Abstract

t. An attempt has been made to retrieve columnar density of methane (XCH4) over a suburban area of the Shadnagar in India from the high spatial and moderate spectral resolution (5 nm
0.5 nm) airborne visible/infrared imaging spectrometer next generation (AVIRIS-NG) data. The data collected were part of the joint airborne campaign by National Aeronautics Space Administration/Jet Propulsion Laboratory and Indian Space Research Organisation over several Indian sites during December 2015 to January 2016. The XCH4 was retrieved following the Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor retrieval algorithm and was compared with the ground-based Fourier transform infrared (FTIR) observations. FTIR measured CH4 transmittance was fitted against Fast Atmospheric Signature Code3 (FASCODE3) simulated transmittance, and found their residual lies within
1%, which shows a very good retrieval sensitivity of FTIR. Hence FTIR observations are used for the validation of AVIRIS-NG derived XCH4 and found in good agreement. In the present study, we utilized airborne imaging radiance data in the CH4 absorbing channel at 2.365
0.006 μm and nonabsorbing channels at 2.325
0.006 μm and 2.365
0.006 μm to compute atmospheric transmittance spectrum. Using a line-by-line radiative transfer algorithm, the XCH4 was estimated from AVIRIS-NG transmittance data. Prior to the application of the MODIS water vapor algorithm on AVIRIS-NG transmittance, it was also compared with the FASCODE3 simulated transmittance and found comparable. Study revealed that within the Shadnagar region, retrieved XCH4 varies little to moderate with an average of 9 × 1027 molecules cm−2 with the highest value of 12 × 1027 molecules cm−2 near the source region of the agriculture field. The study demonstrates the capability of AVIRIS-NG to quantify the column density of atmospheric species such as CH4 over a region

Abstract

In this paper, we present the study of Laban Movement Analysis (LMA) to understand basic human emotions from nonverbal human behaviors. While there are a lot of studies on understanding behavioral patterns based on natural language processing and speech processing applications, understanding emotions or behavior from non-verbal human motion is still a very challenging and unexplored field. LMA provides a rich overview of the scope of movement possibilities. These basic elements can be used for generating movement or for describing movement. They provide an inroad to understanding movement and for developing movement efficiency and expressiveness. Each human being combines these movement factors in his/her own unique way and organizes them to create phrases and relationships which reveal personal, artistic, or cultural style. In this work, we build a motion descriptor based on a deep understanding of Laban theory. The proposed descriptor builds up on previous works and encodes experiential features by using temporal windows. We present a more conceptually elaborate formulation of Laban theory and test it in a relatively new domain of behavioral research with applications in human-machine interaction. The recognition of affective human communication may be used to provide developers with a rich source of information for creating systems that are capable of interacting well with humans. We test our algorithm on UCLIC dataset which consists of body motions of 13 non-professional actors portraying angry, fear, happy and sad emotions. We achieve an accuracy of 87.30% on this dataset.

Abstract

This work targets Dance Style Recognition in videos as an application of Human Action Recognition. We propose a novel Spatio-Temporal Laban Feature descriptor (STLF) for dance style recognition based on Laban theory. Laban Movement Analysis has become increasingly popular as a language to describe, index and record human motion. We only exploit motion features and body-pose information without encoding the appearance. The model is tested on some action recognition benchmarks and ICD, a challenging dataset of YouTube dance videos. Unlike other works, where Laban based features have been used in constrained environments, with static camera, sensors and no background noise, we employ STLF on videos in unconstrained and natural settings. It is robust to camera jitter, zoom variations and other acquisition conditions and is computationally cheap. It performs comparable or better than the state-of-the-art.

Abstract

In this paper, we address the problem of dance style classification to classify Indian dance or any dance in general. We propose a 3-step deep learning pipeline. First, we extract 14 essential joint locations of the dancer from each video frame, this helps us to derive any body region location within the frame, we use this in the second step which forms the main part of our pipeline. Here, we divide the dancer into regions of important motion in each video frame. We then extract patches centered at these regions. Main discriminative motion is captured in these patches. We stack the features from all such patches of a frame into a single vector and form our hierarchical dance pose descriptor. Finally, in the third step, we build a high level representation of the dance video using the hierarchical descriptors and train it using a Recurrent Neural Network (RNN) for classification. Our novelty also lies in the way we use multiple representations for a single video. This helps us to: (1) Overcome the RNN limitation of learning small sequences over big sequences such as dance; (2) Extract more data from the available dataset for effective deep learning by training multiple representations. Our contributions in this paper are three-folds: (1) We provide a deep learning pipeline for classification of any form of dance; (2) We prove that a segmented representation of a dance video works well with sequence learning techniques for recognition purposes; (3) We extend and refine the ICD dataset and provide a new dataset for evaluation of dance. Our model performs comparable or better in some cases than the state-of-the-art on action recognition benchmarks. Keywords: human activity recognition, dance style recognition, joint-localization, pose-descriptor, CNN, RNN, ICD.

Abstract

Over the last decade, the volume of unannotated user-generated web content has skyrocketed but manually annotating data is costly in terms of time and resources. We leverage the advancements in Machine Learning to reduce these costs. We create a semantically searchable dance database with automatic annotation and retrieval. We use a pose estimation module to retrieve body pose and generate Labanotation over recorded videos. Though generic, it provides an essential application due to large amount of videos available online. Labanotation can be further exploited to generate ontology and is also very relevant for preservation and digitization of such resources. We also propose a semi-automatic annotation model which generates semantic annotations over any video archive using only 2-4 manually annotated clips. We experiment on two publicly available ballet datasets. High-level concepts such as ballet pose and steps are used to make the semantic library. These also act as descriptive meta-tags making the videos retrievable using a semantic text or video query.

Abstract

Edge computing has emerged as an effective solution for delay sensitive IoT applications. In the edge-cloud hierarchy, reliability and fault tolerance are important issues. This paper proposes a novel agent-based reliable and fault-tolerant hierarchical IoT-cloud architecture which can survive the failures of the edge servers. In the proposed architecture, the cloud is distributed over four levels (cloud-fog-mist-dew) based on the processing power and distance from the end IoT device. It makes the whole system reliable by replicating the data on the edge of the network. The proposed system is fault tolerant as it redirects the application on an alternate server. In case of a server’s failure, redirection is done at the best possible level in the hierarchy, based on delay-tolerance of the IoT application. The application keeps on working even if the system fails on any level, on cloud, fog or mist. The solution is proposed using mobile agents (MAs) on servers to share systems’ state and other important information with other agents in the hierarchy, to be used for application redirection in case of server’s failure. The proposed system is simulated using Matlab and results prove its efficiency. Index Terms— Edge Computing, Real-time IoT, Reliability, Fault Tolerance, Fault Localization, Cloud, Mobile Agent.

Abstract

A river water quality management model under average climatic conditions may not be able to account for the extreme risk of low water quality which is more prominent under an increase in river water temperature and altered river flows. A modeling framework is developed to assess the risk of river low water quality extremes by integrating a statistical downscaling model based on Canonical Correlation Analysis, risk quantification model based on Frank Archimedean Copula function and multiple logistic regression model integrated with a river water quality simulation model, QUAL2 K. The results reveal that the combination of predicted decrease in low flows of approximately 57% and increase in maximum river water temperatures of approximately 1.2
C has shown an increase of about 46% in risk of low water quality conditions for the future scenarios along Tunga-Bhadra River, India. The extreme risk of low water quality is observed to increase by 50.6% for the period 2020– 2040 when compared with the current extreme conditions of 4.5% and average risk conditions of about 3% for the period 1988–2005. The study captured the occurrence of extremes of low water quality with evidence of a strong link between climate and water quality impairment events.

Abstract

The popularity and applicability of mobile crowdsensing applications are continuously increasing due to the widespread of mobile devices and their sensing and processing capabilities. However, we need to offer appropriate incentives to the mobile users who contribute their resources and preserve their privacy. Blockchain technologies enable semi-anonymous multi-party interactions and can be utilized in crowdsensing applications to maintain the privacy of the mobile users while ensuring first-rate crowdsensed data. In this work, we propose to use blockchain technologies and smart contracts to orchestrate the interactions between mobile crowdsensing providers and mobile users for the case of spatial crowdsensing, where mobile users need to be at specific locations to perform the tasks. Smart contracts, by operating as processes that are executed on the blockchain, are used to preserve users’ privacy and make payments. Furthermore, for the assignment of the crowdsensing tasks to the mobile users, we design a truthful, cost-optimal auction that minimizes the payments from the crowdsensing providers to the mobile users. Extensive experimental results show that the proposed privacy preserving auction outperforms state-of-the-art proposals regarding cost by ten times for high numbers of mobile users and tasks.

Abstract

Nations have set-up Critical Information Infrastruc-ture (CII) protection centers to provide safety of organizations which are deemed to be critical for the nation. In this context,this paper proposes a framework to identify Critical Information Infrastructures (CIIs). This framework has been adopted to identify the banks which are critical for Indian economy. Previously,Reserve Bank of India, the central bank of the country, has announced three banks as systemically important banks. The proposed approach, in this paper, considers a publicly available data set publishing the number (volume) and the amount (value)of financial transactions in the Indian domestic market involving all the banks. The proposed approach finds the individual ranks of each participating bank based on these two parameters.Finally, rank aggregation method combines these two rank lists(based on the value and the volume) to generate a final rank of the banks. Several rank aggregation methods have been compared in this context.

Abstract

Internationally, the cooling degree-days (CDD) and heating degree-days (HDD) have been commonly used as indicators of cooling and heating load occurring due to outdoor temperature. These indicators have also applied for classification of climate zones as referred in ASHRAE Standard 169-2013. As of now various base temperatures has been used in the different part of the world. Additionally, the analysis for base temperature had been done in 1980 by ASHRAE. This study aims to determine the base temperature for cities in India. The simulation approach has been used to identify base temperature using Energy Signature and Performance line method for 60 cities of India. The US-DOE developed 16 reference buildings which have been used for eight climate zones of India. These eight climate zones have been classified using the methodology of ASHRAE Standard 169–2013 on weather data of Indian cities. It was observed that the cooling base temperature is varying from 6.8 °C to 28.6 °C for the large hotel and 8.7–28.1 °C for office buildings while the heating base temperature is ranging from 13.8 °C to 21.4 °C for the large hotel and 10.4–18.7 °C respectively for different cities. The estimated base temperature for cooling and heating is 18 °C for India.