Abstract

The rapid advancement of Large Language Models (LLMs) has en- abled them to generate complex, multi-step plans and itineraries. However, these generated plans often lack temporal and spatial consistency, particularly in scenarios involving physical travel con- straints. This research aims to study the temporal performance of di!erent LLMs and presents a validation framework that eval- uates and improves the temporal consistency of LLM-generated travel itineraries. The system employs multiple state-of-the-art LLMs to generate travel plans and validates them against real-world "ight duration constraints using the AeroDataBox API. This work contributes to the understanding of LLM capabilities in handling complex temporal reasoning tasks like itinerary generation and provides a framework to rectify any temporal inconsistencies like overlapping journeys or unrealistic transit times in the itineraries generated by LLMs before the itinerary is given to the user. Our experiments reveal that while current LLMs frequently produce temporally inconsistent itineraries, these can be systematically and reliably corrected using our framework, enabling their practical deployment in large-scale travel planning.

Abstract

Bharatanatyam is one of the most celebrated classical dance forms in India. It is performed and enjoyed by art enthusiasts all over the world. It comprises expressions, hand gestures, footwork, rhythmic patterns, eye, neck and head movements, lyrics, and music. These basic components are integrated and woven into a beautifully crafted visual story. However, beneath this creative visual craft lies a structured compilation of technical, mathematical, computational, and modular aspects. In this paper, we provide a database schema to store the artistic components in a structured relational database model. By providing a database schema, we provide a layered approach to store Bharatanatyam choreographies. This layered approach helps in building choreographies by working individually on different modules of the art form like the foot work, hand movement, etc, and consolidating them to form and store sequences. Bharatanatyam choreographies are a collection of sequences. In this paper, we discuss how complete choreographies are modelled through the logical sequencing of these components. Our paper lays the foundation for bridging traditional knowledge of the performing arts with technologies to help in digital archiving, cultural analytics, and choreography suggestions.

Abstract

No Results Found

Abstract

Autonomous Underwater Vehicles (AUVs) play a vital role in explor- ing and mapping underwater environments. However, the presence of nadir gaps, or blind zones, in commercial AUVs can lead to unex- plored areas during mission execution, limiting their effectiveness. Our work addresses the challenges of path planning in the presence of nadir gaps and presents scalable coverage strategies for AUVs minimizing either the mission completion time or the total number of turns performed while ensuring complete exploration, eliminating the risk of leaving critical areas unexplored. We provide provably complete strategies and perform extensive simulations on diverse input configurations based on real-world instances to demonstrate the efficacy of our strategies.

Abstract

A system to visualise large graphs

Abstract

A system and method for visualizing large graph data using spirals are provided. The method includes (i) generating communities from an input graph data, (ii) configuring nodes in the communities in an ascending order based on a centrality measure, (iii) determining a spiral for the communities by configuring (a) a node with a highest centrality measure at a spiral center and (b) the nodes in a spiral shape based on the ascending order of the centrality measure along the spiral shape, (iv) determining a first super edge for the spiral of the communities, (v) generating a coarse graph using super nodes, the first super edge, and a second super edge, (vi) configuring, using a standard force-directed layout algorithm, corresponding spirals of the communities on the generated coarse graph based on a weight of the second super edge and attractive forces to generate the visualization of the large graph data.

Abstract

The number of individuals having identical names on the internet is increasing. Thus making the task of searching for a specific individual tedious. The user must vet through many profiles with identical names to get to the actual individual of interest. The online presence of an individual forms the profile of the individual. We need a solution that helps users by consolidating the profiles of such individuals by retrieving factual information available on the web and providing the same as a single result. We present a novel solution that retrieves web profiles belonging to those bearing identical Full Names through an end-to-end pipeline. Our solution involves information retrieval from the web (extraction), LLM-driven Named Entity Extraction (retrieval), and standardization of facts using Wikipedia, which returns profiles with fourteen multi-valued attributes. After that, profiles that correspond to the same real-world individuals are determined. We accomplish this by identifying similarities among profiles based on the extracted facts using a Prefix Tree inspired data structure (validation) and utilizing ChatGPT's contextual comprehension (revalidation). The system offers varied levels of strictness while consolidating these profiles, namely strict, relaxed, and loose matching. The novelty of our solution lies in the innovative use of GPT -- a highly powerful yet an unpredictable tool, for such a nuanced task. A study involving twenty participants, along with other results, found that one could effectively retrieve information for a specific individual.

Abstract

We present a Spiral Visualization that facilitates users to visually comprehend large graphs. Spiral Visualization is a representation that highlights key aspects of networks, including the number, size, and density of communities, important or central nodes within communities, centrality distribution within communities, connections between communities, and connections between nodes. To facilitate analysis and comprehension of networks using various interaction techniques, such as zooming, tooltip, and highlight, we have implemented a Spiral Visualization dashboard. We conducted a qualitative user study incorporating observation, think-aloud protocols, and participant rating of confidence and easiness to assess the usability and suitability of our visualization. The findings suggest that our visualization is appropriate for the network tasks evaluated. However, tasks requiring color comparison, such as identifying the densest community and comparing community densities were found to be more challenging to perform.

Abstract

Aspect-based summarization of a legal case file related to regulating bodies allows different stakeholders to consume information of interest therein efficiently. In this paper, we propose a multi-step process to achieve the same. First, we explore the semantic sentence segmentation of SEBI case files via classification. We also propose a dataset of Indian legal adjudicating orders which contain tags from carefully crafted domain-specific sentence categories with the help of legal experts. We experiment with various machine learning and deep learning methods for this multi-class classification. Then, we examine the performance of numerous summarization methods on the segmented document to generate persona-specific summaries. Finally, we develop a pipeline making use of the best methods in both sub-tasks to achieve high recall.

Abstract

In this work, we introduce the problem of scenic routes among points in R d . The key development is the nature of the problem in terms of both defining the concept of scenic points and scenic routes and then coming up with algorithms that meet different criteria for the generated scenic routes. The scenic routes problem provides a visual trajectory for a user to comprehend the layout of high-dimensional points. The nature of this trajectory and the visual layout of the points have applications in comprehending the results of machine learning supervised and unsupervised learning techniques. We study the problem in 2D and 3D (with two color points) before exploring the issues in R d . The red/blue points in our examples could be to be in a class or not to be in a class. The applications could include landscape design to adhere to the scenic beauty of the artifacts on the ground. The generation of equally separated layouts for designing composite hardware where interference could be an issue. Keywords: sce