pwc-archive/papers-with-abstracts huggingface dataset:
Using Machine Learning to Calibrate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System
Authors: Montgomery Flora, Corey K. Potvin, Patrick S. Skinner, Shawn Handler, Amy McGovern
Date: 2020-11-12 00:00:00

A primary goal of the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0-3 h) severe weather forecasts. Maximizing the usefulness of probabilistic severe weather guidance from an ensemble of convection-allowing model forecasts requires calibration. In this study, we compare the skill of a simple method using updraft helicity against a series of machine learning (ML) algorithms for calibrating WoFS severe weather guidance. ML models are often used to calibrate severe weather guidance since they leverage multiple variables and discover useful patterns in complex datasets. \indent Our dataset includes WoF System (WoFS) ensemble forecasts available every 5 minutes out to 150 min of lead time from the 2017-2019 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (81 dates). Using a novel ensemble storm track identification method, we extracted three sets of predictors from the WoFS forecasts: intra-storm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. We then trained random forests, gradient-boosted trees, and logistic regression algorithms to predict which WoFS 30-min ensemble storm tracks will correspond to a tornado, severe hail, and/or severe wind report. For the simple method, we extracted the ensemble probability of 2-5 km updraft helicity (UH) exceeding a threshold (tuned per severe weather hazard) from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced more reliable probabilities than the UH-based predictions. Overall, the results suggest that ML-based calibrations of dynamical ensemble output can improve short term, storm-scale severe weather probabilistic guidance
A data-driven approach to the forecasting of ground-level ozone concentration
Authors: Dario Marvin, Lorenzo Nespoli, Davide Strepparava, Vasco Medici
Date: 2020-10-14 00:00:00
Tasks: Feature Importance, feature selection, Spatio-Temporal Forecasting

The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to the forecast of the day-ahead maximum value of the ozone concentration for several geographical locations in southern Switzerland. Due to the low density of measurement stations and to the complex orography of the use case terrain, we adopted feature selection methods instead of explicitly restricting relevant features to a neighbourhood of the prediction sites, as common in spatio-temporal forecasting methods. We then used Shapley values to assess the explainability of the learned models in terms of feature importance and feature interactions in relation to ozone predictions; our analysis suggests that the trained models effectively learned explanatory cross-dependencies among atmospheric variables. Finally, we show how weighting observations helps in increasing the accuracy of the forecasts for specific ranges of ozone's daily peak values.
Short term solar energy prediction by machine learning algorithms
Authors: Farah Shahid, Aneela Zameer, Mudasser Afzal, Muhammad Hassan
Date: 2020-10-25 00:00:00
Tasks: BIG-bench Machine Learning

Smooth power generation from solar stations demand accurate, reliable and efficient forecast of solar energy for optimal integration to cater market demand; however, the implicit instability of solar energy production may cause serious problems for the smooth power generation. We report daily prediction of solar energy by exploiting the strength of machine learning techniques to capture and analyze complicated behavior of enormous features effectively. For this purpose, dataset comprising of 98 solar stations has been taken from energy competition of American Meteorological Society (AMS) for predicting daily solar energy. Forecast models of base line regressors including linear, ridge, lasso, decision tree, random forest and artificial neural networks have been implemented on the AMS solar dataset. Grid size is converted into two sections: 16x9 and 10x4 to ascertain attributes contributing more towards the generated power from densely located stations on global ensemble forecast system (GEFS). To evaluate the models, statistical measures of prediction error in terms of RMSE, MAE and R2_score have been analyzed and compared with the existing techniques. It has been observed that improved accuracy is achieved through random forest and ridge regressor for both grid sizes in contrast to all other proposed methods. Stability and reliability of the proposed schemes are evaluated on a single solar station as well as on multiple independent runs.
Multi-Modal Hybrid Architecture for Pedestrian Action Prediction
Authors: Amir Rasouli, Tiffany Yau, Mohsen Rohani, Jun Luo
Date: 2020-11-16 00:00:00
Tasks: Prediction

Pedestrian behavior prediction is one of the major challenges for intelligent driving systems in urban environments. Pedestrians often exhibit a wide range of behaviors and adequate interpretations of those depend on various sources of information such as pedestrian appearance, states of other road users, the environment layout, etc. To address this problem, we propose a novel multi-modal prediction algorithm that incorporates different sources of information captured from the environment to predict future crossing actions of pedestrians. The proposed model benefits from a hybrid learning architecture consisting of feedforward and recurrent networks for analyzing visual features of the environment and dynamics of the scene. Using the existing 2D pedestrian behavior benchmarks and a newly annotated 3D driving dataset, we show that our proposed model achieves state-of-the-art performance in pedestrian crossing prediction.
Federated AI for building AI Solutions across Multiple Agencies
Authors: Dinesh Verma, Simon Julier, Greg Cirincione
Date: 2018-09-20 00:00:00

The different sets of regulations existing for differ-ent agencies within the government make the task of creating AI enabled solutions in government dif-ficult. Regulatory restrictions inhibit sharing of da-ta across different agencies, which could be a significant impediment to training AI models. We discuss the challenges that exist in environments where data cannot be freely shared and assess tech-nologies which can be used to work around these challenges. We present results on building AI models using the concept of federated AI, which al-lows creation of models without moving the training data around.
C-DLSI: An Extended LSI Tailored for Federated Text Retrieval
Authors: Qijun Zhu, Dandan Li, Dik Lun Lee
Date: 2018-10-05 00:00:00
Tasks: Clustering, Information Retrieval, Retrieval, Text Retrieval

As the web expands in data volume and in geographical distribution, centralized search methods become inefficient, leading to increasing interest in cooperative information retrieval, e.g., federated text retrieval (FTR). Different from existing centralized information retrieval (IR) methods, in which search is done on a logically centralized document collection, FTR is composed of a number of peers, each of which is a complete search engine by itself. To process a query, FTR requires firstly the identification of promising peers that host the relevant documents and secondly the retrieval of the most relevant documents from the selected peers. Most of the existing methods only apply traditional IR techniques that treat each text collection as a single large document and utilize term matching to rank the collections. In this paper, we formalize the problem and identify the properties of FTR, and analyze the feasibility of extending LSI with clustering to adapt to FTR, based on which a novel approach called Cluster-based Distributed Latent Semantic Indexing (C-DLSI) is proposed. C-DLSI distinguishes the topics of a peer with clustering, captures the local LSI spaces within the clusters, and consider the relations among these LSI spaces, thus providing more precise characterization of the peer. Accordingly, novel descriptors of the peers and a compatible local text retrieval are proposed. The experimental results show that C-DLSI outperforms existing methods.
Dataset for eye-tracking tasks
Authors: Ildar Rakhmatulin
Date: 2020-12-02 00:00:00

In recent years many different deep neural networks were developed, but due to a large number of layers
in deep networks, their training requires a long time and a large number of datasets. Today is popular to
use trained deep neural networks for various tasks, even for simple ones in which such deep networks are
not required. The well-known deep networks such as YoloV3, SSD, etc. are intended for tracking and
monitoring various objects, therefore their weights are heavy and the overall accuracy for a specific task
is low. Eye-tracking tasks need to detect only one object - an iris in a given area. Therefore, it is logical to
use a neural network only for this task. But the problem is the lack of suitable datasets for training the
model. In the manuscript, we presented a dataset that is suitable for training custom models of
convolutional neural networks for eye-tracking tasks. Using data set data, each user can independently
pre-train the convolutional neural network models for eye-tracking tasks. This dataset contains annotated
10,000 eye images in an extension of 416 by 416 pixels. The table with annotation information shows the
coordinates and radius of the eye for each image. This manuscript can be considered as a guide for the
preparation of datasets for eye-tracking devices.
An Unsupervised Framework for Anomaly Detection in a Water Treatment System.
Authors: Mayra Macas, Chunming Wu
Date: 2019-12-16 00:00:00
Tasks: Anomaly Detection, Decoder, Management

Current Cyber-Physical Systems (CPSs) are sophisticated, complex, and equipped with networked sensors and actuators. As such, they have become further exposed to cyber-attacks. Recent catastrophic events have demonstrated that standard, human-based management of anomaly detection in complex systems is not efficient enough and have underlined the significance of automated detection, intelligent and rapid response. Nevertheless, existing anomaly detection frameworks usually are not capable of dealing with the dynamic and complicated nature of the CPSs. In this study, we introduce an unsupervised framework for anomaly detection based on an Attention-based Spatio-Temporal Autoencoder. In particular, we first construct statistical correlation matrices to characterize the system status across different time steps. Next, a 2D convolutional encoder is employed to encode the patterns of the correlation matrices, whereas an Attention-based Convolutional LSTM Encoder-Decoder (ConvLSTM-ED) is used to capture the temporal dependencies. More precisely, we introduce an input attention mechanism to adaptively select the most significant input features at each time step. Finally, the 2D convolutional decoder reconstructs the correlation matrices. The differences between the reconstructed correlation matrices and the original ones are used as indicators of anomalies. Extensive experimental analysis on data collected from all six stages of Secure Water Treatment (SWaT) testbed, a scaled-down version of a real-world industrial water treatment plant, demonstrates that the proposed model outperforms the state-of-the-art baseline techniques.
Data Mining model in the discovery of trends and patterns of intruder attacks on the data network as a public-sector innovation
Authors: Mayra Macas, Lidia Lagla; Walter Fuertes, Graciela Guerrero, Theofilos Toulkeridis
Date: 2017-04-19 00:00:00
Tasks: Intrusion Detection, Network Intrusion Detection

Innovation in the public-sector refers to the development of important improvements in the public administration and their corresponding services. One of such public services is the social security, of which central process has been the information security of their offered services. The aim of the present study has been the analysis of the trends and the discovery of behavioural patterns in the attacks to the data network of an institution of the public-sector. To fulfil such objective, a model has been implemented on algorithms and data mining techniques, based on the Cross Industry Standard Process for Data Mining methodology. The model uses a free and open source network Intrusion Detection and Prevention System (IDS/IPS) for the capture of the logs of the attacks to the data network of the organization. This has been followed by a quantitative assessment of various algorithms of intrusion detection leading to the selection of J48 and REPTree as Data Mining algorithms with a level of insolence in instances properly classified by the lowest absolute error. The data were processed and served as input for the construction of rules. The resulting rules of the decision tree have been based on the principle of calculating the information gain via entropy and minimizing the error that arises from the variance. These rules were the product of applying machine learning on the logs analysed and they were subsequently translated and reprogrammed to the IDS/IPS in order to assess the efficiency of the model. The results demonstrate a significant improvement of some 67% in detection of attacks in relation to the traditional IDS. Consequently, we extrapolated a wide difference in behaviour and trends with the use of a traditional system compared to that generated by Data Mining.
Enhanced Cyber-Physical Security through Deep Learning Techniques
Authors: Mayra Macas, Chunming Wu
Date: 2019-08-23 00:00:00
Tasks: Anomaly Detection, Deep Learning, Management, Time Series, Time Series Analysis

Nowadays that various aspects of our lives depend on complex cyber-physical systems,  automated anomaly detection,  as well as attack prevention and reaction have become of paramount importance and directly affect our security and ultimately our quality of life.  Recent catastrophic events have demonstrated that manual, human-based management of anomalies in complex systems is not efficient enough, underlying the importance of automatic detection and intelligent response as the recommended approach of defence. We proposed an anomaly detection framework for complex systems based on monitored data storage and Statistical Correlation Analysis for different pairs of constituent time-series of a multivariate time series segment, and unsupervised deep learn-ing to intelligently distinguish between normal and abnormal behavior of the system. Experimental results demonstrate that the proposed model is much better than baseline methods, and it can model (inter)correlation and temporal patterns of multivariate time series effectively.
Grabber: A tool to improve convergence in interactive image segmentation
Authors: Jordão Bragantini, Bruno Moura, Alexandre Xavier Falcão, Fábio A. M. Cappabianco
Date: 2020-12-01 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Interactive image segmentation has considerably evolved from techniques that do not learn the parameters of the model to methods that pre-train a model and adapt it from user inputs during the process. However, user control over segmentation still requires significant improvements to avoid that corrections in one part of the object cause errors in other parts. We address this problem by presenting Grabber — a tool to improve convergence (user control) in interactive image segmentation. Grabber is thought to complete segmentation of some other initial method. From a given segmentation mask, Grabber quickly estimates anchor points in one orientation along the boundary of the mask and delineates an optimum contour constrained to pass through those points. The user can control the process by adding, removing, and moving anchor points. Grabber can also explore object properties from the initial coarse segmentation to improve boundary delineation. We integrate Grabber with two recent methods, a region-based approach and a pixel classification method based on deep neural networks. Extensive experiments with robot users on two datasets show in both cases that Grabber can significantly improve convergence, with faster delineation, higher effectiveness, and less user effort. The code of Grabber is available at https://github.com/LIDS-UNICAMP/grabber .
Theory of adaptive SVD regularization for deep neural networks
Authors: Mohammad Mahdi Bejani, MehdiGhatee
Date: 2020-08-01 00:00:00

Deep networks can learn complex problems, however, they suffer from overfitting. To solve this problem, regularization methods have been proposed that are not adaptable to the dynamic changes in the training process. With a different approach, this paper presents a regularization method based on the Singular Value Decomposition (SVD) that adjusts the learning model adaptively. To this end, the overfitting can be evaluated by condition numbers of the synaptic matrices. When the overfitting is high, the matrices are substituted with their SVD approximations. Some theoretical results are derived to show the performance of this regularization method. It is proved that SVD approximation cannot solve overfitting after several iterations. Thus, a new Tikhonov term is added to the loss function to converge the synaptic weights to the SVD approximation of the best-found results. Following this approach, an Adaptive SVD Regularization (ASR) is proposed to adjust the learning model with respect to the dynamic training characteristics. ASR results are visualized to show how ASR overcomes overfitting. The different configurations of Convolutional Neural Networks (CNN) are implemented with different augmentation schemes to compare ASR with state-of-the-art regularization methods. The results show that on MNIST, F-MNIST, SVHN, CIFAR-10 and CIFAR-100, the accuracies of ASR are 99.4%, 95.7%, 97.1%, 93.2% and 55.6%, respectively. Although ASR improves the overfitting and validation loss, its elapsed time is not significantly greater than the learning without regularization.
Improving contact prediction along three dimensions
Authors: Christoph Feinauer, Marcin J. Skwark, Andrea Pagnani, Erik Aurell
Date: 2014-03-03 00:00:00
Tasks: Prediction

Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to: (i) filter and align the raw sequence data representing the evolutionarily related proteins; (ii) choose a predictive model to describe a sequence alignment; (iii) infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple) extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date.
Single-Shot Lightweight Model For The Detection of Lesions And The Prediction of COVID-19 From Chest CT Scans
Authors: Aram Ter-Sarkisov
Date: 2020-12-02 00:00:00
Tasks: Classification, COVID-19 Diagnosis, General Classification, Instance Segmentation, Lesion Segmentation, Segmentation, Sensitivity

We introduce a lightweight model based on Mask R-CNN with ResNet18 and ResNet34 backbone models that
segments lesions and predicts COVID-19 from chest CT scans in a single shot. The model requires a small dataset to
train: 650 images for the segmentation branch and 3000 for the classification branch, and it is evaluated on 21292 images
to achieve a 42.45% average precision (main MS COCO criterion) on the segmentation test split (100 images), 93.00%
COVID-19 sensitivity and F1-score of 96.76% on the classification test split (21192 images) across 3 classes: COVID-19,
Common Pneumonia and Control/Negative. The full source code, models and pretrained weights are available on
https://github.com/AlexTS1980/COVID-Single-Shot-Model.
Ubicoustics: Plug-and-Play Acoustic Activity Recognition
Authors: Gierad Laput, Karan Ahuja, Mayank Goel, Chris Harrison
Date: 2018-10-14 00:00:00
Tasks: Activity Recognition, Audio Classification, Data Augmentation, Environmental Sound Classification, Sound Event Detection

Despite sound being a rich source of information, computing devices with microphones do not leverage audio to glean useful insights about their physical and social context. For example, a smart speaker sitting on a kitchen countertop cannot figure out if it is in a kitchen, let alone know what a user is doing in a kitchen—a missed opportunity. In this work, we describe a novel, real-time, sound-based activity recognition system. We start by taking an existing, state-of-the-art sound labeling model, which we then tune to classes of interest by drawing data from professional sound effect libraries traditionally used in the entertainment industry. These well-labeled and high-quality sounds are the perfect atomic unit for data augmentation, including amplitude, reverb, and mixing, allowing us to exponentially grow our tuning data in realistic ways. We quantify the performance of our approach across a range of environments and device categories and show that microphone-equipped computing devices already have the requisite capability to unlock real-time activity recognition comparable to human accuracy.
Direction-of-Voice (DoV) Estimation for Intuitive Speech Interaction with Smart Devices Ecosystems
Authors: Karan Ahuja, Andy Kong, Mayank Goel, and Chris Harrison.
Date: 2020-11-15 00:00:00
Tasks: Speaker Orientation

Future homes and offices will feature increasingly dense ecosystems of IoT devices, such as smart lighting, speakers, and domestic appliances. Voice input is a natural candidate for interacting with out-of-reach and often small devices that lack full-sized physical interfaces. However, at present, voice agents generally require wake-words and device names in order to specify the target of a spoken command (e.g., “Hey Alexa, kitchen lights to full brightness”). In this research, we explore whether speech alone can be used as a directional communication channel, in much the same way visual gaze specifies a focus. Instead of a device’s microphones simply receiving and processing spoken commands, we suggest they also infer the Direction of Voice (DoV). Our approach innately enables voice commands with addressability (i.e., devices know if a command was directed at them) in a natural and rapid manner. We quantify the accuracy of our implementation across users, rooms, spoken phrases, and other key factors that affect performance and usability. Taken together, we believe our DoV approach demonstrates feasibility and the promise of making distributed voice interactions much more intuitive and fluid.