Abstract

Dynamic networks evolve as nodes, edges, and communities appear, disappear, or reorganize across time. Tracking this evolution while preserving the analyst's mental map poses a fundamental challenge: force-directed and static-community layouts reset spatial structure at each timeslice, breaking visual continuity. We present DyNetSpin, a visual analytics system that addresses this through two principled contributions. First, a History-Aware Spiral Layout extends community-centric spiral visualization [JK23] to the temporal domain via chronological accretion: communities are ranked by a weighted combination of logarithmic size (preventing large-community visual dominance) and historical barycenter (anchoring veteran communities near the spiral core across timeslices). Second, a Local Volatility metric classifies each node's directional temporal state-- Incoming, Outgoing, Transient, or Stable--within a three-timeslice sliding window, enabling analysts to distinguish structural growth from structural decline.

Abstract

Visualising all dimensions of multi-dimensional real data while providing semantics is a challenging problem in data analysis. In this paper, we present the visualisation of multi-dimensional real data using our tool, 360◦ RAYS. RAYS plots all features and points of the dataset and provides information such as the geometric positioning of points or classes in the orthants of space, thereby aiding understanding of the closeness of points, classification of points, and ordering of subspaces. This visualisation technique is built after exploring viable options to plot multi-dimensional data, which included parallel coordinates, concentric coordinates, and circle segments, among others. The key idea of our approach is a hierarchical concentric ring structure, where each successive ring incrementally adds a dimension to the existing subspace, ordered by the increasing or decreasing coefficient of variation of data in the added dimension. Normalised data points are mapped to ring sectors based on a bitwise encoding of their coordinate values (non-negative or negative). The advantage of RAYS is that it presents the complete data with all dimensions and provides a clear visual grouping of points by subspace concentration. The visualisation also shows pure and overlapping orthants within the data in a flattened 2D radial layout.

Abstract

Large Language Models (LLMs) are increasingly deployed in highstakes settings where errors are costly and decisions must be made
under time pressure, ambiguity, and strict constraints. However,
most existing benchmarks evaluate model performance under relaxed conditions and fail to capture how LLM behavior degrades
under stress. This paper presents a structured framework for stresstesting LLM robustness across five real-world scenarios with increasing difficulty. We evaluate three widely used models--ChatGPT
(GPT-5.1), Gemini 3, and Claude 4.5 Sonnet--using a 45-item rubric
assessing accuracy, safety, logical consistency, and constraint adherence. To quantify robustness, we introduce a Stress Sensitivity
Index (SSI) that measures performance degradation as task difficulty
increases. Our results show that ChatGPT remains largely stable
under stress, Gemini exhibits moderate degradation, and Claude
displays substantial sensitivity, particularly in constraint-following
tasks. These findings highlight the importance of stress-oriented
evaluation for understanding LLM reliability prior to their deployment in real-world, high-stress human environments.

Abstract

High-dimensional datasets present a fundamental challenge:
understanding how data points relate to each other across different feature combinations. Existing visualization methods typically show global structure through dimensionality reduction or highlight relationships in the full feature space, but they fail to reveal which specific feature subspaces drive point-to-point connections. We present CSP-HEIDI, a visualization framework that resolves this ambiguity by analyzing knearest neighbor (k-NN) relationships across the complete feature subspaces. Central to our approach is the Interpretable Expression Matrix (IEM), which replaces scalar distances with minimal Boolean expressions (minterms). By employing a logic minimization strategy, CSPHEIDI isolates the "root cause" subspaces that are sufficient to maintain neighborhood connections, discarding redundant supersets. Through a novel Concentric Spiral Arrangement, these expressions are organized to reveal structural topology. We demonstrate on biological and synthetic datasets that CSP-HEIDI successfully disentangles intra-cluster cohesion from inter-cluster bridging. The visualization explicitly reveals how high-discriminability features act as "identity anchors" within clusters, while low-discriminability dimensions function as specific "semantic bridges" between them. This offers a qualitative, "white-box" alternative to projection methods, enabling researchers to diagnose the precise logic governing cluster cohesion and overlap.

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

Millions of people in India who are deaf or hard of hearing rely on Indian Sign Language (ISL) as their main form of communication. Nonetheless, there is still a big communication gap between people who speak ISL and those who don't. For the deaf community, social integration and information access are hampered by the absence of real-time translation tools. The language barrier that separates ISL from commonly spoken languages like Hindi and English also makes communication more difficult and restricts the participation of deaf people in public services, education, and mainstream conversations. This research aims to develop an AI-powered system that can effectively detect and translate Indian Sign Language (ISL) into English, and vice versa.

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