environment. The proposed scheme uses three-factor authentication, namely a user’s smart card, password, and personal biometric information. The proposed scheme does not require the storing of user specific information at the gateway node. We then demonstrate the proposed scheme’s security using the broadly accepted real-or-random (ROR) model, Burrows–Abadi–Needham (BAN) logic, and automated validation of Internet security protocols and applications (AVISPAs) software simulation tool, as well as presenting an informal security analysis to demonstrate its other features. In addition, through our simulations, we demonstrate that the proposed scheme outperforms existing related user authentication schemes, in terms of its security and functionality features, and computation costs.

Lung Cancer Detection: A Deep Learning Approach

Abstract: We present an approach to detect lung cancer from CT scans using deep residual learning. We delineate a pipeline of preprocessing techniques to highlight lung regions vulnerable to cancer and extract features using UNet and ResNet models. The feature set is fed into multiple classifiers, viz. XGBoost and Random Forest, and the individual predictions are ensembled to predict the likelihood of a CT scan being cancerous. The accuracy achieved is 84% on LIDC-IRDI outperforming previous attempts.

Traversable wormholes and energy conditions with two different shape functions in f(R) gravity

Abstract: Traversable wormholes, tunnel-like structures introduced by Morris and Thorne [Am. J. Phys. 56 (1988) 395], have a significant role in connection of two different spacetimes or two different parts of the same spacetime. The characteristics of these wormholes depend upon the redshift and shape functions which are defined in terms of radial coordinate. In literature, several shape functions are defined and wormholes are studied in f(R) gravity with respect to these shape functions [F. S. N. Lobo and M. A. Oliveira, Phys. Rev. D 80 (2009) 104012; H. Saiedi and B. N. Esfahani, Mod. Phys. Lett. A 26 (2011) 1211; S. Bahamonde, M. Jamil, P. Pavlovic and M. Sossich, Phys. Rev. D 94 (2016) 044041]. In this paper, two shape functions (i) b(r) = r0log(r+1) log(r0+1) and (ii) b(r) = r0( r r0 )γ, 0 < γ < 1, are considered. The first shape function is newly defined, however, the second one is collected from the literature [M. Cataldo, L. Liempi and P. Rodriguez, Eur. Phys. J. C 77 (2017) 748]. The wormholes are investi...

compare-mt: A Tool for Holistic Comparison of Language Generation Systems

Abstract: In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation. The main goal of the tool is to give the user a high-level and coherent view of the salient differences between systems that can then be used to guide further analysis or system improvement. It implements a number of tools to do so, such as analysis of accuracy of generation of particular types of words, bucketed histograms of sentence accuracies or counts based on salient characteristics, and extraction of characteristic n-grams for each system. It also has a number of advanced features such as use of linguistic labels, source side data, or comparison of log likelihoods for probabilistic models, and also aims to be easily extensible by users to new types of analysis. compare-mt is a pure-Python open source package, that has already proven useful to generate analyses that have been used in our published papers. Demo Video: https://youtu.be/NyJEQT7t2CA