Abstract

Obtaining sufficient information in one’s mother tongue is crucial for satisfying the information needs of the users. While high-resource languages have abundant online resources, the situation is less than ideal for very low-resource languages. Moreover, the insufficient reporting of vital national and international events continues to be a worry, especially in languages with scarce resources, like Mizo. In this paper, we conduct a study to investigate the effectiveness of a simple methodology designed to generate a holistic summary for Mizo news articles, which leverages English-language news to supplement and enhance the information related to the corresponding news events. Furthermore, we make available 500 Mizo news articles and corresponding enriched holistic summaries. Human evaluation confirms that our approach significantly enhances the information coverage of Mizo news articles.

Abstract

Controller synthesis is pivotal in automating control system design from formal specifications and enhancing industrial system verification and optimization processes. This paper critically evaluates LTL-based controller synthesis, highlighting significant gaps in tool support that hinder its widespread adoption in the industry. Despite substantial theoretical progress, an apparent disparity persists between academic research outcomes and the robust, practical tools demanded by industry. Through a comprehensive evaluation, this study reveals mismatches between industrial requirements and the capabilities of current open-source tools. The findings emphasize underexplored challenges and propose future research directions and strategies for practical integration. This work aims to bridge the gap by advocating for enhanced tool support, enabling solutions that align with industrial standards and fostering the broader application of controller synthesis across various sectors.

Abstract

Graphs, including, for example, social networks, collaboration networks, and epidemiological networks, offer a way to represent relationships among entities. Graphs arising from most real-world phenomena are not static and evolve. Dealing with such dynamic graphs requires special algorithms, known as dynamic graph algorithms, that update the required graph analytic based on the change in the current graph instead of recomputing the analytic from scratch. Parallel dynamic graph algorithms are now known for various graph problems such as connectivity, spanning trees, shortest paths, centrality metrics, and community detection. Dynamic algorithms often work in settings where a batch of edge insertions/deletions affects the current graph.

Abstract

Open Vocabulary Keyword Spotting is essential in numerous applications, from virtual assistants to security systems, as it allows systems to identify specific words or phrases in continuous speech. In this paper, we propose a novel end-to-end method for detecting user-defined open vocabulary keywords by leveraging linguistic patterns for the correlation between audio and text modalities. Our approach utilizes quantized pre-trained foundation models for robust audio embeddings and a unique lightweight Multi-Scale Linear Attention (MSLA) network that aligns speech and text representations for effective cross-modal agreement. We evaluate our method on two distinct datasets, comparing its performance against other baselines. The results highlight the effectiveness of our approach, achieving significant improvements over the Cross-Modality Correspondence Detector (CMCD) method, with a 16.08% increase in AUC and a 17.2% reduction in EER metrics on the Google Speech Commands dataset. These findings demonstrate the potential of our method to advance keyword spotting across various real-world applications.

Abstract

Coreference resolution, the process of determining what a referring expression (a pronoun or a noun phrase) refers to in discourse, is a critical aspect of natural language understanding. However, the development of computational models for coreference resolution in low-resource languages, such as the Dravidian (and more broadly all South Asian) languages, still remains a significant challenge due to the scarcity of annotated corpora in these languages. To address this data scarcity, we adopt a pipeline that translates the English GAP dataset into various South Asian languages, creating a multi-lingual coreference dataset mGAP. Our research aims to leverage this dataset and develop two novel models, namely the joint embedding model and the cross attention model for coreference resolution with Dravidian languages in mind. We also demonstrate that cross-attention captures pronoun-candidate relations better leading to improved coreference resolution. We also harness the similarity across South Asian languages via transfer learning in order to use high resource languages to learn coreference for low resource languages.

Abstract

Scene text recognition has historically concentrated on English, with limited advancements in developing solutions that perform well across multiple languages. Previous efforts in multilingual scene text recognition have predominantly targeted languages with considerable syntactic and semantic differences. However, Indian languages, while diverse, share numerous common features that remain largely underutilized. This competition aims to address the often-overlooked challenge of scene text recognition within the Indian context and to advance robust word image recognition across ten Indian languages. The dataset provided for this competition is one of the most comprehensive multilingual datasets, encompassing 10 languages, each with 17,500 training samples, 2,500 validation samples and 5,000 test word-image samples. The task was to correctly recognize the word-images, for which we received forty-nine registrations and five final submissions from industrial and research communities. The winning team achieved an average Character Recognition Rate (CRR) of 92.85% and a Word Recognition Rate (WRR) of 84.01% across the ten languages. This paper details the proposed dataset and summarizes the submissions for the competition- WIRIndic-2024.

Abstract

In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a non-contact way using a Frequency Modulated Continuous Wave (FMCW) radar-based system. Traditional vital sign monitoring methods often face significant limitations, including subject discomfort with wearable devices, challenges in calibration, and the risk of infection transmission through contact measurement devices. To address these issues, this research is motivated by the need for versatile, non-contact vital monitoring solutions applicable in various critical scenarios. This work also explores the challenges of extending this capability to an arbitrary number of subjects, including hardware and theoretical limitations. Supported by rigorous experimental results and discussions, the paper illustrates the system’s potential to redefine vital sign monitoring. An FPGA-based implementation is also presented as proof of concept for a hardware-based and portable solution, improving upon previous works by offering 2.7x faster execution and 18.4% less Look-Up Table (LUT) utilization, as well as providing over 7400x acceleration compared to its software counterpart.

Abstract

With advances in vehicular communications technology such as Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V), the computation of vehicular tasks has become distributed facilitated by computation offloading from the vehicular platform to road side units (RSUs) or the cloud infrastructure. In this work, due to communication overheads, we do not consider the cloud and only consider offloading horizontally across RSU nodes. However, the advantage of offloading depends upon how the tasks are scheduled across the RSUs. Unlike existing horizontal offloading works, this work explores the benefits of online task partitioned scheduling for computation offloading from multiple vehicles to RSUs or edge nodes. Specifically, we propose a new efficient timeslot-based online hybrid partitioned scheduling algorithm, which splits some tasks into subtasks and schedules them across RSU nodes while considering vehicle flow constraints. We compared and evaluated the effectiveness of our proposed hybrid partitioned scheduling algorithm with the fully partitioned algorithm. We also compared the performance of the aforementioned algorithms with an optimal scheduling algorithm utilizing several experiments conducted on a real-world vehicular dataset.

Abstract

Finding a set of empirical criteria fulfilled by any theory that satisfies the generalized notion of noncontextuality is a challenging task of both operational and foundational importance. The conventional approach of deriving facet inequalities from the relevant noncontextual polytope is computationally demanding. Specifically, the noncontextual polytope is a product of two polytopes, one for preparations and the other for measurements, and the dimension of the former typically increases polynomially with the number of measurements. This work presents an alternative methodology for constructing a polytope that encompasses the actual noncontextual polytope while ensuring that the dimension of the polytope associated with the preparations remains constant regardless of the number of measurements and their outcome size. In particular, the facet inequalities of this polytope serve as necessary conditions for noncontextuality. To demonstrate the efficacy of our methodology, we apply it to nine distinct contextuality scenarios involving four to nine preparations and two to three measurements to obtain the respective sets of facet inequalities. Additionally, we retrieve the maximum quantum violations of these inequalities. Our investigation uncovers many novel non-trivial noncontextuality inequalities and reveals intriguing aspects and applications of quantum contextual correlations.

Abstract

Walking is one of the most natural ways to explore an environment. While Virtual Reality (VR) facilities simulation of infinite spaces, its limited by the physical space available. To address this limitation, several techniques have been proposed, such as redirected walking, walking in place, and teleportation. While some of these techniques, such as teleportation and walking in place, disrupt the natural immersion, others like redirected walking limit the walkable area within the virtual space. The concept of overlapping architecture tackles this challenge by compressing large virtual environments into smaller spaces. This is achieved by generating portions of an environment that might be physically impossible as a whole. In this paper, we propose a system for creating maze-based virtual environments with multi-path capabilities. This system allows for infinite natural walking experiences while maintaining architectural consistency. Specifically, our system can generate distinct, customizable, and scalable virtual mazes for physical areas exceeding 4x4 meters. The combination of multi-path capabilities and architectural consistency can enhance the overall maze traversal experience for end users.