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Abstract

In this work, we study the natural monotone analogues of various equivalent definitions of : a well studied class (Poizat 2008, Koiran & Perifel 2009, Malod 2011, Mahajan & Rao 2013) that is believed to be larger than . We observe that these monotone analogues are not equivalent unlike their non-monotone counterparts, and propose monotone ( ) to be defined as the monotone analogue of Poizat's definition. With this definition, turns out to be exponentially stronger than and also satisfies several desirable closure properties that the other analogues may not. Our initial goal was to understand the monotone complexity of transparent polynomials, a concept that was recently introduced by Hrubeš & Yehudayoff (2021). In that context, we show that transparent polynomials with large support are hard for the monotone analogues of all the known definitions of , except for the one due to Poizat.

Abstract

n personalizing interactions with smart devices, identifying keywords in an open-vocabulary context is crucial. Previous methods for open-vocabulary keyword spotting relied on a shared embedding space created by audio and text encoders. However, they suffered from heterogeneous modality representations, causing audio-text mismatch. To tackle this issue, our proposed framework utilizes knowledge from a pre-trained text-to-speech (TTS) system. This knowledge transfer incorporates awareness of audio projections into text representations derived from the text encoder. Consequently, this approach aids in preventing false triggers in scenarios such as closely related pronunciations of audio-text pairs. Additionally, our proposed approach benefits from the keyword embedding calculation only during keyword enrollment phase. The proposed system gives consistent performance across all the word lengths.

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Abstract

Many real-world speech processing applications depend on the accurate estimation of epoch location. In a real-world scenario, speech may contain multiple speakers or multiple emotions in a single utterance. In the literature, the performance of epoch extraction algorithm was evaluated on speech utterance consisting of a single speaker or single emotion alone. In this study, speech utterance from different speakers, and emotions are combined to simulate multi-speaker and multi-emotion scenario, respectively. Five state-of-the-art epoch extraction methods were used to evaluate the performance for multi-speaker, multi-emotion, and voice disorder scenarios. We also analysed the performance of state-of-the-art epoch extraction methods after applying the region-wise approach on existing methods. CMU arctic, and Berlin emotional database is used in this study to simulate the multi-speaker, and multi-emotion scenarios, respectively for analysing the performance of epoch extraction algorithms. Saarbruecken voice disorder database is used to perform the experiments for voice disorder scenario. The result of this study indicates that performance of state-of-the art epoch extraction methods (which depend on pitch period) degrades in terms of false alarm rate (FAR) for these three scenarios. By applying the region-wise approach on these methods for extraction of epoch locations, FAR is reduced by 5% to 8% for the real-world scenario. It was also observed from this study, that the performance of dynamic programming based methods remains unchanged even after applying the region-wise approach on existing methods.

Abstract

The language acquisition skills are predominant during childhood. Due to the lack of resources, a gap often arises in the language communication abilities of students. To bridge this gap, it is necessary to consider automatic methods for improving the children language skills. Our IIITH Ucchar e-Sudharak web based tool, deployed in a real environment, helps schoolgoing children to practice English reading skills with a teacher in the loop. The choice of English as the target language is strategic, given its status as a global lingua franca. Within the tool, children are provided with class and chapter-specific English sentences for practice tailored by their teachers. During practice sessions, the tool provides real-time feedback in the form of sentence and word-level scores, which are computed by comparing against teacher (expert) audio recordings. These scores offer insights into word-level pronunciation correctness and sentence-level quality by measuring fluency. Moreover, the tool provides a key feature where teachers can assign specific sentences to individual students and assess their performance. This functionality empowers teachers to personalize their instruction, catering to the specific needs of each student. As far as we know, this is a one-of-a-kind tool specifically designed to meet the needs of school-going children.

Abstract

The Robotic Mobile Fulfillment System (RMFS) is an automated technology to fulfill various types of orders (e.g., vehicle assembly) in modern warehouses or factories. In particular, battery-equipped mobile robots move racks that contain various components (Stock Keeping Unit) by visiting replenishment or picking stations to complete orders. We formulate the preliminary system model to optimize the order throughput and energy consumption of mobile robots, which consists of (i) rack allocation, (ii) task allocation, and (iii) route routing.

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Abstract

This paper explores how real-world geopolitics can influence financial decision-making in virtual environments. The study primarily focuses on trading dynamics in a pandemic simulation game. The players manage resources and trade with each other to save their population. We compared two groups: one playing with made-up country names (control) and another with real country names (test). The players were not permitted to communicate outside of the resources the game provides and were finally ranked based on the number of months survived and trade volume. Our findings reveal two distinct player archetypes, one focusing on maximising trade volume and a second focusing on resource conservation. Real country names compared to color-coded labels seem to delay the perception of competition and reduce overall trade volume, suggesting increased immersion and a more conservative approach towards resource management. Observations from gameplay footage reveal instances of potential bias, with players forming diplomatic ties based on national identities. The data collected suggests that real-world geopolitics can influence financial behaviour in virtual environments as hypothesised. It also explores virtual environments to study how political narratives and dynamics affect human decision-making. The findings offer practical insights for game developers regarding fostering player engagement, mitigating bias, and creating fair economies. While parallels with real-world resource trading and allocation during pandemics were noted, further analysis would be required to explore these connections.