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

Many domain specific decisions of our day-to-day lives are dependent on weather. Weather and its variables have a significant impact on crop gro wth stages and crop producti vity.Weather forecast service has been started by In dia Meteorological Department (IMD) from the year 1945. To help the farming community, IMD has started pro viding district level agro-meteorological advisory based on the district-level Medium Range Forecast (MRF) f rom the year2008 fo r all districts through 130 agro-meteorological field units (AMFUs). Based on the MRF for five days, each AMFU is providing crop-s pecific agromet advice for the districts covered by that AMFU. To impro ve the effectiveness, IMD has started block level MRF on an experimental basis from the year 2015. Based on block level MRF, IMD is planning to provide block-level agromet advisory based on the block-level MRF. In such a situation, agromet scientisst haveto prepare agromet advisory for several blocks and crops at the earliest after receiving the block level MRF which will be disseminated to the farmers and stakeholders. In this context, research efforts have to be made to develop efficient system to enable agromet scientists to prepare the block-level agromet advice based on the block-level MRF. Normally, it can be observed that the weather and cro ps of nearby blocks may not vary significantly. S o, there is an opportunity to reuse the agromet advice prepared for one block to the nearby blocks. In this paper, we have pro posed an IT- based framewo rk to enable the agromet scientists to prepare the block level agromet advisoryby exploitingthe fact that the agromet advice which has been prepared for one block can be reused for another block if the weather condition and the crop is the same. The proposed framework pro vides the scope to exploit the reuse of agromet advisory prepared for one block toother blocks and could improve the efficiency of agromet scientists for the preparation of block-level agromet advisory based on the MRF

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

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

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 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

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

The present work is a multi-temporal satellite based spatial dynamic study of an important coastal habitat, the Pichavaram mangrove ecosystem, over a period of 15 years. The study discusses the importance and the status of mangroves on both global and regional scales. Maximum likelihood estimate method of supervised classification technique has been used to classify the land use-land cover changes in the Pichavaram Reserve Forest, Killai Reserve Forest and Pichavaram Extension. The status of the classes has been monitored using Landsat ETM+ of 2000, 2006, 2011, and OLI of 2016 satellite imageries. The total area of the Pichavaram mangrove showed a net increase of approximately 11.41% of the total study area within a span of 15 years (2000–2016)

Abstract

t. In addition to maximizing the revenue, retailers also aim at diversifying product offerings for facilitating sustainable revenue generation in the long run. Thus, it becomes a necessity for retailers to place appropriate itemsets in a limited k number of premium slots in retail stores for achieving the goals of revenue maximization and itemset diversification. In this regard, research efforts are being made to extract itemsets with high utility for maximizing the revenue, but they do not consider itemset diversification i.e., there could be duplicate (repetitive) items in the selected top-utility itemsets. Furthermore, given utility and support thresholds, the number of candidate itemsets of all sizes generated by existing utility mining approaches typically explodes. This leads to issues of memory and itemset retrieval times. In this paper, we present a framework and schemes for efficiently retrieving the top-utility itemsets of any given itemset size based on both revenue as well as the degree of diversification. Here, higher degree of diversification implies less duplicate items in the selected top-utility itemsets. The proposed schemes are based on efficiently determining and indexing the topλ high-utility and diversified itemsets. Experiments with a real dataset show the overall effectiveness and scalability of the proposed schemes in terms of execution time, revenue and degree of diversification w.r.t. a recent existing scheme.

Abstract

Post-capture control of the focus position of an image is a useful photographic tool. Changing the focus of a single image involves the complex task of simultaneously estimating the radiance and the defocus radius of all scene points. We introduce RefocusGAN, a deblurthen-reblur approach to single image refocusing. We train conditional adversarial networks for deblurring and refocusing using wide-aperture images created from light-fields. By appropriately conditioning our networks with a focus measure, an in-focus image and a refocus control parameter δ, we are able to achieve generic free-form refocusing over a single image.