pwc-archive/papers-with-abstracts huggingface dataset:
Activation function impact on Sparse Neural Networks
Authors: Adam Dubowski
Date: 2020-10-12 00:00:00

While the concept of a Sparse Neural Network has been researched for some time, researchers have only recently made notable progress in the matter. Techniques like Sparse Evolutionary Training allow for significantly lower computational complexity when compared to fully connected models by reducing redundant connections. That typically takes place in an iterative process of weight creation and removal during network training. Although there have been numerous approaches to optimize the redistribution of the removed weights, there seems to be little or no study on the effect of activation functions on the performance of the Sparse Networks. This research provides insights into the relationship between the activation function used and the network performance at various sparsity levels.
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients
Authors: Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan, Rafael S. de Souza, Alexander Ihler, Kara Ponder, Ricardo Vilalta, Anais Moller, David O. Jones, Mi Dai, Alberto Krone-Martins, Bruno Quint, Sreevarsha Sreejith, Alex I. Malz, Lluis Galbany
Date: 2020-10-12 00:00:00
Tasks: Active Learning, Astronomy, BIG-bench Machine Learning

The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions, such as sample representativeness and labeling cost stability cannot be fulfilled. The Recommendation System for Spectroscopic follow-up (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment. In this work, we test the robustness of active learning techniques in a realistic simulated astronomical data scenario. Our experiment takes into account the evolution of training and pool samples, different costs per object, and two different sources of budget. Results show that traditional active learning strategies significantly outperform random sampling. Nevertheless, more complex batch strategies are not able to significantly overcome simple uncertainty sampling techniques. Our findings illustrate three important points: 1) active learning strategies are a powerful tool to optimize the label-acquisition task in astronomy, 2) for upcoming large surveys like LSST, such techniques allow us to tailor the construction of the training sample for the first day of the survey, and 3) the peculiar data environment related to the detection of astronomical transients is a fertile ground that calls for the development of tailored machine learning algorithms.
Inverse Multiobjective Optimization Through Online Learning
Authors: Chaosheng Dong, Yijia Wang, Bo Zeng
Date: 2020-10-12 00:00:00
Tasks: Decision Making, Multiobjective Optimization

We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions. In particular, these decisions might not be exact and possibly carry measurement noise or are generated with the bounded rationality of decision makers. In this paper, we propose a general online learning framework to deal with this learning problem using inverse multiobjective optimization. More precisely, we develop two online learning algorithms with implicit update rules which can handle noisy data. Numerical results show that both algorithms can learn the parameters with great accuracy and are robust to noise.
Escalation Prediction using Feature Engineering: Addressing Support Ticket Escalations within IBM's Ecosystem
Authors: Lloyd Montgomery
Date: 2020-10-12 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, Management

Large software organizations handle many customer support issues every day in the form of bug reports, feature requests, and general misunderstandings as submitted by customers. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. Whenever insufficient attention is given to support issues, there is a chance customers will escalate their issues, and escalation to management is time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. This thesis provides a step towards simplifying the job for support analysts and managers, particularly in predicting the risk of escalating support tickets. In a field study at our large industrial partner, IBM, a design science methodology was employed to characterize the support process and data available to IBM analysts in managing escalations. Through iterative cycles of design and evaluation, support analysts' expert knowledge about their customers was translated into features of a support ticket model to be implemented into a Machine Learning model to predict support ticket escalations. The Machine Learning model was trained and evaluated on over 2.5 million support tickets and 10,000 escalations, obtaining a recall of 79.9% and an 80.8% reduction in the workload for support analysts looking to identify support tickets at risk of escalation. The features developed in the Support Ticket Model are designed to serve as a starting place for organizations interested in implementing the model to predict support ticket escalations, and for future researchers to build on to advance research in Escalation Prediction.
An Empirical Study on User Reviews Targeting Mobile Apps' Security & Privacy
Authors: Debjyoti Mukherjee, Alireza Ahmadi, Maryam Vahdat Pour, Joel Reardon
Date: 2020-10-11 00:00:00

Application markets provide a communication channel between app developers and their end-users in form of app reviews, which allow users to provide feedback about the apps. Although security and privacy in mobile apps are one of the biggest issues, it is unclear how much people are aware of these or discuss them in reviews. In this study, we explore the privacy and security concerns of users using reviews in the Google Play Store. For this, we conducted a study by analyzing around 2.2M reviews from the top 539 apps of this Android market. We found that 0.5\% of these reviews are related to the security and privacy concerns of the users. We further investigated these apps by performing dynamic analysis which provided us valuable insights into their actual behaviors. Based on the different perspectives, we categorized the apps and evaluated how the different factors influence the users' perception of the apps. It was evident from the results that the number of permissions that the apps request plays a dominant role in this matter. We also found that sending out the location can affect the users' thoughts about the app. The other factors do not directly affect the privacy and security concerns for the users.
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification
Authors: Pedro R. A. S. Bassi, Willian Rampazzo, Romis Attux
Date: 2020-10-08 00:00:00
Tasks: Brain Computer Interface, Classification, Data Augmentation, EEG, Electroencephalogram (EEG), General Classification, speech-recognition, Speech Recognition, SSVEP, Transfer Learning

Objective: We used deep convolutional neural networks (DCNNs) to classify electroencephalography (EEG) signals in a steady-state visually evoked potentials (SSVEP) based single-channel brain-computer interface (BCI), which does not require calibration on the user. Methods: EEG signals were converted to spectrograms and served as input to train DCNNs using the transfer learning technique. We also modified and applied a data augmentation method, SpecAugment, generally employed for speech recognition. Furthermore, for comparison purposes, we classified the SSVEP dataset using Support-vector machines (SVMs) and Filter Bank canonical correlation analysis (FBCCA). Results: Excluding the evaluated user's data from the fine-tuning process, we reached 82.2% mean test accuracy and 0.825 mean F1-Score on 35 subjects from an open dataset, using a small data length (0.5 s), only one electrode (Oz) and the DCNN with transfer learning, window slicing (WS) and SpecAugment's time masks. Conclusion: The DCNN results surpassed SVM and FBCCA performances, using a single electrode and a small data length. Transfer learning provided minimal accuracy change, but made training faster. SpecAugment created a small performance improvement and was successfully combined with WS, yielding higher accuracies. Significance: We present a new methodology to solve the problem of SSVEP classification using DCNNs. We also modified a speech recognition data augmentation technique and applied it to the context of BCIs. The presented methodology surpassed performances obtained with FBCCA and SVMs (more traditional SSVEP classification methods) in BCIs with small data lengths and one electrode. This type of BCI can be used to develop small and fast systems.
Kartta Labs: Collaborative Time Travel
Authors: Sasan Tavakkol, Feng Han, Brandon Mayer, Mark Phillips, Cyrus Shahabi, Yao-Yi Chiang, Raimondas Kiveris
Date: 2020-10-07 00:00:00

We introduce the modular and scalable design of Kartta Labs, an open source, open data, and scalable system for virtually reconstructing cities from historical maps and photos. Kartta Labs relies on crowdsourcing and artificial intelligence consisting of two major modules: Maps and 3D models. Each module, in turn, consists of sub-modules that enable the system to reconstruct a city from historical maps and photos. The result is a spatiotemporal reference that can be used to integrate various collected data (curated, sensed, or crowdsourced) for research, education, and entertainment purposes. The system empowers the users to experience collaborative time travel such that they work together to reconstruct the past and experience it on an open source and open data platform.
Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone
Authors: Yuzhen Chen, Menghan Hu, Chunjun Hua, Guangtao Zhai, Jian Zhang, Qingli Li, Simon X. Yang
Date: 2020-10-09 00:00:00

Coronavirus Disease 2019 (COVID-19) has spread all over the world since it broke out massively in December 2019, which has caused a large loss to the whole world. Both the confirmed cases and death cases have reached a relatively frightening number. Syndrome coronaviruses 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. In most cases, people use face masks in a high-frequent but short-time way. Aimed at solving the problem that we don't know which service stage of the mask belongs to, we propose a detection system based on the mobile phone. We first extract four features from the GLCMs of the face mask's micro-photos. Next, a three-result detection system is accomplished by using KNN algorithm. The results of validation experiments show that our system can reach a precision of 82.87% (standard deviation=8.5%) on the testing dataset. In future work, we plan to expand the detection objects to more mask types. This work demonstrates that the proposed mobile microscope system can be used as an assistant for face mask being used, which may play a positive role in fighting against COVID-19.
The DongNiao International Birds 10000 Dataset
Authors: Jian Mei, Hao Dong
Date: 2020-09-21 00:00:00
Tasks: BIG-bench Machine Learning, Image Classification

DongNiao International Birds 10000 (DIB-10K) is a challenging image dataset which has more than 10 thousand different types of birds. It was created to enable the study of machine learning and also ornithology research. DIB-10K does not own the copyright of these images. It only provides thumbnails of images, in a way similar to ImageNet.
LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis
Authors: Quan Huu Cap, Hiroki Tani, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi Iyatomi
Date: 2020-10-12 00:00:00
Tasks: Diagnostic, Super-Resolution

The collection of high-resolution training data is crucial in building robust plant disease diagnosis systems, since such data have a significant impact on diagnostic performance. However, they are very difficult to obtain and are not always available in practice. Deep learning-based techniques, and particularly generative adversarial networks (GANs), can be applied to generate high-quality super-resolution images, but these methods often produce unexpected artifacts that can lower the diagnostic performance. In this paper, we propose a novel artifact-suppression super-resolution method that is specifically designed for diagnosing leaf disease, called Leaf Artifact-Suppression Super Resolution (LASSR). Thanks to its own artifact removal module that detects and suppresses artifacts to a considerable extent, LASSR can generate much more pleasing, high-quality images compared to the state-of-the-art ESRGAN model. Experiments based on a five-class cucumber disease (including healthy) discrimination model show that training with data generated by LASSR significantly boosts the performance on an unseen test dataset by nearly 22% compared with the baseline, and that our approach is more than 2% better than a model trained with images generated by ESRGAN.
A Possible Method of Carbon Deposit Mapping on Plasma Facing Components Using Infrared Thermography
Authors: R Mitteau, J Spruytte, S Vallet, J Travère, D Guilhem, C Brosset
Date: 2020-09-25 00:00:00

The material eroded from the surface of plasma facing components is redeposited partly close to high heat flux areas. At these locations, the deposit is heated by the plasma and the deposition pattern evolves depending on the operation parameters. The mapping of the deposit is still a matter of intense scientific activity, especially during the course of experimental campaigns. A method based on the comparison of surface temperature maps, obtained in situ by infrared cameras and by theoretical modelling is proposed. The difference between the two is attributed to the thermal resistance added by deposited material, and expressed as a deposit thickness. The method benefits of elaborated imaging techniques such as possibility theory and fuzzy logics. The results are consistent with deposit maps obtained by visual inspection during shutdowns.
Piece-wise Matching Layer in Representation Learning for ECG Classification
Authors: Behzad Ghazanfari, Fatemeh Afghah, Sixian Zhang
Date: 2020-09-26 00:00:00
Tasks: Classification, ECG Classification, General Classification, Representation Learning, Time Series, Time Series Analysis

This paper proposes piece-wise matching layer as a novel layer in representation learning methods for electrocardiogram (ECG) classification. Despite the remarkable performance of representation learning methods in the analysis of time series, there are still several challenges associated with these methods ranging from the complex structures of methods, the lack of generality of solutions, the need for expert knowledge, and large-scale training datasets. We introduce the piece-wise matching layer that works based on two levels to address some of the aforementioned challenges. At the first level, a set of morphological, statistical, and frequency features and comparative forms of them are computed based on each periodic part and its neighbors. At the second level, these features are modified by predefined transformation functions based on a receptive field scenario. Several scenarios of offline processing, incremental processing, fixed sliding receptive field, and event-based triggering receptive field can be implemented based on the choice of length and mechanism of indicating the receptive field. We propose dynamic time wrapping as a mechanism that indicates a receptive field based on event triggering tactics. To evaluate the performance of this method in time series analysis, we applied the proposed layer in two publicly available datasets of PhysioNet competitions in 2015 and 2017 where the input data is ECG signal. We compared the performance of our method against a variety of known tuned methods from expert knowledge, machine learning, deep learning methods, and the combination of them. The proposed approach improves the state of the art in two known completions 2015 and 2017 around 4% and 7% correspondingly while it does not rely on in advance knowledge of the classes or the possible places of arrhythmia.
On the cost of Bayesian posterior mean strategy for log-concave models
Authors: Sébastien Gadat, Fabien Panloup, Clément Pellegrini
Date: 2020-10-08 00:00:00

In this paper, we investigate the problem of computing Bayesian estimators using Langevin Monte-Carlo type approximation. The novelty of this paper is to consider together the statistical and numerical counterparts (in a general log-concave setting). More precisely, we address the following question: given $n$ observations in $\mathbb{R}^q$ distributed under an unknown probability $\mathbb{P}_{\theta^\star}$ with $\theta^\star \in \mathbb{R}^d$ , what is the optimal numerical strategy and its cost for the approximation of $\theta^\star$ with the Bayesian posterior mean? To answer this question, we establish some quantitative statistical bounds related to the underlying Poincar\'e constant of the model and establish new results about the numerical approximation of Gibbs measures by Cesaro averages of Euler schemes of (over-damped) Langevin diffusions. These last results include in particular some quantitative controls in the weakly convex case based on new bounds on the solution of the related Poisson equation of the diffusion.
Estimating COVID-19 cases and outbreaks on-stream through phone-calls
Authors: Ezequiel Alvarez, Daniela Obando, Sebastian Crespo, Enio Garcia, Nicolas Kreplak, Franco Marsico
Date: 2020-10-10 00:00:00
Tasks: Decision Making

One of the main problems in controlling COVID-19 epidemic spread is the delay in confirming cases. Having information on changes in the epidemic evolution or outbreaks rise before lab-confirmation is crucial in decision making for Public Health policies. We present an algorithm to estimate on-stream the number of COVID-19 cases using the data from telephone calls to a COVID-line. By modeling the calls as background (proportional to population) plus signal (proportional to infected), we fit the calls in Province of Buenos Aires (Argentina) with coefficient of determination $R^2 > 0.85$. This result allows us to estimate the number of cases given the number of calls from a specific district, days before the lab results are available. We validate the algorithm with real data. We show how to use the algorithm to track on-stream the epidemic, and present the Early Outbreak Alarm to detect outbreaks in advance to lab results. One key point in the developed algorithm is a detailed track of the uncertainties in the estimations, since the alarm uses the significance of the observables as a main indicator to detect an anomaly. We present the details of the explicit example in Villa Azul (Quilmes) where this tool resulted crucial to control an outbreak on time. The presented tools have been designed in urgency with the available data at the time of the development, and therefore have their limitations which we describe and discuss. We consider possible improvements on the tools, many of which are currently under development.
Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae
Authors: Ewelina Weglarz-Tomczak, Jakub M. Tomczak, Agoston E. Eiben, Stanley Brul
Date: 2020-09-19 00:00:00

Models in systems biology are mathematical descriptions of biological processes that are used to answer questions and gain a better understanding of biological phenomena. Dynamic models represent the network through rates of the production and consumption for the individual species. The ordinary differential equations that describe rates of the reactions in the model include a set of parameters. The parameters are important quantities to understand and analyze biological systems. Moreover, the perturbation of the kinetic parameters are correlated with upregulation of the system by cell-intrinsic and cell-extrinsic factors, including mutations and the environment changes. Here, we aim at using well-established models of biological pathways to identify parameter values and point their potential perturbation/deviation. We present our population-based optimization framework that is able to identify kinetic parameters in the dynamic model based on only input and output data (i.e., timecourses of selected metabolites). Our approach can deal with the identification of the non-measurable parameters as well as with discovering deviation of the parameters. We present our proposed optimization framework on the example of the well-studied glycolytic pathway in Saccharomyces cerevisiae.
Hindsight Experience Replay with Kronecker Product Approximate Curvature
Authors: Dhuruva Priyan G M, Abhik Singla, Shalabh Bhatnagar
Date: 2020-10-09 00:00:00
Tasks: MuJoCo

Hindsight Experience Replay (HER) is one of the efficient algorithm to solve Reinforcement Learning tasks related to sparse rewarded environments.But due to its reduced sample efficiency and slower convergence HER fails to perform effectively. Natural gradients solves these challenges by converging the model parameters better. It avoids taking bad actions that collapse the training performance. However updating parameters in neural networks requires expensive computation and thus increase in training time. Our proposed method solves the above mentioned challenges with better sample efficiency and faster convergence with increased success rate. A common failure mode for DDPG is that the learned Q-function begins to dramatically overestimate Q-values, which then leads to the policy breaking, because it exploits the errors in the Q-function. We solve this issue by including Twin Delayed Deep Deterministic Policy Gradients(TD3) in HER. TD3 learns two Q-functions instead of one and it adds noise tothe target action, to make it harder for the policy to exploit Q-function errors. The experiments are done with the help of OpenAis Mujoco environments. Results on these environments show that our algorithm (TDHER+KFAC) performs better inmost of the scenarios