The paper investigates how ambiguous data and cognitive biases affect machine learning in humanitarian decision making.
problem Ambiguous data and cognitive biases impact the interpretability of machine learning models in humanitarian decision making.
method The study will explore the effects of data ambiguity and cognitive biases on machine learning algorithms in humanitarian contexts.
result The research aims to uncover the specific ways in which ambiguous data and cognitive biases influence the interpretability of machine learning models in humanitarian decision making.
System builds Somali ASR for UN humanitarian efforts.
problem Developing ASR for under-resourced Somali language.
method Acoustic model training with annotated speech, neural architectures, language model data augmentation, acoustic data perturbation.
result Best system achieved 53.75% word error rate.
Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal resolution, or are significantly delayed. In this paper, we describe how geoloc…
Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challen…
Paper proposes evaluation methods for climate change illustrations.
problem Lack of metrics to compare realism in conditional generative models.
method Adapted and assessed several existing metrics, including FID.
result FID with Inception-V3 embeddings correlates best with human realism.
CNN-DTW system improves keyword spotting in under-resourced languages.
problem Keyword spotting in nearly zero-resource languages.
method Multilingual bottleneck features, CNN-DTW, DTW template matching, convolutional neural network.
result Multilingual BNFs improve CNN-DTW by 10.9%.
Develops a graph-based convolutional network for multi-view networks to improve poverty research.
problem Binary treatment of social network relations in graph learning models.
method Multi-GCN: Graph Convolutional Networks for Multi-View Networks.
result Multi-GCN outperforms state-of-the-art algorithms on poverty prediction tasks and broader multi-view network tasks.
Study forecasts food security trends using real-time data.
problem Food insecurity prediction for sub-national regions.
method Quantitative methodology combining various machine learning models.
result Reservoir Computing model performs best in food security prediction.
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.
problem Effective supply chain management under high demand variability.
method Combines dynamic Negative Binomial demand modeling with endogenous smart contract adoption optimization.
result The NB model outperforms other benchmarks in forecasting and optimizing smart contract adoption and order quantity.
Improved ASR-free keyword spotting in under-resourced languages.
problem Dynamic time warping for keyword spotting in languages with limited resources.
method Multilingual bottleneck extractor and correspondence autoencoder integration.
result More than 11% absolute improvement in ROC AUC over MFCCs.
Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.
problem Limited use of range-based volatility estimators in local commodity markets.
method Adapted OHLC volatility estimators to monitor market distress across various contexts.
result OHLC-based volatility indicators detect market disruptions missed by standard momentum indicators.