Abstract

Code-mixing (CM) is a frequently observed phenomenon on social media platforms in mul- tilingual societies such as India. While the increase in code-mixed content on these plat- forms provides good amount of data for study- ing various aspects of code-mixing, the lack of automated text analysis tools makes such studies difficult. To overcome the same, tools such as language identifiers and parts of-speech (POS) taggers for analysing code-mixed data have been developed. One such tool is Named Entity Recognition (NER), an important Natu- ral Language Processing (NLP) task, which is not only a subtask of Information Extraction, but is also needed for downstream NLP tasks such as semantic role labeling. While entity extraction from social media data is generally difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete infor- mation. In this work, we present the first ever corpus for Kannada-English code-mixed social media data with the corresponding named en- tity tags for NER. We provide strong baselines with machine learning classification models such as CRF, Bi-LSTM, and Bi-LSTM-CRF on our corpus with word, character, and lexical features.

Abstract

We present a method to control the emotional prosody of Text to Speech (TTS) systems by us- ing phoneme-level intermediate features (pitch, energy, and duration) as levers. As a key idea, we propose Differential Scaling (DS) to dis- entangle features relating to affective prosody from those arising due to acoustics conditions and speaker identity. With thorough exper- imental studies, we show that the proposed method improves over the prior art in accu- rately emulating the desired emotions while retaining the naturalness of speech. We ex- tend the traditional evaluation of using indi- vidual sentences for a more complete evalua- tion of HCI systems. We present a novel ex- perimental setup by replacing an actor with a TTS system in offline and live conversa- tions. The emotion to be rendered is either predicted or manually assigned. The results show that the proposed method is strongly pre- ferred over the state-of-the-art TTS system and adds the much-coveted “human touch” in ma- chine dialogue. Audio samples for our exper- iments and the code are available at: https: //emtts.github.io/tts-demo/

Abstract

A tool Smart Glazing Simulator (SGLSim), has been developed to perform parametric simulation analysis of different window systems with several window-to-wall ratios and orientations to compute and compare the annual energy performance. The net annual energy performance of the building is based on the electricity consumption in heating, cooling, interior lighting, and appliances, along with the electricity generation by the photovoltaic (PV) glazing, which is used to evaluate the energy performance of smart glazing. Performing parametric energy simulations and calculating the net annual electricity consumption of different combinations requires building modeling and energy simulation expertise. A web-based parametric tool can assist the user in carrying out the desired studies without requiring extensive technical knowledge. A case study is prepared for India’s warm and humid climatic zone. This study examines the benefits of double pane semi-transparent photovoltaics (STPV) glazing, STPV glaz-ing with dynamic internal blind, and electrochromic (EC) glazing over other traditional glazing systems. The study shows that the optimal net annual electricity consumption in the case of STPV windows is 10–12% less than the optimal value obtained in a sim-ple glazing case. Additionally, the result suggested that glare-controlled interior blinds in the STPV window further reduce the net annual electricity consumption by up to 15% compared to conventional glazing. Similarly, installing the EC glazing reduces the yearly electricity consumption by up to 5% compared to standard glazing.

Abstract

We propose a novel flow synthesis based visual servoing framework enabling long-range obstacle avoidance for Micro Air Vehicles (MAV) flying amongst tall skyscrapers. Recent deep learning based frameworks use optical flow to do high-precision visual servoing. In this paper, we explore the question: can we design a surrogate flow for these high-precision visual-servoing methods, which leads to obstacle avoidance? We revisit the concept of saliency for identifying high-rise structures in/close to the line of attack amongst other competing skyscrapers and buildings as a collision obstacle. A synthesised flow is used to displace the salient object segmentation mask. This flow is so computed that the visual servoing controller maneuvers the MAV safely around the obstacle. In this approach, we use a multi-step Cross-Entropy Method (CEM) based servo control to achieve flow convergence, resulting in obstacle avoidance. We use this novel pipeline to successfully and persistently maneuver high-rises and reach the goal in simulated and photo-realistic real-world scenes. We conduct extensive experimentation and compare our approach with optical flow and short-range depth-based obstacle avoidance methods to demonstrate the proposed framework’s merit. Additional Visualisation can be found at https://sites.google.com/view/monocular-obstacle/home

Abstract

When the Indian government declared the frst lockdown on 25 March 2020 to control the increasing number of COVID-19 cases, people were forced to stay and work from home. The aim of this study is to quantify the impact of stay-at-home orders on residential Air Conditioning (AC) energy and household electricity consumption (excluding AC energy). This was done using monitored data from 380 homes in a group of fve buildings in Hyderabad, India. We gathered AC energy and household electricity consumption data at a 30-min interval for each home individually in April 2019 and April 2020. Descriptive and inferential statistical analysis was done on this data. To ofset the diference in temperatures for the month of April in 2019 and 2020, only those weekdays were selected where the average temperature in 2019 was same as the average temperature in 2020. The study establishes that the average number of hours the AC was used per day in each home increased in the range 4.90–7.45% depending on the temperature for the year 2020. Correspondingly, the overall AC consumption increased in the range 3.60–4.5%, however the daytime (8:00 AM to 8:00 PM) AC energy consumption increased in the range 22–26% and nighttime (8:00 PM to 8:00 AM) AC energy consumption decreased by 5–7% in the year 2020. The study showed a rise in household electricity consumption of about 15% for the entire day in the year 2020. The household electricity consumption increased during daytime by 22- 27.50% and 1.90- 6.6% during the nighttime. It was observed that the morning household electricity peak demand shifted from 7:00 AM in 2019 to 9:00 AM in 2020. Conversely, the evening peak demand shifted from 9:00 PM in 2019 to 7:00 PM in 2020. An additional peak was observed during afternoon hours in the lockdown.

Abstract

Modern autonomous vehicles (AVs) often rely on vision, LIDAR, and even radar-based simultaneous localization and mapping (SLAM) frameworks for precise localization and navigation. However, modern SLAM frameworks often lead to unacceptably high levels of drift (i.e., localization error) when AVs observe few visually distinct features or encounter occlusions due to dynamic obstacles. This paper argues that minimizing drift must be a key desiderata in AV motion planning, which requires an AV to take active control decisions to move towards feature-rich regions while also minimizing conventional control cost. To do so, we first introduce a novel data-driven perception module that observes LIDAR point clouds and estimates which features/regions an AV must navigate towards for drift minimization. Then, we introduce an interpretable model predictive controller (MPC) that moves an AV toward such feature-rich regions while avoiding visual occlusions and gracefully trading off drift and control cost. Our experiments on challenging, dynamic scenarios in the state-ofthe-art CARLA simulator indicate our method reduces drift up to 76.76% compared to benchmark approaches.

Abstract

Due to the lack of a large annotated corpus, many resource-poor Indian languages struggle to reap the beneits of recent deep feature representations in Natural Language Processing (NLP). Moreover, adopting existing language models trained on large English corpora for Indian languages is oten limited by data availability, rich morphological variation, syntax, and semantic diferences. In this paper, we explore the traditional to recent eicient representations to overcome the challenges of low resource language, Telugu. In particular, our main objective is to mitigate the low-resource problem for Telugu. Overall, we present several contributions to a resource-poor language viz. Telugu. (i) a large annotated data (35,142 sentences in each task) for multiple NLP tasks such as sentiment analysis, emotion identiication, hate-speech detection, and sarcasm detection, (ii) we create diferent lexicons for sentiment, emotion, and hate-speech for improving the eiciency of the models, (iii) pretrained word and sentence embeddings, and (iv) diferent pretrained language models for Telugu such as ELMo-Te, BERT-Te, RoBERTa-Te, ALBERT-Te, and DistilBERT-Te on a large Telugu corpus consisting of 80,15,588 sentences (16,37,408 sentences from Telugu Wikipedia and 63,78,180 sentences crawled from diferent Telugu websites). Further, we show that these representations signiicantly improve the performance of four NLP tasks and present the benchmark results for Telugu. We argue that our pretrained embeddings are competitive or beter than the existing multilingual pretrained models: mBERT, XLM-R, and IndicBERT. Lastly, the ine-tuning of pretrained models show higher performance than linear probing results on four NLP tasks with following F1-scores: Sentiment (68.72), Emotion (58.04), Hate-Speech (64.27) and Sarcasm (77.93). We also experiment on publicly available Telugu datasets (Named Entity Recognition, Article Genre Classiication, and Sentiment Analysis ), ind that our Telugu pretrained language models (BERT-Te and RoBERTa-Te) outperform the state-of-the-art system except for the sentiment task. We open-source our corpus, four diferent datasets, lexicons, embeddings, and code https://github.com/Cha14ran/DREAM-T. he pretrained Transformer models for Telugu are available at https://huggingface.co/ltrctelugu.

Abstract

In this work, we propose IndoLayout, a novel real-time approach for generating high-quality occupancy maps from an RGB image for indoor scenes. Such occupancy maps are often crucial for path-planning and mapping in indoor environments but are often built using only information contained in the ego view. In contrast, our approach also predicts occupancy values beyond immediately visible regions from just a monocular image, leveraging learnt priors from indoor scenes. Hence, our proposed network can produce a hallucinated, amodal scene layout that includes areas occluded in the RGB image, such as a navigable floor behind a desk. Specifically, we propose a novel architecture that uses self-attention and adversarial learning to vastly improve the quality of the predicted layout. We evaluate our model on several photorealistic indoor datasets and outperform previous relevant work on all metrics that measure layout quality, including newly adopted ones. Finally, we demonstrate the effectiveness of our method by showing significant improvements on the PointNav task over similar approaches using IndoLayout. For more details, please refer to the project page: https://indolayout.github.io/.

Abstract

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/

Abstract

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