Abstract

In this paper, we address the problem of monocular 3D human reconstruction with an acute focus on the challenge of recovering person-specific facial geometry as well as suppressing surface noise, specifically addressing the issue of false geometrical variations caused by textural edges. Most of the existing state-of-the-art methods in this domain fail to address these challenges. More specifically, we propose to integrate facial and wrinkle map priors in a learning-based framework to improve the quality of full-body 3D reconstruction from monocular images. By incorporating facial prior, we recover person-specific identity unlike many of the existing methods which rely on parametric shape models. Similarly, the wrinkle map prior enables our network to alleviate the challenge of false geometrical variations caused by high-frequency textural details present in the input image. We evaluate our method on publicly available datasets & in-the-wild internet images with loose clothing and report superior performance both qualitatively and quantitatively when compared with SOTA methods

Abstract

Posts on Twitter allow users to express ideas and opinions very dynamically. This high volume of data provides relevant clues about the public judgment on a specific product, event, service, etc. While traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level (very positive, low negative, and so on) and cannot exploit the intensity information. Recently, the problem of irony detection in social media has been proven to be pervasive among research enthusiasts, posing a challenge to sentiment analysis systems. Moreover, the figurative use of language has received scarce attention from the computational linguistic research point of view. This paper proposes an architecture, the Experts Model, inspired by the standard Mixture of Experts (MoE) model. The key idea here is that each expert learns different sets of features from the feature vector, which helps in better irony detection (Ironic vs. Non-ironic - SemEval-2018 subtask A) and ironic type detection (Verbal irony with vs. without polarity contrast vs. Situational irony vs. Non-irony - SemEval-2018 subtask B) from the tweet. We compared our Experts Model’s results with baseline results along with the top five performers of SemEval-2018 Task-3, Ironic detection. The experimental results show that our proposed approach deals with the ironic detection problem and stands at the top-3 results.

Abstract

The need for automated fact-checking is getting prominent with every passing day as the spread of misinformation is swelling over the ever-increasing stream of online content. We focus on fine-grained labelling of factual information in tweets to facilitate better fact-checking systems capable of providing improved justifications. In this paper, we present a token-level annotation of factual claims in tweets from Indian Twitter. To deal with the multilingual variety of the Indian diaspora, we deal with tweets in English, Bengali, Hindi, and their codemixed variants. To the best of our knowledge, this dataset is first of kind, both in terms of labelling scheme as well as data sources.

Abstract

Grading student work is critical for assessing their understanding and providing necessary feedback. However, answer grading can become monotonous for teachers. On the standard ASAG data set, our system shows substantial improvements in classification disparity of correct and incorrect answers from a reference answer compared to baseline methods. Our supervised model (1) utilizes recent advances in semantic word embeddings and (2) implements ideas from one-shot learning methods, which are proven to work with minimal. We present experimental results from a model based on different approaches and demonstrates decent performance on standard benchmark dataset.

Abstract

Although the city of Hyderabad in Telangana, India lies in seismic zone II, low to medium intensity tremors that pose a serious concern towards safety of the built envi- ronment are not uncommon. One such series of tremors occurred during 13–20 October 2020, in the financial district of Hyderabad and created a panic situation due to perceivable shaking and jolts with loud sounds associ- ated with hydro-seismicity. To understand the safety of the city’s built environment, a study was conducted on low, medium and tall buildings using ground motions recorded at the International Institute of Information Technology (IIIT), Hyderabad, which is 2.3 km from the epicentre. The amplification of ground motion on the second floor of the Nilgiri Building in IIIT, Hyderabad was 1.2–2.3. The vibrations recorded on the ground floor of the Nilgiri Building were used to develop a site-speci- fic response spectrum. This was further used to obtain the peak responses of the considered buildings through response spectrum analysis. The present study suggests that the low-rise buildings, mid-rise buildings and non- structural elements in high-rise buildings are under threat in the case of high-intensity earthquakes.

Abstract

Domain Generalization (DG) requires a model to learn a hypothesis from multiple distributions that generalizes to an unseen distri- bution. Recent explorations show that, for neural networks, the choice of hyper-parameters and model architecture significantly affects DG performance, and making the right choice is non-trivial. In this paper, we show evidence suggesting that the models that perform better at DG, might be implicitly learning a low dimen- sional representation in the feature space. Furthermore, we take forward this idea and employ explicit feature learning to improve DG. To this end, we propose a DG specific supervised contrastive loss. We show how this performance improvement correlates to reduced dimensionality of the representation. Our work establishes new state-of-the-art on five different DG benchmarks, compared against over two dozen existing approaches in DomainBed. We show how this performance improvement correlates to reduced dimensionality of the representation

Abstract

The Internet of Things (IoT) relies heavily on exchange of data collected by nodes in a sensor network. This network data can be represented as a signal on a graph allowing tools from graph signal processing (GSP) to analyse it. Here, we utilize graph signal reconstruction algorithms to extrapolate missing sensor data from partially sampled network data. An important class of graph signal reconstruction algorithms operate by partitioning the graph vertices into local sets to achieve lower reconstruction errors and faster convergence. However, methods for identification of these local neighbourhoods have not been clearly studied in the literature. Here, we propose two flexible algorithms to generate local sets on a graph. These algorithms are based on the distance between the sampled and unsampled vertices. Our algorithms are competitive with the methods in literature while offering flexibility in the number of sampled vertices. We carry out simulation-based analysis of sampling strategies and proposed local set generation algorithms using local-set-based reconstruction algorithms.

Abstract

Greenhouse gases (GHGs) play an important role in controlling local air pollution as well as climate change. In this study, we retrieved column-averaged dry-air ( X ) mole fractions of carbon dioxide (CO 2 ), methane (CH 4 ), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier transform infrared spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this letter, the diurnal variation and the time series of daily averaged X CO 2 , X CH 4 , and X CO covering the period from December 2020 to May 2021 are analyzed. The maximum values of X CO 2 , X CH 4 , and X CO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb, respectively. Less diurnal but clear seasonal changes are observed during the study period. X CH 4 and X CO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient for the EM27/SUN retrieved X CH 4 and X CO, with the S5P/TROPOMI products, are 0.75 and 0.94, respectively.

Abstract

Evaluation of ill-posed problems like Intrinsic Image Decomposi- tion (IID) is challenging. IID involves decomposing an image into its constituent illumination-invariant Reflectance (R) and albedo- invariant Shading (S) components. Contemporary IID methods use Deep Learning models and require large datasets for training. The evaluation of IID is carried out on either synthetic Ground Truth images or sparsely annotated natural images. A scene can be split into reflectance and shading in multiple, valid ways. Comparison with one specific decomposition in the ground-truth images used by current IID evaluation metrics like LMSE, MSE, DSSIM, WHDR, SAW AP%, etc., is inadequate. Measuring R-S disentanglement is a better way to evaluate the quality of IID. Inspired by ML inter- pretability methods, we propose Concept Sensitivity Metrics (CSM) that directly measure disentanglement using sensitivity to relevant concepts. Activation vectors for albedo invariance and illumination invariance concepts are used for the IID problem. We evaluate and interpret three recent IID methods on our synthetic benchmark of controlled albedo and illumination invariance sets. We also compare our disentanglement score with existing IID evaluation metrics on both natural and synthetic scenes and report our observations. Our code and data are publicly available for reproducibility

Abstract

PERFORMANCE ANALYSIS OF 339 MLD SEWAGE TREATMENT PLANT AT AMBERPET – HYDERABAD. Veerannapet Santhosh Vishal1, Shaik Rehana2, N. Munilakshmi3, L Partha Praveen4 1- Research Scholar, International Institute of Information Technology, Gachibowli, Hyderbad, Telangana-500032. 2- 2- Assistant Professor, International Institute of Information Technology, Gachibowli, Hyderbad, Telangana - 500032. 3- 3- Assistant Professor, Department of Civil Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh – 517502. 4- 4- Research Scholar, Department of Civil Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh – 517502 * Corresponding author Email: santhoshvishal.v@research.iiit.ac.in Abstract Environmental and water resources have deteriorated due to rapid population growth, chemical industry effluents, agricultural practices, and climate changes. The primary cause of water contamination is due to sewage water pollution. Hence understanding and implementing an effective sewage treatment is necessary. Many cutting-edge techniques have been developed in recent years to increase the effectiveness of the removal of organic matter and nutrients by wastewater treatment plants (WWTP). To comprehend the effectiveness of wastewater treatment, the current study considered a sewage treatment plant (STP) located in Amberpet, Hyderabad. The capacity of the STP at Amberpet is 339 MLD (Million Litres per Day) and is evaluated by collecting 156 samples for 12 months (January 2018 – December 2018). An STP aims to minimize or remove organic debris, sediments, disease-causing organisms, and other pollutants in sewage water before disposing into the streams. The current study observed the removal efficiencies of the constituents Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD). This investigation assesses whether the effluents emitted into the river body are within the NRCD- set limitations (National River Conservative Directorate) as the treated sewage water is discharged into the Musi River (a tributary of Krishna Basin). The MSE values of different parameters like TSS, BOD, COD are 23.85, 28.54, 72.59. The RMSE values of TSS, BOD, COD are 4.88, 5.32, 8.52. The R2 values of TSS, BOD, COD are 0.95, 0.79, 0.90. Keywords: Wastewater Treatment Plant (WTTP), Biochemical Oxygen Demand, Chemical Oxygen Demand, Total suspended solids