Abstract

This paper contributes to the expanding research on toxicity in online games, exploring the relationship between game design and player behaviours in India. Using an ethnographic approach and in-depth interviews with gamers, we capture firsthand accounts of toxic gaming experiences, delving into game design, game mechanics, and player interactions intersecting to shape player behaviours. While previous research on toxicity in gaming environments has largely focused on the role of game design, aspects of gender and, to a lesser extent, race, primarily within the context of the Global North, our study shifts the focus to India and, by extension, the Global South. We seek to uncover broader dimensions of gaming toxicity that go beyond design and gender. Our analysis examines the underlying factors contributing to the prevalence and persistence of toxic behaviours in Indian gaming environments by highlighting the influence of cultural and contextual elements in producing toxic behaviours. Expanding the scope of research to include these overlooked aspects, our study aims to provide a more comprehensive understanding of the complexities of toxicity in online gaming spaces within the Indian context.

Abstract

Bidding in a periodic double auction (PDA) is challenging due to its sequential nature, where one needs to consider current as well as future auctions to decide the bids. Monte-Carlo Tree Search (MCTS), which is a state-of-the-art online planning algorithm for tackling sequential problems, seems a perfect fit for bidding in PDAs. However, the success stories of MCTS are largely limited to discrete action spaces, and its efficacy diminishes when dealing with continuous actions. Conventional methods often resort to overly simplistic discretizations that limit exploration and fail to provide valuable insights into unexplored actions. In this work, we propose a novel bidding strategy for PDAs, Regression-MCTS, that is built upon MCTS for a continuous action space of bid prices. Unlike conventional methods, our novel MCTS method leverages information obtained from explored actions to enhance the understanding of the larger action set within the continuous domain to place bids in the auctions, thus generalizing the information about action quality between a wider action space for faster learning. To test the efficacy of our proposed method, we design an efficient PDA simulator that closely resembles real-world PDAs. Our analysis verifies that the increase in the number of rollouts improves its performance. Furthermore, our experimental results demonstrate that our approach outperforms existing MCTS-based bidding strategies and the majority of state-of-the-art PDA bidding strategies, showcasing its superior performance in PDAs.

Abstract

Decision Transformers have demonstrated impressive performance in offline reinforcement learning, but their complex internal mechanisms remain challenging to interpret. This paper presents a comprehensive analysis of Decision Transformers(DT) trained on various MuJoCo environments using different interpretability techniques. Our analysis includes: (a) positional encoding (PE) analysis to assess the impact of temporal information on performance in different types of environments, (b) return-to-go (RTG) analysis to explore the variability in achieving specific returns, (c) embedding analysis revealing the models' ability to learn detailed representations of the environments, (d) attention visualizations to understand the role of attention in behaviour guidance, and (e) perturbation studies to test robustness to input noise. Our analysis reveals the extent to which positional information is necessary for achieving high performance in different MuJoCo environments and provides insights into the model's adaptability to varying task requirements. In addition, the embedding analysis shows hierarchical abstractions captured by the model across layers. Through these analyses, we provide novel insights into the decision-making process of transformers in continuous control domains. These insights are pivotal for designing more interpretable and reliable decision-making systems and deepen our comprehension of the capabilities and limitations of DT in complex tasks.

Abstract

This article presents ‘Teledrive’, a telepresence robotic system with embodied AI features that empowers an operator to navigate the telerobot in any unknown remote place with minimal human intervention. We conceive Teledrive in the context of democratizing remote ‘care-giving’ for elderly citizens as well as for isolated patients, affected by contagious diseases. In particular, this paper focuses on the problem of navigating to a rough target area (like ‘bedroom’ or ‘kitchen’) rather than pre-specified point destinations. This ushers in a unique ‘AreaGoal’ based navigation feature, which has not been explored in depth in the contemporary solutions. Further, we describe an edge computing-based software system built on a WebRTC-based communication framework to realize the aforementioned scheme through an easy-to-use speech-based human-robot interaction. Moreover, to enhance the ease of operation for the remote caregiver, we incorporate a ‘person following’ feature, whereby a robot follows a person on the move in its premises as directed by the operator. Moreover, the system presented is loosely coupled with specific robot hardware, unlike the existing solutions. We have evaluated the efficacy of the proposed system through baseline experiments, user study, and real-life deployment.

Abstract

Assistive agents performing household tasks such as making the bed or cooking breakfast often compute and execute actions that accomplish one task at a time. However, efficiency can be improved by anticipating upcoming tasks and computing an action sequence that jointly achieves these tasks. State-of-the-art methods for task anticipation use data-driven deep networks and Large Language Models (LLMs), but they do so at the level of high-level tasks and/or require many training examples. Our framework leverages the generic knowledge of LLMs through a small number of prompts to perform high-level task anticipation, using the anticipated tasks as goals in a classical planning system to compute a sequence of finer-granularity actions that jointly achieve these goals. We ground and evaluate our framework’s abilities in realistic scenarios in the VirtualHome environment and demonstrate a 31% reduction in execution time compared with a system that does not consider upcoming tasks.

Abstract

This paper presents a meticulous and well structured approach to annotating a corpus of Hindi spoken data. We deployed 4 annotators to augment the spoken section of the EMILLE Hindi corpus by marking the various linguistic phenomena observed in spoken data. Then we analyzed various phonological (sound deletion), morphological (code-mixing and reduplication) and syntactic phenomena (case markers and ambiguity), not attested in written data. Code mixing and switching constitute the majority of the phenomena we annotated, followed by orthographic errors related to symbols in the Devanagiri script. In terms of divergences from written form of Hindi, case marker usage, missing auxiliary verbs and agreement patterns are markedly distinct for spoken Hindi. The annotators also assigned a quality rating to each sentence in the corpus. Our analysis of the quality ratings revealed that most of the sentences in the spoken data corpus are of moderate to high quality. Female speakers produced a greater percentage of high quality sentences compared to their male counterparts. While previous efforts in corpus annotation have been largely focused on creating resources for engineering applications, we illustrate the utility of our dataset for scientific hypothesis testing. Inspired from the Surprisal Theory of language comprehension (Hale, 2001; Levy, 2008), we validate the hypothesis that sentences with high values of lexical surprisal are rated low in terms of quality by native speakers, even when controlling for sentence length and word frequencies in a sentence.

Abstract

Stuttering constitutes a pervasive speech disorder with a global impact on individuals. Detecting stuttering in speech would aid speech pathologists in monitoring fluency over time and enhance the quality of life for those with atypical speech patterns. Presently, the available public datasets tailored for stuttering identification are confined to the utterance level, with labels ascribed to individual utterances. However, in real-time situations, a considerable proportion of individuals grappling with stuttering manifest dysfluency at the syllable level. This limitation markedly constrains the effectiveness and applicability of dysfluency detection systems. In an effort to address this lacuna in knowledge, this study introduces a novel dataset comprising audio recordings directly obtained from native Kannada-speaking individuals afflicted with the stuttering disorder at the syllable level and also provides detailed information on the dataset's creation, including patient selection criteria and clip labelling. Additionally, benchmark models trained on this distinctive dataset are presented as part of this research endeavor.

Abstract

A method and system based on machine learning to create self-adapting analog circuits adapted to change their internal components on-the-fly in response to changes in process, voltage, and temperature to re-tune the electrical characteristics back to nominal specified values is disclose. The method and system herein comprise of designing the analog circuit, generating simulation data for machine learning, creating a full query database, creating and training, using simulation results, a machine learning (ML) model of the circuit and applying the ML model to infer the required changes to internal components of the analog circuit in response to changes in P, V, and T conditions. With this method and system, evaluation of the adverse effects of PVT changes, decision on internal circuit changes, and realization of requisite design changes are performed by the computer system solely within a ML data domain

Abstract

In this article, we propose a new practical synchronization protocol for multiple Euler–Lagrange 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

Particulate matter (PM) is a critical air pollutant with severe health implications, yet existing stationary monitoring networks often fail to capture its complete spatial and temporal variability in urban environments. This paper presents a novel approach to city-wide air quality monitoring using low-cost sensors mounted on mobile platforms. To validate this method, a six-month field study was conducted in Hyderabad, India, deploying IoT-enabled devices on four college buses. These mobile devices capture PM concentrations, temperature, relative humidity, GPS coordinates, and vehicle speed twice daily across different parts of the city. The primary objective was to demonstrate the necessity of mobile air pollution monitoring to identify PM variability across diverse urban areas. The data revealed significant variations in PM concentrations across different parts of the city and seasons, highlighting the impact of local activities on air quality. The study examines seasonal trends, area-specific variations, and temporal patterns of PM concentrations, identifying pollution hotspots within the city. It shows how important it is to provide up-to-date, location-specific air quality information to people with pollution-related health issues.