Abstract

Lack of diverse perspectives causes neutrality bias in Wikipedia content leading to millions of worldwide readers getting exposed by potentially inaccurate information. Hence, neutrality bias detection and mitigation is a critical problem. Although previous studies have proposed effective solutions for English, no work exists for Indian languages. First, we contribute two large datasets, mWIKIBIAS and mWNC, covering 8 languages, for the bias detection and mitigation tasks respectively. Next, we investigate the effectiveness of popular multilingual Transformer-based models for the two tasks by modeling detection as a binary classification problem and mitigation as a style transfer problem. We make the code and data publicly available.

Abstract

The intentional binding (IB) phenomenon reflects a perceived attraction between a voluntary action and an intendedconsequence. Recent research has shown that during IB, intended outcomes expand in time. Although, this effect was significant only for shorter action-outcome delays. However, literature on IB suggests it can also exist for long delays. To address this, we implemented a temporal reproduction task to observe the expansion of an intended outcome. Results revealed the expansion of an intended outcome in the shorter and longer action-outcome delays. These results were discussed under the PIDI (proximal intent distal intent) framework since, using this methodology, both types of intention can be operationalised. Proximal for shorter delays under the motor action, and distal for longer delays as the inference made towards the objective duration to be reproduced. Our implementation allows a robust way to observe the temporal dynamics of an intended outcome, irrespective of the action-outcome delay.

Abstract

The phenomenon of intentional binding pertains to the perceived connection between a voluntary action and its anticipated result. When an individual intends an outcome, it appears to subjectively extend in time due to a pre-activation of the intended result, particularly evident at shorter action-outcome delays. However, there is a concern that the operationalization of intention might have led to a mixed interpretation of the outcome expansion attributed to the pre-activation of intention, given the sensitivity of time perception and intentional binding to external cues that could accelerate the realization of expectations. To investigate the expansion dynamics of an intended outcome, we employed a modified version of the temporal bisection task in two experiments. Experiment 1 considered the action-outcome delay as a within-subject factor, while experiment 2 treated it as a between-subject factor. The results revealed that the temporal expansion of an intended outcome was only evident under the longer action-outcome delay condition. We attribute this observation to working memory demands and attentional allocation due to temporal relevancy, and not due to pre-activation. The discrepancy in effects across studies is explained by operationalizing different components of the intentional binding effect, guided by the cue integration theory. Moreover, we discussed speculative ideas regarding the involvement of specific intentions based on the proximal intent distal intent (PIDI) theory, and whether causality plays a role in temporal binding. Our study contributes to the understanding of how intention influences time perception and sheds light on how various methodological factors, cues, and delays can impact the dynamics of temporal expansion associated with an intended outcome.

Abstract

No Results Found

Abstract

Crucial phases in aerial transportation and de- livery of suspended payloads are the clasping and unclasping of the payload to the cable. During these phases, along with the uncertainties in the quadrotor and in the environment, the inevitable payload swings induced by the human interaction or by other external interaction will create additional state- dependent uncertainties; such uncertainties pose a significant challenge in terms of control. If they continue unabated, these uncertainties can cause safety hazard for the quadrotor, the payload and, most importantly, for the human operating the clasping/unclasping tasks. As the state-of-the-art adaptive controllers cannot tackle such uncertainties or considers them as bounded terms, this paper presents an adaptive anti-swing controller where all uncertainties are taken in a state-dependent form. This choice is made to better capture uncertain clasping and unclasping operations of the suspended payload. The closed-loop stability is studied analytically and the real-time experiments confirm significant performance improvements for the proposed scheme over the state of the art.

Abstract

Semantic segmentation provides a pixel-level understanding of an image essential for various scene-understanding vision tasks. However, semantic segmentation models demand significant computational resources during training and inference. These requirements pose a challenge in resource-constraint scenarios. To address this issue, we present a compression algorithm based on differentiable meta-pruning through hypernetwork: MPHyp. Our proposed method MPHyp utilizes hypernetworks that take latent vectors as input and output weight matrices for the segmentation model. L1 sparsification follows the proximal gradient optimizer, updates the latent vectors and introduces sparsity leading to automatic model pruning. The proposed method offers the benefit of achieving controllable compression during the training and significantly reducing the training time. We compare our methodology with a popular pruning approach and demonstrate its efficacy by reducing the number of parameters and floating point operations while maintaining the mean Intersection over Union (mIoU) metric. We conduct experiments on two widely accepted semantic segmentation architectures: UNet and ERFNet. Our experiments and ablation study demonstrate the effectiveness of our proposed methodology by achieving efficient and reasonable segmentation results.

Abstract

Water quality modelling has proved to be effective method for managing river water quality. But the most effective and comprehensive approach involving integration of river water quality simulation and pollution visualization with the objective of pollution reduction and maintenance of environmental flow strategies has gained less attention. Thus, the objective of this study was to employ an integrated approach for mapping and analysing river water quality under various hydro-climatic and pollution scenarios. Specifically, this approach involved the integration of a river water quality simulation model, QUAL2K, Global Environmental Flow Calculator (GEFC), and Geographical Information System (GIS) to develop water quality index (WQI) based map charts of water quality. The calibrated QUAL2K model was utilized to simulate WQI parameters including water temperature, pH, electrical conductivity, dissolved oxygen (DO), biological oxygen demand (BOD), nitrates (NO3), ammonia (NH4), and alkalinity. To analyse the WQI, the Weighted Arithmetic-Water Quality Index (WA-WQI) method was employed for various individual and combined pollution scenarios, environmental flow (Eflow), and climate change scenarios. The developed integrated approach was applied to the Bhadravati segment of Bhadra River, India. The findings revealed that the prevailing WQI status of the study stretch ranged from poor to unsuitable for drinking purposes. This deterioration can be attributed to the impact of both industrial and municipal effluents. By maintaining the effective Environmental Management Class (EMC) flow rates (class C flowrate of EMC (40.32 m3/s)) in conjunction with appropriate Pollution Reduction (PR) level (10% PR) at headwater and incoming drains, the stream self-purification capacity was enhanced resulting in the Bhadravati River stretch water quality transitioning to favourable water quality condition.

Abstract

In an effort to address the training instabilities of GANs, we introduce a class of dual-objective GANs with different value functions (objectives) for the generator (G) and discriminator (D). In particular, we model each objective using α-loss, a tunable classification loss, to obtain (αD,αG)-GANs, parameterized by (αD,αG)∈(0,∞]2. For sufficiently large number of samples and capacities for G and D, we show that the resulting non-zero sum game simplifies to minimizing an f-divergence under appropriate conditions on (αD,αG). In the finite sample and capacity setting, we define estimation error to quantify the gap in the generator's performance relative to the optimal setting with infinite samples and obtain upper bounds on this error, showing it to be order optimal under certain conditions. Finally, we highlight the value of tuning (αD,αG) in alleviating training instabilities for the synthetic 2D Gaussian mixture ring and the Stacked MNIST datasets.

Abstract

No Results Found

Abstract

No Results Found