Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Source Localisation in Wireless Sensor Networks Based on Optimised Maximum Likelihood

Maximum likelihood (ML) is a popular and effective estimator for a wide range of diverse applications and currently affords the most accurate estimation for source localisation in wireless sensor networks (WSN). ML however has two major shortcomings namely, that it is a biased estimator and is also highly sensitive to parameter perturbations. An Optimisation to ML (OML) algorithm was introduced that minimises the sum-of-squares bias and exhibits superior performance to ML in statistical estimation, particularly with finite datasets. This paper proposes a new model for acoustic source localisation in WSN, based upon the OML estimation process. In addition to the performance analysis using real world field experimental data for the tracking of moving military vehicles, simulations have been performed upon the more complex source localisation and tracking problem, to verify the potential of the new OML-based model

UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research

Source Localisation in Wireless Sensor Networks Based on Optimised Maximum Likelihood

Maximum likelihood (ML) is a popular and effective estimator for a wide range of diverse applications and currently affords the most accurate estimation for source localisation in wireless sensor networks (WSN). ML however has two major shortcomings namely, that it is a biased estimator and is also highly sensitive to parameter perturbations. An Optimisation to ML (OML) algorithm was introduced that minimises the sum-of-squares bias and exhibits superior performance to ML in statistical estimation, particularly with finite datasets. This paper proposes a new model for acoustic source localisation in WSN, based upon the OML estimation process. In addition to the performance analysis using real world field experimental data for the tracking of moving military vehicles, simulations have been performed upon the more complex source localisation and tracking problem, to verify the potential of the new OML-based model

TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models

Pre-trained large language models have recently achieved ground-breaking performance in a wide variety of language understanding tasks. However, the same model can not be applied to multimodal behavior understanding tasks (e.g., video sentiment/humor detection) unless non-verbal features (e.g., acoustic and visual) can be integrated with language. Jointly modeling multiple modalities significantly increases the model complexity, and makes the training process data-hungry. While an enormous amount of text data is available via the web, collecting large-scale multimodal behavioral video datasets is extremely expensive, both in terms of time and money. In this paper, we investigate whether large language models alone can successfully incorporate non-verbal information when they are presented in textual form. We present a way to convert the acoustic and visual information into corresponding textual descriptions and concatenate them with the spoken text. We feed this augmented input to a pre-trained BERT model and fine-tune it on three downstream multimodal tasks: sentiment, humor, and sarcasm detection. Our approach, TextMI, significantly reduces model complexity, adds interpretability to the model's decision, and can be applied for a diverse set of tasks while achieving superior (multimodal sarcasm detection) or near SOTA (multimodal sentiment analysis and multimodal humor detection) performance. We propose TextMI as a general, competitive baseline for multimodal behavioral analysis tasks, particularly in a low-resource setting

ANISOTROPIC BIANCHI TYPE-I COSMOLOGICAL MODEL FOR VISCOUS FLUID IN A MODIFIED BRANS-DICKE COSMOLOGY

ABSTRACT We present a new Cosmological solution for a Bianchi type-I Cosmological model filled with viscous fluid in a modified Brans-Dicke theory in which the variable cosmological term is an explicit function of a scalar field. The physical and geometrical properties of this model have been discussed. Finally, this model has been transform to the original form (1961) of Bras-Dicke theory

Social Accountability of Microfinance Institutions in South Asian Region

The purpose of the study is to examine the social accountability and argue comparison of outreach level of microfinance institutions in member countries of South Asian Association for Regional Cooperation (SAARC). The inquiry has employed quantitative research approach to meticulous secondary data that has quantify using financial ratio and multiple regression analysis. Our results expose that gross loan portfolio has significant positive relation with the number of clients served. Conversely, the average loan balance per borrower per GNI per capital and average outstanding balance have significant negative relation to the dependent variable. On the other hand, the yield on a gross loan portfolio, size of MFI and operational self-sufficiency has insignificant effect to the number of active borrowers. Eventually the study found no evidence of trade-off between profitability and outreach breadth. However, interest rate, board and ownership structure and outreach depth issue suggested for the further studies. Keywords: Microfinance, Accountability, Outreach, Mission Drift, South Asia JEL Classifications: G21, G32, O1

Social accountability of microfinance institutions in South Asian Region

The purpose of the study is to examine the social accountability and argue comparison of outreach level of microfinance institutions (MFIs) in member countries of South Asian Association for Regional Cooperation. The inquiry has employed quantitative research approach to meticulous secondary data that has quantify using financial ratio and multiple regression analysis. Our results expose that gross loan portfolio (GLP) has significant positive relation with the number of clients served. Conversely, the average loan balance per borrower per gross national income per capital and average outstanding balance have significant negative relation to the dependent variable.On the other hand, the yield on a GLP, size of MFIs and operational self-sufficiency has insignificant effect to the number of active borrowers. Eventually the study found no evidence of trade-off between profitability and outreach breadth.However, interest rate, board and ownership structure and outreach depth issue suggested for the further studies