Abstract

Global access to electricity has increased from 78.2% to 2000 to 90.5% in 2020, resulting in an increased electricity demand worldwide. Unlike commercial electricity consumption, which is managed by professionals, residential consumption is managed by the householders, who often lack insight into their energy usage. Quality feedback, including detailed energy consumption and tips, can lead to substantial household savings. There are several mediums for providing energy feedback, such as Short Message Service (SMS), postal letter, email, mobile app, and In-Home Display (IHD). Studies suggest that feedback through electronic media can save up to 20% of energy consumption. In this work, we aim to design mobile application interfaces that can maximize energy savings through efective feedback. The level of savings realized is dependent on the user’s preferences and understanding of the information presented. User preferences are subjective of their profle (e.g., age, occupation, income) and the cultural context (e.g., country). The possibility of energy reduction is high when the provided information matches the user preferred information for feedback. Smart homes have recently been included as an annexure in India’s building energy code (Eco Niwas Samhita 2021), indicating a growing demand for quality energy feedback in India. However, there is a lack of research that addresses what feedback information is suitable for Indian users. We conducted two questionnaire-based surveys, one to understand users’ preferences for feedback information and another to validate the designed mobile application interface screens. The surveys were conducted on two age groups, young and middle-aged adults. A Chi-Square Test of Independence was performed to assess the relationship between the user’s preference for feedback information and their age group. Participants identifed total energy consumption, appliance level disaggregated information, energy-saving tips, goals, and historical consumption comparisons as the top fve information types. In contrast, normative comparison was the least preferred information. The follow-up design validations suggest that the interface should be customizable to accommodate the varying preferences of users. The current fndings will help customize the energy feedback display UI design as per the Indian population. Keywords: Energy-feedback, Residential, Interface-design, User centric feedback, In-home-display

Abstract

The demand for energy in residential structures is set to outstrip the supply soon. Most studies rely on national databases or previously published datasets. It is crucial to approach this topic using a bottom-up methodology. Therefore, this research aims to analyse the energy consumption pattern of residential buildings in Jaipur by utilizing primary datasets. The present study takes Jaipur as a case study and proposes a way to define architectural archetypes in Indian towns based on their energy efficiency. The collection and analysis of 2,327 primary data samples included energy use statistics and socioeconomic information. This research applies multivariate analysis in a bottom-up manner to a primary database. The clustering method was utilized to determine the energy consumption of Indian families in Jaipur. Low- and middle-income families tend to purchase more appliances when their income increases, yet …

Abstract

In this work we propose a new practical synchronization protocol for multiple Euler Lagrange (EL) systems without structural linear-in-the-parameters (LIP) knowledge of the uncertainty and where the agents can be interconnected before control design by unknown state-dependent interconnection terms. This setting is meant to overcome two standard a priori assumptions in the literature concerning uncertainty with LIP structure and absence of interaction among agents before designing the synchronization protocol. To overcome these assumptions, we propose an adaptive distributed control mechanism having the purpose of estimating the coefficients of the resulting state-dependent uncertainty structure.

Abstract

In this article, a novel adaptive trajectory tracking controller is designed for a payload-carrying spacecraft under full state constraints. The proposed controller can tackle state-dependent uncertainties without a priori knowledge of their structures and upper bounds. The controller ensures time-varying constraints on all states and their time derivatives. The closed-loop stability of the proposed scheme is verified analytically via the Lyapunov method, and real-life experiments using a robotic testbed validated the effectiveness of the proposed adaptive controller over the state-of-the-art.

Abstract

Autonomous driving requires accurate reasoning of the location of objects from raw sensor data. Recent end-to-end learning methods go from raw sensor data to a trajectory output via Bird’s Eye View(BEV) segmentation as an interpretable intermediate representation. Motion planning over cost maps generated via Birds Eye View (BEV) segmen- tation has emerged as a prominent approach in autonomous driving. However, the current approaches have two critical gaps. First, the optimization process is simplistic and involves just evaluating a fixed set of trajectories over the cost map. The trajectory samples are not adapted based on their associated cost values. Second, the existing cost maps do not account for the uncertainty in the cost maps that can arise due to noise in RGB images, BEV annotations. As a result, these approaches can struggle in challenging scenarios where there is abrupt cut-in, stopping, overtaking, merging, etc from the neighboring vehicles. In this paper, we propose UAP-BEV, a novel approach that models the noise in Spatio-Temporal BEV predictions to create an uncertainty-aware occupancy grid map. Using queries of the distance to the closest occupied cell, we obtain a sample estimate of the collision probability of the ego-vehicle. Subsequently, our approach uses gradient-free sampling-based optimization to compute low-cost trajectories over the cost map

Abstract

It is a well-established fact that strategic placement of items on the shelves of a retail store significantly impacts the revenue of the retailer. Consumer goods sold in retail stores can be classified into essential and typically low-priced convenience items, and non-essential and high-priced shopping items. Notably, the lower-priced convenience items are critical to ensuring consumer foot-traffic, thereby also driving the sales of shopping items. Moreover, users typically buy multiple items together (i.e., itemsets) to facilitate one-stop shopping. Hence, it becomes a necessity to strategically index and place itemsets that contain both convenience items and shopping items. In this regard, we propose a consumer-good-type aware and revenue-conscious itemset indexing scheme for efficiently retrieving high-revenue itemsets containing both convenience and shopping items. Moreover, we propose an itemset placement …

Abstract

Understanding user perspectives on Artificial Intelligence (AI) in education is essential for creating pedagogically effective and ethically responsible AI-integrated learning environments. In this paper, we conduct an extensive qualitative content analysis of four major social media platforms (Twitter, Reddit, YouTube, and LinkedIn) to explore the user experience (UX) and perspectives of early adopters toward ChatGPT-an AI Chatbot technology-in various education sectors. We investigate the primary applications of ChatGPT in education (RQ1) and the various perceptions of the technology (RQ2). Our findings indicate that ChatGPT is most popularly used in the contexts of higher education (24.18%), K-12 education (22.09%), and practical-skills learning (15.28%). On social media platforms, the most frequently discussed topics about ChatGPT are productivity, efficiency, and ethics. While early adopters generally lean toward seeing ChatGPT as a revolutionary technology with the potential to boost students' self-efficacy and motivation to learn, others express concern that overreliance on the AI system may promote superficial learning habits and erode students' social and critical thinking skills. Our study contributes to the broader discourse on Human-AI Interaction and offers recommendations based on crowd-sourced knowledge for educators and learners interested in incorporating ChatGPT into their educational settings. Furthermore, we propose a research agenda for future studies that sets the foundation for continued investigation into the application of ChatGPT in education.

Abstract

Perception of interval timing influences the behaviour of the organisms. Computational models of interval timing are categorized into Pacemaker Accumulator models, Memory-based models, Oscillator models and Random Process models, Ramping Activity models and Population Clock models. Random process models or drift diffusion models are biologically plausible models and are based on the activity of spiking neurons. In this paper, we proposed a computational model of interval timing based on spiking neurons. The results are validated against the Scalar property of interval timing

Abstract

Factual consistency is one of the most important requirements when editing high quality documents. It is extremely important for automatic text generation systems like summarization, question answering, dialog modeling, and language modeling. Still, automated factual inconsistency detection is rather under-studied. Existing work has focused on (a) finding fake news keeping a knowledge base in context, or (b) detecting broad contradiction (as part of natural language inference literature). However, there has been no work on detecting and explaining types of factual inconsistencies in text, without any knowledge base in context. In this paper, we leverage existing work in linguistics to formally define five types of factual inconsistencies. Based on this categorization, we contribute a novel dataset, FICLE (Factual Inconsistency CLassification with Explanation), with ~8K samples where each sample consists of two sentences (claim and context) annotated with type and span of inconsistency. When the inconsistency relates to an entity type, it is labeled as well at two levels (coarse and fine-grained). Further, we leverage this dataset to train a pipeline of four neural models to predict inconsistency type with explanations, given a (claim, context) sentence pair. Explanations include inconsistent claim fact triple, inconsistent context span, inconsistent claim component, coarse and fine-grained inconsistent entity types. The proposed system first predicts inconsistent spans from claim and context; and then uses them to predict inconsistency types and inconsistent entity types (when inconsistency is due to entities). We experiment with multiple Transformer …

Abstract

Humans have a natural ability to perform semantic associations with the surrounding objects in the environment. This allows them to create a mental map of the environment which helps them to navigate on-demand when given a linguistic instruction. A natural goal in Vision Language Navigation (VLN) research is to impart autonomous agents with similar capabilities. Recently introduced VL Maps \cite{huang23vlmaps} take a step towards this goal by creating a semantic spatial map representation of the environment without any labelled data. However, their representations are limited for practical applicability as they do not distinguish between different instances of the same object. In this work, we address this limitation by integrating instance-level information into spatial map representation using a community detection algorithm and by utilizing word ontology learned by large language models (LLMs) to perform open-set semantic associations in the mapping representation. The resulting map representation improves the navigation performance by two-fold (233\%) on realistic language commands with instance-specific descriptions compared to VL Maps. We validate the practicality and effectiveness of our approach through extensive qualitative and quantitative experiments.