--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. Transferring one person's face from a source image to a target image of another person, while keeping the image photo-realistic overall has become increasingly easy and automatic, even for individuals without much knowledge of image processing. In this study, we use deep transfer learning for face swapping detection, showing true positive rates 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. T o capture human recognition performance, we build a website to collect pairwise comparisons of images from human subjects. Based on these comparisons, images are ranked from most real to most fake. We compare this ranking to the outputs from our automatic model, showing good, but imperfect, correspondence with linear correlations 0 . Overall, the results show the effectiveness of our method. As part of this study, we create a novel, publicly available dataset that is, to the best of our knowledge, the largest public swapped face dataset created using still images. Our goal of this study is to inspire more research in the field of image forensics through the creation of a public dataset and initial analysis. Face swapping refers to the process of transferring one person's face from a source image to another person in a target image, while maintaining photo-realism.

The Monte Carlo dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions. This approach was recently applied to Fault Detection and Di-agnosis (FDD) applications to improve the classification performance on incipient faults. In this paper, we propose a novel approach of augmenting the classification model with an additional unsupervised learning task. We justify our choice of algorithm design via an information-theoretical analysis. Our experimental results on three datasets from diverse application domains show that the proposed method leads to improved fault detection and diagnosis performance, especially on out-of-distribution examples including both incipient and unknown faults.

The widespread use of machine learning models, especially within the context of decision-making systems impacting individuals, raises many ethical issues with respect to fairness and interpretability of these models. While the research in these domains is booming, very few works have addressed these two issues simultaneously. To solve this shortcoming, we propose FairCORELS, a supervised learning algorithm whose objective is to learn at the same time fair and interpretable models. FairCORELS is a multi-objective variant of CORELS, a branch-and-bound algorithm, designed to compute accurate and interpretable rule lists. By jointly addressing fairness and interpretability, FairCORELS can achieve better fairness/accuracy tradeoffs compared to existing methods, as demonstrated by the empirical evaluation performed on real datasets. Our paper also contains additional contributions regarding the search strategies for optimizing the multi-objective function integrating both fairness, accuracy and interpretability.

We consider the problem of the recovery of a k-sparse vector from compressed linear measurements when data are corrupted by a quantization noise. When the number of measurements is not sufficiently large, different $k$-sparse solutions may be present in the feasible set, and the classical l1 approach may be unsuccessful. For this motivation, we propose a non-convex quadratic programming method, which exploits prior information on the magnitude of the non-zero parameters. This results in a more efficient support recovery. We provide sufficient conditions for successful recovery and numerical simulations to illustrate the practical feasibility of the proposed method.

In natural language understanding components, detecting out-of-domain (OOD) inputs is important for dialogue systems since wrongly accepting these OOD utterances that are not currently supported may lead to catastrophic failures of the entire system. Entropy regularization is an effective solution to avoid such failures, however, its computation heavily depends on OOD data, which are expensive to collect. In this paper, we propose a novel text generation model to produce high-quality OOD samples and thereby improve the performance of OOD detection. The proposed model can also utilize a set of unlabeled data to improve the effectiveness of these generated OOD samples. Experiments show that our method can effectively improve the OOD detection performance of a NLU module. 1 Introduction Natural Language Understanding (NLU) in dialog systems, particularly including task-oriented dialog systems and intelligent personal assistants, is vital for understanding users' inputs and making effective interactions. NLU maps text inputs to structured user intents, and decides the downstream processing pipelines of a dialog system, thereby becoming a precursor for the success of such systems. Recently, various deep neural network (DNN) based NLU models have been proposed and applied in real-world applications (Kim et al., 2018; Sarikaya, 2017; Y oo et al., 2018). Most existing DNN based NLU modules are built by following a closed-world assumption (Fei and Liu, 2016), i.e, the data used in the training and test phrase are drawn from the same distribution. However, such an assumption is commonly violated in practical systems that are deployed in a dynamic or open environment. Specifically, practical NLU systems often encounter o ut-o f-d omain (OOD) inputs that are not supported by the system and thus not observed in the training data.

Most of the existing algorithms fail to properly address the issues stemming from a large number of features. In particular, outlier detection algorithms perform poorly on data set of small size with a large number of features. In this paper, we propose a novel outlier detection algorithm based on principal component analysis and kernel density estimation. The proposed method is designed to address the challenges of dealing with high-dimensional data by projecting the original data onto a smaller space and using the innate structure of the data to calculate anomaly scores for each data point. Numerical experiments on synthetic and real-life data show that our method performs well on high-dimensional data. Our method also produces better-than-average execution times compared to the benchmark methods. Despite the growing amount of data that has become available for research and discovery there remain areas where certain type of data is scarce. In the fields such as medical diagnostics, network intrusion detection, fraudulent financial transactions and many others, deviations from normal behavior, i.e. anomalies, are rare. However, often, these events are of the greatest importance. For example, it would be extremely beneficial to determine if a person has an illness based on abnormal lab results.

During the Parkland, Florida, school shooting in 2018, the shooter was caught on a security camera pulling his rifle out of a duffle bag in the staircase 15 seconds before discharging the first round. However, the School Resource Officer didn't enter the building because he wasn't confident about the situation, and the Coral Springs Police Department had no idea what the shooter even looked like until 7 minutes and 30 seconds after the first round was fired. If the video system had included technology to recognize the gun threat in real time, alerts could have been sent to the security team. An announcement could have been made right away for all students and faculty in Building 12 to barricade their doors, and law enforcement could have responded a lot faster to a real-time feed of timely and accurate information. Aegis AI offers such a technology, which the company says enables existing security cameras to automatically recognize gun threats and notify security in real-time.

Freezing of gait (FoG) is a common gait disability in Parkinson's disease, that usually appears in its advanced stage. Freeze episodes are associated with falls, injuries, and psychological consequences, negatively affecting the patients' quality of life. For detecting FoG episodes automatically, a highly accurate detection method is necessary. This paper presents an approach for detecting FoG episodes utilizing a deep recurrent neural network (RNN) on 3D-accelerometer measurements. We investigate suitable features and feature combinations extracted from the sensors' time series data. Specifically, for detecting FoG episodes, we apply a deep RNN with Long Short-Term Memory cells. In our experiments, we perform both user dependent and user independent experiments, to detect freeze episodes. Our experimental results show that the frequency domain features extracted from the trunk sensor are the most informative feature group in the subject independent method, achieving an average AUC score of 93%, Specificity of 90% and Sensitivity of 81%. Moreover, frequency and statistical features of all the sensors are identified as the best single input for the subject dependent method, achieving an average AUC score of 97%, Specificity of 96% and Sensitivity of 87%. Overall, in a comparison to state-of-the-art approaches from literature as baseline methods, our proposed approach outperforms these significantly.

I know this is a joke but the answer is usually the company that bought the automatic toilet chose the cheapest option they could find. Vision-based neural networks trained on detecting humans on toilets prob have a 1% false positive rate.

In this paper, we study the problem of precision matrix estimation when the dataset contains sensitive information. In the differential privacy framework, we develop a differentially private ridge estimator by perturbing the sample covariance matrix. Then we develop a differentially private graphical lasso estimator by using the alternating direction method of multipliers (ADMM) algorithm. The theoretical results and empirical results that show the utility of the proposed methods are also provided. Keywords differential privacy, graphical model, ADMM algorithm 1 Introduction Precision matrix plays a fundamental role in many statistical inference problems. For example, in discriminant analysis, the precision matrix needs to be estimated to compute the classification rules[1]. In graphical models, the structure exploration of gaussian graphical model is equivalent to recover the support of the precision matrix[2]. Moreover, the precision matrix is useful for a wide range of applications including portfolio optimization, genomics and single processing, among many others. Therefore, it is of great importance to estimate the precision matrix.