arXiv subjects

Albert Ali Salah

Publications and source records attributed to Albert Ali Salah.

23 records · Page 2Linked to original sources

arXiv2020

Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion Recognition

Acoustic and linguistic analysis for elderly emotion recognition is an under-studied and challenging research direction, but essential for the creation of digital assistants for the elderly, as well as unobtrusive telemonitoring of elderly in their residences for mental healthcare purposes. This paper presents our contribution to the INTERSPEECH 2020 Computational Paralinguistics Challenge (ComParE) - Elderly Emotion Sub-Challenge, which is comprised of two ternary classification tasks for arousal and valence recognition. We propose a bi-modal framework, where these tasks are modeled using state-of-the-art acoustic and linguistic features, respectively. In this study, we demonstrate that exploiting task-specific dictionaries and resources can boost the performance of linguistic models, when the amount of labeled data is small. Observing a high mismatch between development and test set performances of various models, we also propose alternative training and decision fusion strategies to better estimate and improve the generalization performance.

cs.CL↗

arXiv2020

Mobile phone data and COVID-19: Missing an opportunity?

This paper describes how mobile phone data can guide government and public health authorities in determining the best course of action to control the COVID-19 pandemic and in assessing the effectiveness of control measures such as physical distancing. It identifies key gaps and reasons why this kind of data is only scarcely used, although their value in similar epidemics has proven in a number of use cases. It presents ways to overcome these gaps and key recommendations for urgent action, most notably the establishment of mixed expert groups on national and regional level, and the inclusion and support of governments and public authorities early on. It is authored by a group of experienced data scientists, epidemiologists, demographers and representatives of mobile network operators who jointly put their work at the service of the global effort to combat the COVID-19 pandemic.

cs.CY↗

arXiv2019

Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos

Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.

cs.CV↗

arXiv2018

Data for Refugees: The D4R Challenge on Mobility of Syrian Refugees in Turkey

The Data for Refugees (D4R) Challenge is a non-profit challenge initiated to improve the conditions of the Syrian refugees in Turkey by providing a special database to scientific community for enabling research on urgent problems concerning refugees, including health, education, unemployment, safety, and social integration. The collected database is based on anonymised mobile Call Detail Record (CDR) of phone calls and SMS messages from one million Turk Telekom customers. It indicates broad activity and mobility patterns of refugees and citizens in Turkey for one year. The data collection period is from 1 January 2017 to 31 December 2017. The project is initiated by Turk Telekom, in partnership with the Turkish Academic and Research Council (TUBITAK) and Bogazici University, and in collaboration with several academic and non-governmental organizations, including UNHCR Turkey, UNICEF, and International Organization for Migration.

cs.CY↗

arXiv2015

Adaptive Mixtures of Factor Analyzers

A mixture of factor analyzers is a semi-parametric density estimator that generalizes the well-known mixtures of Gaussians model by allowing each Gaussian in the mixture to be represented in a different lower-dimensional manifold. This paper presents a robust and parsimonious model selection algorithm for training a mixture of factor analyzers, carrying out simultaneous clustering and locally linear, globally nonlinear dimensionality reduction. Permitting different number of factors per mixture component, the algorithm adapts the model complexity to the data complexity. We compare the proposed algorithm with related automatic model selection algorithms on a number of benchmarks. The results indicate the effectiveness of this fast and robust approach in clustering, manifold learning and class-conditional modeling.

stat.ML↗