pwc-archive/papers-with-abstracts huggingface dataset:
Internet of Things (IoT) Based Video Analytics: a use case of Smart Doorbell
Authors: Shailesh Arya
Date: 2021-05-13 00:00:00
Tasks: vehicle detection

The vision of the internet of things (IoT) is a reality now. IoT devices are getting cheaper, smaller. They are becoming more and more computationally and energy-efficient. The global market of IoT-based video analytics has seen significant growth in recent years and it is expected to be a growing market segment. For any IoT-based video analytics application, few key points required, such as cost-effectiveness, widespread use, flexible design, accurate scene detection, reusability of the framework. Video-based smart doorbell system is one such application domain for video analytics where many commercial offerings are available in the consumer market. However, such existing offerings are costly, monolithic, and proprietary. Also, there will be a trade-off between accuracy and portability. To address the foreseen problems, I'm proposing a distributed framework for video analytics with a use case of a smart doorbell system. The proposed framework uses AWS cloud services as a base platform and to meet the price affordability constraint, the system was implemented on affordable Raspberry Pi. The smart doorbell will be able to recognize the known/unknown person with at most accuracy. The smart doorbell system is also having additional detection functionalities such as harmful weapon detection, noteworthy vehicle detection, animal/pet detection. An iOS application is specifically developed for this implementation which can receive the notification from the smart doorbell in real-time. Finally, the paper also mentions the classical approaches for video analytics, their feasibility in implementing with this use-case, and comparative analysis in terms of accuracy and time required to detect an object in the frame is carried out. Results conclude that AWS cloud-based approach is worthy for this smart doorbell use case.
Waste detection in Pomerania: non-profit project for detecting waste in environment
Authors: Sylwia Majchrowska, Agnieszka Mikołajczyk, Maria Ferlin, Zuzanna Klawikowska, Marta A. Plantykow, Arkadiusz Kwasigroch, Karol Majek
Date: 2021-05-12 00:00:00
Tasks: Classification, Instance Segmentation, Object Detection, Self-Supervised Learning

Waste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, either for economic or ecological reasons, and the industry demands high efficiency. Our team conducted comprehensive research on Artificial Intelligence usage in waste detection and classification to fight the world's waste pollution problem. As a result an open-source framework that enables the detection and classification of litter was developed. The final pipeline consists of two neural networks: one that detects litter and a second responsible for litter classification. Waste is classified into seven categories: bio, glass, metal and plastic, non-recyclable, other, paper and unknown. Our approach achieves up to 70% of average precision in waste detection and around 75% of classification accuracy on the test dataset. The code used in the studies is publicly available online.
BNNpriors: A library for Bayesian neural network inference with different prior distributions
Authors: Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison
Date: 2021-05-14 00:00:00

Bayesian neural networks have shown great promise in many applications where calibrated uncertainty estimates are crucial and can often also lead to a higher predictive performance. However, it remains challenging to choose a good prior distribution over their weights. While isotropic Gaussian priors are often chosen in practice due to their simplicity, they do not reflect our true prior beliefs well and can lead to suboptimal performance. Our new library, BNNpriors, enables state-of-the-art Markov Chain Monte Carlo inference on Bayesian neural networks with a wide range of predefined priors, including heavy-tailed ones, hierarchical ones, and mixture priors. Moreover, it follows a modular approach that eases the design and implementation of new custom priors. It has facilitated foundational discoveries on the nature of the cold posterior effect in Bayesian neural networks and will hopefully catalyze future research as well as practical applications in this area.
Thompson Sampling for Gaussian Entropic Risk Bandits
Authors: Ming Liang Ang, Eloise Y. Y. Lim, Joel Q. L. Chang
Date: 2021-05-14 00:00:00
Tasks: Decision Making, Thompson Sampling

The multi-armed bandit (MAB) problem is a ubiquitous decision-making problem that exemplifies exploration-exploitation tradeoff. Standard formulations exclude risk in decision making. Risknotably complicates the basic reward-maximising objectives, in part because there is no universally agreed definition of it. In this paper, we consider an entropic risk (ER) measure and explore the performance of a Thompson sampling-based algorithm ERTS under this risk measure by providing regret bounds for ERTS and corresponding instance dependent lower bounds.
A Hypothesis Testing Approach to Nonstationary Source Separation
Authors: Reza Sameni, Christian Jutten
Date: 2021-05-14 00:00:00
Tasks: blind source separation, Clustering

The extraction of nonstationary signals from blind and semi-blind multivariate observations is a recurrent problem. Numerous algorithms have been developed for this problem, which are based on the exact or approximate joint diagonalization of second or higher order cumulant matrices/tensors of multichannel data. While a great body of research has been dedicated to joint diagonalization algorithms, the selection of the diagonalized matrix/tensor set remains highly problem-specific. Herein, various methods for nonstationarity identification are reviewed and a new general framework based on hypothesis testing is proposed, which results in a classification/clustering perspective to semi-blind source separation of nonstationary components. The proposed method is applied to noninvasive fetal ECG extraction, as case study.
Information-theoretic Evolution of Model Agnostic Global Explanations
Authors: Sukriti Verma, Nikaash Puri, Piyush Gupta, Balaji Krishnamurthy
Date: 2021-05-14 00:00:00
Tasks: Marketing, model

Explaining the behavior of black box machine learning models through human interpretable rules is an important research area. Recent work has focused on explaining model behavior locally i.e. for specific predictions as well as globally across the fields of vision, natural language, reinforcement learning and data science. We present a novel model-agnostic approach that derives rules to globally explain the behavior of classification models trained on numerical and/or categorical data. Our approach builds on top of existing local model explanation methods to extract conditions important for explaining model behavior for specific instances followed by an evolutionary algorithm that optimizes an information theory based fitness function to construct rules that explain global model behavior. We show how our approach outperforms existing approaches on a variety of datasets. Further, we introduce a parameter to evaluate the quality of interpretation under the scenario of distributional shift. This parameter evaluates how well the interpretation can predict model behavior for previously unseen data distributions. We show how existing approaches for interpreting models globally lack distributional robustness. Finally, we show how the quality of the interpretation can be improved under the scenario of distributional shift by adding out of distribution samples to the dataset used to learn the interpretation and thereby, increase robustness. All of the datasets used in our paper are open and publicly available. Our approach has been deployed in a leading digital marketing suite of products.
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE
Authors: Siva Shanmugam, Sheetal Kalyani
Date: 2021-05-14 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, Rolling Shutter Correction

Affine rank minimization problem is the generalized version of low rank matrix completion problem where linear combinations of the entries of a low rank matrix are observed and the matrix is estimated from these measurements. We propose a trainable deep neural network by unrolling a popular iterative algorithm called the singular value thresholding (SVT) algorithm to perform this generalized matrix completion which we call Learned SVT (LSVT). We show that our proposed LSVT with fixed layers (say T) reconstructs the matrix with lesser mean squared error (MSE) compared with that incurred by SVT with fixed (same T) number of iterations and our method is much more robust to the parameters which need to be carefully chosen in SVT algorithm.
Adapting deep generative approaches for getting synthetic data with realistic marginal distributions
Authors: Kiana Farhadyar, Federico Bonofiglio, Daniela Zoeller, Harald Binder
Date: 2021-05-14 00:00:00
Tasks: Synthetic Data Generation

Synthetic data generation is of great interest in diverse applications, such as for privacy protection. Deep generative models, such as variational autoencoders (VAEs), are a popular approach for creating such synthetic datasets from original data. Despite the success of VAEs, there are limitations when it comes to the bimodal and skewed marginal distributions. These deviate from the unimodal symmetric distributions that are encouraged by the normality assumption typically used for the latent representations in VAEs. While there are extensions that assume other distributions for the latent space, this does not generally increase flexibility for data with many different distributions. Therefore, we propose a novel method, pre-transformation variational autoencoders (PTVAEs), to specifically address bimodal and skewed data, by employing pre-transformations at the level of original variables. Two types of transformations are used to bring the data close to a normal distribution by a separate parameter optimization for each variable in a dataset. We compare the performance of our method with other state-of-the-art methods for synthetic data generation. In addition to the visual comparison, we use a utility measurement for a quantitative evaluation. The results show that the PTVAE approach can outperform others in both bimodal and skewed data generation. Furthermore, the simplicity of the approach makes it usable in combination with other extensions of VAE.
DoS and DDoS Mitigation Using Variational Autoencoders
Authors: Eirik Molde Bårli, Anis Yazidi, Enrique Herrera Viedma, Hårek Haugerud
Date: 2021-05-14 00:00:00
Tasks: Anomaly Detection

DoS and DDoS attacks have been growing in size and number over the last decade and existing solutions to mitigate these attacks are in general inefficient. Compared to other types of malicious cyber attacks, DoS and DDoS attacks are particularly more challenging to combat. With their ability to mask themselves as legitimate traffic, developing methods to detect these types of attacks on a packet or flow level, has proven to be a difficult task. In this paper, we explore the potential of Variational Autoencoders to serve as a component within an intelligent security solution that differentiates between normal and malicious traffic. Two methods based on the ability of Variational Autoencoders to learn latent representations from network traffic flows are proposed. The first method resorts to a classifier based on the latent encodings obtained from Variational Autoencoders learned from traffic traces. The second method is rather an anomaly detection method where the Variational Autoencoder is used to learn the abstract feature representations of exclusively legitimate traffic. Then anomalies are filtered out by relying on the reconstruction loss of the Variational Autoencoder. Both of the proposed methods have been thoroughly tested on two separate datasets with a similar feature space. The results show that both methods are promising, with a slight superiority of the classifier based method over the anomaly based one. %that the first method is able to successfully detect individual traffic flows with high precision on the training and validation data, slightly less successfully on the test data. For the second method, the Variational Autoencoder will require further adjustments to be able to sufficiently filter out anomalies from network traffic flows.
Privacy-preserving Logistic Regression with Secret Sharing
Authors: Ali Reza Ghavamipour, Fatih Turkmen, Xiaoqian Jian
Date: 2021-05-14 00:00:00
Tasks: Privacy Preserving, regression

Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Research that collects and combines datasets from various data custodians and jurisdictions can excessively benefit from the increased statistical power to support their analyzing goals. However, combining data from these various sources creates significant privacy concerns that need to be addressed. In this paper, we proposed secret sharing-based privacy-preserving logistic regression protocols using the Newton-Raphson method. Our proposed approaches are based on secure Multi-Party Computation (MPC) with different security settings to analyze data owned by several data holders. We conducted experiments on both synthetic data and real-world datasets and compared the efficiency and accuracy of them with those of an ordinary logistic regression model. Experimental results demonstrate that the proposed protocols are highly efficient and accurate. This study introduces iterative algorithms to simplify the federated training a logistic regression model in a privacy-preserving manner. Our implementation results show that our improved method can handle large datasets used in securely training a logistic regression from multiple sources.
Priors in Bayesian Deep Learning: A Review
Authors: Vincent Fortuin
Date: 2021-05-14 00:00:00
Tasks: Bayesian Inference, Deep Learning, Gaussian Processes

While the choice of prior is one of the most critical parts of the Bayesian inference workflow, recent Bayesian deep learning models have often fallen back on vague priors, such as standard Gaussians. In this review, we highlight the importance of prior choices for Bayesian deep learning and present an overview of different priors that have been proposed for (deep) Gaussian processes, variational autoencoders, and Bayesian neural networks. We also outline different methods of learning priors for these models from data. We hope to motivate practitioners in Bayesian deep learning to think more carefully about the prior specification for their models and to provide them with some inspiration in this regard.
Sparsity-Probe: Analysis tool for Deep Learning Models
Authors: Ido Ben-Shaul, Shai Dekel
Date: 2021-05-14 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

We propose a probe for the analysis of deep learning architectures that is based on machine learning and approximation theoretical principles. Given a deep learning architecture and a training set, during or after training, the Sparsity Probe allows to analyze the performance of intermediate layers by quantifying the geometrical features of representations of the training set. We show how the Sparsity Probe enables measuring the contribution of adding depth to a given architecture, to detect under-performing layers, etc., all this without any auxiliary test data set.
Predicting speech intelligibility from EEG in a non-linear classification paradigm
Authors: Bernd Accou, Mohammad Jalilpour Monesi, Hugo Van hamme, Tom Francart
Date: 2021-05-14 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Objective: Currently, only behavioral speech understanding tests are available, which require active participation of the person being tested. As this is infeasible for certain populations, an objective measure of speech intelligibility is required. Recently, brain imaging data has been used to establish a relationship between stimulus and brain response. Linear models have been successfully linked to speech intelligibility but require per-subject training. We present a deep-learning-based model incorporating dilated convolutions that operates in a match/mismatch paradigm. The accuracy of the model's match/mismatch predictions can be used as a proxy for speech intelligibility without subject-specific (re)training. Approach: We evaluated the performance of the model as a function of input segment length, EEG frequency band and receptive field size while comparing it to multiple baseline models. Next, we evaluated performance on held-out data and finetuning. Finally, we established a link between the accuracy of our model and the state-of-the-art behavioral MATRIX test. Main results: The dilated convolutional model significantly outperformed the baseline models for every input segment length, for all EEG frequency bands except the delta and theta band, and receptive field sizes between 250 and 500 ms. Additionally, finetuning significantly increased the accuracy on a held-out dataset. Finally, a significant correlation (r=0.59, p=0.0154) was found between the speech reception threshold estimated using the behavioral MATRIX test and our objective method. Significance: Our method is the first to predict the speech reception threshold from EEG for unseen subjects, contributing to objective measures of speech intelligibility.
Threshold Martingales and the Evolution of Forecasts
Authors: Dean P. Foster, Robert A. Stine
Date: 2021-05-14 00:00:00

This paper introduces a martingale that characterizes two properties of evolving forecast distributions. Ideal forecasts of a future event behave as martingales, sequen- tially updating the forecast to leverage the available information as the future event approaches. The threshold martingale introduced here measures the proportion of the forecast distribution lying below a threshold. In addition to being calibrated, a threshold martingale has quadratic variation that accumulates to a total determined by a quantile of the initial forecast distribution. Deviations from calibration or to- tal volatility signal problems in the underlying model. Calibration adjustments are well-known, and we augment these by introducing a martingale filter that improves volatility while guaranteeing smaller mean squared error. Thus, post-processing can rectify problems with calibration and volatility without revisiting the original forecast- ing model. We apply threshold martingales first to forecasts from simulated models and then to models that predict the winner in professional basketball games.
A cost-benefit analysis of cross-lingual transfer methods
Authors: Guilherme Moraes Rosa, Luiz Henrique Bonifacio, Leandro Rodrigues de Souza, Roberto Lotufo, Rodrigo Nogueira
Date: 2021-05-14 00:00:00
Tasks: Cross-Lingual Transfer, Translation

An effective method for cross-lingual transfer is to fine-tune a bilingual or multilingual model on a supervised dataset in one language and evaluating it on another language in a zero-shot manner. Translating examples at training time or inference time are also viable alternatives. However, there are costs associated with these methods that are rarely addressed in the literature. In this work, we analyze cross-lingual methods in terms of their effectiveness (e.g., accuracy), development and deployment costs, as well as their latencies at inference time. Our experiments on three tasks indicate that the best cross-lingual method is highly task-dependent. Finally, by combining zero-shot and translation methods, we achieve the state-of-the-art in two of the three datasets used in this work. Based on these results, we question the need for manually labeled training data in a target language. Code and translated datasets are available at https://github.com/unicamp-dl/cross-lingual-analysis
Quantified Sleep: Machine learning techniques for observational n-of-1 studies
Authors: Gianluca Truda
Date: 2021-05-14 00:00:00
Tasks: BIG-bench Machine Learning, Descriptive, Imputation, Missing Values, Sleep Quality, Time Series Analysis

This paper applies statistical learning techniques to an observational Quantified-Self (QS) study to build a descriptive model of sleep quality. A total of 472 days of my sleep data was collected with an Oura ring and combined with lifestyle, environmental, and psychological data. Such n-of-1 QS projects pose a number of challenges: heterogeneous data sources; missing values; high dimensionality; dynamic feedback loops; human biases. This paper directly addresses these challenges with an end-to-end QS pipeline that produces robust descriptive models. Sleep quality is one of the most difficult modelling targets in QS research, due to high noise and a large number of weakly-contributing factors. Sleep quality was selected so that approaches from this paper would generalise to most other n-of-1 QS projects. Techniques are presented for combining and engineering features for the different classes of data types, sample frequencies, and schema - including event logs, weather, and geo-spatial data. Statistical analyses for outliers, normality, (auto)correlation, stationarity, and missing data are detailed, along with a proposed method for hierarchical clustering to identify correlated groups of features. The missing data was overcome using a combination of knowledge-based and statistical techniques, including several multivariate imputation algorithms. "Markov unfolding" is presented for collapsing the time series into a collection of independent observations, whilst incorporating historical information. The final model was interpreted in two ways: by inspecting the internal $\beta$-parameters, and using the SHAP framework. These two interpretation techniques were combined to produce a list of the 16 most-predictive features, demonstrating that an observational study can greatly narrow down the number of features that need to be considered when designing interventional QS studies.
Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations
Authors: Matthew Watson, Bashar Awwad Shiekh Hasan, Noura Al Moubayed
Date: 2021-05-14 00:00:00

Deep Learning of neural networks has progressively become more prominent in healthcare with models reaching, or even surpassing, expert accuracy levels. However, these success stories are tainted by concerning reports on the lack of model transparency and bias against some medical conditions or patients' sub-groups. Explainable methods are considered the gateway to alleviate many of these concerns. In this study we demonstrate that the generated explanations are volatile to changes in model training that are perpendicular to the classification task and model structure. This raises further questions about trust in deep learning models for healthcare. Mainly, whether the models capture underlying causal links in the data or just rely on spurious correlations that are made visible via explanation methods. We demonstrate that the output of explainability methods on deep neural networks can vary significantly by changes of hyper-parameters, such as the random seed or how the training set is shuffled. We introduce a measure of explanation consistency which we use to highlight the identified problems on the MIMIC-CXR dataset. We find explanations of identical models but with different training setups have a low consistency: $\approx$ 33% on average. On the contrary, kernel methods are robust against any orthogonal changes, with explanation consistency at 94%. We conclude that current trends in model explanation are not sufficient to mitigate the risks of deploying models in real life healthcare applications.