Existing learning-from-crowds methods aim to design proper aggregation strategies to infer the unknown true labels from noisy labels provided by crowdsourcing. They treat the ground truth as hidden variables and use statistical or deep learning based worker behavior models to infer the ground truth. However, worker behavior models that rely on ground truth hidden variables overlook workers' behavior at the item feature level, leading to imprecise characterizations and negatively impacting the quality of learning-from-crowds. This paper proposes a new paradigm of multi-task supervised learning-from-crowds, which eliminates the need for modeling of items's ground truth in worker behavior models. Within this paradigm, we propose a worker behavior model at the item feature level called Mixture of Experts based Multi-task Supervised Learning-from-Crowds (MMLC), then, two aggregation strategies are proposed within MMLC. The first strategy, named MMLC-owf, utilizes clustering methods in the worker spectral space to identify the projection vector of the oracle worker. Subsequently, the labels generated based on this vector are regarded as the items's ground truth The second strategy, called MMLC-df, employs the MMLC model to fill the crowdsourced data, which can enhance the effectiveness of existing aggregation strategies . Experimental results demonstrate that MMLC-owf outperforms state-of-the-art methods and MMLC-df enhances the quality of existing learning-from-crowds methods.

In crowdsourcing, existing efforts mainly use real datasets collected from crowdsourcing as test datasets to evaluate the effectiveness of aggregation algorithms. However, these work ignore the fact that the datasets obtained by crowdsourcing are usually sparse and imbalanced due to limited budget. As a result, applying the same aggregation algorithm on different datasets often show contradicting conclusions. For example, on the RTE dataset, Dawid and Skene model performs significantly better than Majority Voting, while on the LableMe dataset, the experiments give the opposite conclusion. It is challenging to obtain comprehensive and balanced datasets at a low cost. To our best knowledge, little effort have been made to the fair evaluation of aggregation algorithms. To fill in this gap, we propose a novel method named TDG4Crowd that can automatically generate comprehensive and balanced datasets. Using Kullback Leibler divergence and Kolmogorov–Smirnov test, the experiment results show the superior of our method compared with others. Aggregation algorithms also perform more consistently on the synthetic datasets generated using our method.

Empirical results have shown that deep neural networks achieve superior performance in the application of Knowledge Tracing. However, the design of recurrent cells like long short term memory (LSTM) cells or gated recurrent units (GRU) is influenced largely by applications in natural language processing. They were proposed and evaluated in the context of sequence to sequence modeling, like machine translation. Even though the LSTM cell works well for knowledge tracing, it is unknown if its architecture is ideally suited for knowledge tracing. Despite the fact that there are several recurrent neural network based architectures proposed for knowledge tracing, the methodologies rely on empirical observations and trial and error, which may not be efficient or scalable. In this study, we investigate using reinforcement learning for the automatic design of recurrent neural network cells for knowledge tracing, showing improved performance compared to the LSTM cell. We also discuss a potential method for model regularization using neural architecture search.

Abstract The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately, this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. In this study, we use deep transfer learning for face swapping detection, showing true positive rates greater than 96% with very few false alarms. Distinguished from existing methods that only provide detection accuracy, we also provide uncertainty for each prediction, which is critical for trust in the deployment of such detection systems. Moreover, we provide a comparison to human subjects. To capture human recognition performance, we build a website to collect pairwise comparisons of images from human subjects. Based on these comparisons, we infer a consensus ranking from the image perceived as most real to the image perceived as most fake. Overall, the results show the effectiveness of our method. As part of this study, we create a novel dataset that is, to the best of our knowledge, the largest swapped face dataset created using still images. This dataset will be available for academic research use per request. Our goal of this study is to inspire more research in the field of image forensics through the creation of a dataset and initial analysis.

Arterial oxygen saturation ([Formula: see text]) is an indicator of how much oxygen is carried by hemoglobin in the blood. Having enough oxygen is vital for the functioning of cells in the human body. Measurement of [Formula: see text] is typically estimated with a pulse oximeter, but recent works have investigated how smartphone cameras can be used to infer [Formula: see text]. In this paper, we propose methods for the measurement of [Formula: see text] with a smartphone using convolutional neural networks and preprocessing steps to better guard against motion artifacts. To evaluate this methodology, we conducted a breath-holding study involving 39 participants. We compare the results using two different mobile phones. We compare our model with the ratio-of-ratios model that is widely used in pulse oximeter applications, showing that our system has significantly lower mean absolute error (2.02%) than a medical pulse oximeter.