Abstract

In this paper we show an effective means of integrating data driven frameworks to sampling based optimal control to vastly reduce the compute time for easy adoption and adaptation to real time applications such as on-road autonomous driving in the presence of dynamic actors. Presented with training examples, a spatio-temporal CNN learns to predict the optimal mean control over a finite horizon that precludes further resampling, an iterative process that makes sampling based optimal control formulations difficult to adopt in real time settings. Generating control samples around the network-predicted optimal mean retains the advantage of sample diversity while enabling real time rollout of trajectories that avoids multiple dynamic obstacles in an on-road navigation setting. Further the 3D CNN architecture implicitly learns the future trajectories of the dynamic agents in the scene resulting in successful collision free …

Abstract

Sign language is a rich form of communication, uniquely conveying meaning through a combination of gestures, facial expressions, and body movements. Existing research in sign language generation has predominantly focused on text-to-sign pose generation, while speech-to-sign pose generation remains relatively underexplored. Speech-to-sign language generation models can facilitate effective communication between the deaf and hearing communities. In this paper, we propose an architecture that utilises prosodic information from speech audio and semantic context from text to generate sign pose sequences. In our approach, we adopt a multi-tasking strategy that involves an additional task of predicting Facial Action Units (FAUs). FAUs capture the intricate facial muscle movements that play a crucial role in conveying specific facial expressions during sign language generation.

Abstract

Universal Semantic Representation (USR) captures how the speaker intends to view a situation. The representation is aimed at being language-independent. The theoretical model, Universal Semantic Grammar (USG), that has been adopted for the task is based on Indian Grammatical Tradition (IGT henceforth) and Pāṇini's grammatical model. USR is not limited to just presenting “who is doing what to whom and when and where” in a sentence. It is about capturing a speaker’s vivakṣā, how the speaker views a situation. The way the speaker views a situation or an event, accordingly he/she selects kāraka roles for the participants of an event, tense-aspect-modility features on the verb, sentence structure by foregrounding and backgrounding information, also various discourse elements such as particles, connectives, co-referencing and so on. USR is a text-based data structure that is close to the Attribute Value matrix (AVM) representation. It is easier to read and write manually, as well as process computationally. This document describes the theoretical foundation of USR and the strategies that have been adopted for USR annotation

Abstract

Embodiments herein provide a system and a method for automatically generating at least one synthetic talking head video using a machine learning model. The method includes (i) extracting features from each frame of a video that is extracted from data sources,(ii) analyzing, using a face-detection model, the video to determine a driving face video if a number of identities, and faces of speakers are equal to one in all frames of the video,(iii) generating, using a text to speech model, synthetic speech utterances by automatically selecting a vocabulary of words and sentences from the data sources,(iv) modifying lip movements that are originally present in the driving face video corresponding to the synthetic speech utterances, and (v) generating, using machine learning model, synthetic talking head video based on the lip movements that are modified corresponding to the synthetic speech utterances.

Abstract

Although current deep models for face tasks surpass human performance on some benchmarks, we do not understand how they work. Thus, we cannot predict how it will react to novel inputs, resulting in catastrophic failures and unwanted biases in the algorithms. Explainable AI helps bridge the gap, but currently, there are very few visualization algorithms designed for faces. This work undertakes a first-of-its-kind meta-analysis of explainability algorithms in the face domain. We explore the nuances and caveats of adapting general-purpose visualization algorithms to the face domain, illustrated by computing visualizations on popular face models. We review existing face explainability works and reveal valuable insights into the structure and hierarchy of face networks. We also determine the design considerations for practical face visualizations accessible to AI practitioners by conducting a user study on the utility of various explainability algorithms.

Abstract

Despite rapid advances, modeling a variety of instructional designs to support variations in teaching and learning during the design of educational technologies is still an open challenge. In this paper, we propose a patterns based approach for the design of educational technologies to address this challenge. This is in contrast with existing literature that focuses either on patterns in education or in software, and not both. The core idea of our approach is to leverage patterns for modeling instructional design knowledge and to connect it with patterns in software architecture. We discuss different categories of patterns in instructional design. We then present the notion of Pattern-Oriented Instructional Design (POID) as a way to model instructional design as a connection of patterns (ProcessPattern, ContentPattern) and integrate it with Pattern-Oriented Software Architecture (POSA) based on fundamental principles in software engineering. We demonstrate our approach through adult literacy case study (287 million learners, 22 Indian Languages and a variety of instructional designs). The results of our approach (both web and mobile versions) are available at http://rice.iiit.ac.in and were transferred to

Abstract

River valley projects have a lot of promise in the seismically active Himalayan orogenic region. Some hydroelectric projects are now operational, some are in the planning stages, and a few more will be built shortly. Knowing the nature of ground motion at these locations is critical. The present study uses a probabilistic seismic hazard analysis (PSHA) technique to estimate Peak Ground Acceleration (PGA) for the three hydropower projects in Uttarakhand, Himachal Pradesh, and Jammu and Kashmir (India). Given all potential earthquakes, the aim of probabilistic seismic hazard analysis (PSHA) is to quantify the rate of surpassing certain ground motion levels at the project site. Hazard curves may be used to determine the seismic design input for a location, and they can also be used to analyze the tunnel seismic reaction.

Abstract

Medical image segmentation is critical for effective computer-aided diagnosis and localization of ailments. Automated segmentation of wound regions from patient images can aid clinicians in measuring and managing chronic wounds and monitoring the wound healing trajectory. While there exists a plethora of work on general medical image segmentation, there is hardly any work on wound image analysis and segmentation. Existing methods are limited to segmenting a smaller subset of ulcers, such as foot ulcers, with no special processing for wound images. In this paper, we build segmentation models for eight different types of wound images. Wound image analysis is a challenging problem due to the lack of availability of extensive data (labeled or unlabeled), and annotation is also challenging due to the shortage of well-trained wound care clinicians. To handle these challenges, we contribute WoundSeg 1 , a large and diverse dataset of segmented wound images. Generic wound image segmentation is complex due to the heterogeneous appearance of wound area across images of similar wound types. We propose a novel image segmentation framework, WSNet, which leverages (a) wound-domain adaptive pretraining on a large unlabeled wound image collection and (b) a global-local architecture that utilizes full image and its patches to learn fine-grained details of heterogeneous wounds. On WoundSeg, we achieve a decent Dice score of 0.847. On existing AZH Woundcare and Medetec datasets, we establish a new state-of-the-art. Further, we show the impact of using segmentation for improving the accuracy of downstream tasks like wound

Abstract

In developing economies, residential energy consumption patterns have rapidly transformed with better energy access and service quality. Unlike other building types, residential buildings are more complex due to wide variations in their consumption patterns influenced by various factors. Researchers have characterised residential building stock based on distinct building archetypes. This paper presents a comprehensive review of relevant published research focusing on the classification of residential buildings based on their energy consumption. This review also focuses on residential archetype studies in the context of building science. The methodologies adopted by different researchers to characterise the energy use of residential building stock using an archetypal approach at different spatial scales (building to city scale and local to national scale) have been critically reviewed in this study. The paper will provide the researchers with a holistic understanding of the current directions and magnitude of ongoing research in this domain. KEYWORDS: Housing stockbuilding archetypesclassification methodologybuilding energy consumptionenergy modellingbottom-up studies Previous article View issue table of contents Next article Disclosure statement No potential conflict of interest was reported by the author(s). Additional information Funding This work is supported by a Joint Indo-UK research project called ‘Residential building energy demand reduction in India (RESIDE),’ funded by the Department of Science and Technology (DST/TMD/UK-BEE/2017/07), Government of India, and the Engineering and Physical Sciences Research Council (EP/R008434/1), United Kingdom. Related research People also read Recommended articles Cited by Identifying building archetypes based on energy performance as the major criteria: a case of Jaipur, India Bibhu Kalyan Nayak et al. Advances in Building Energy Research Published online: 27 May 2023 Data-driven approach to prioritize residential buildings’ retrofits in cold climates using smart thermostat data Aya Doma et al. Architectural Science Review Published online: 31 Mar 2023 Review of studies on thermal comfort in Indian residential buildings Sunil Kumar Sansaniwal et al. Science and Technology for the Built Environment Published online: 27 Feb 2020 View more

Abstract

Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. The vulnerability of a building to earthquake can be assessed through inspection that takes into account the expected damage progression of the associated component and the component’s contribution to structural system performance. Most of these inspections are done manually, leading to high utilization of manpower, time, and cost. This paper proposes a methodology to automate these inspections through UAV-based image data collection and a software library for post-processing that helps in estimating the seismic structural parameters. The key parameters considered here are the distances between adjacent buildings, building plan-shape, building